diff --git a/.gitignore b/.gitignore index 285da1e..7ca2f7d 100644 --- a/.gitignore +++ b/.gitignore @@ -22,7 +22,8 @@ docs/site/ # committed for packages, but should be committed for applications that require a static # environment. Manifest*.toml - +# macOS +.DS_Store # File generated by the Preferences package to store local preferences LocalPreferences.toml JuliaLocalPreferences.toml diff --git a/Project.toml b/Project.toml index cfc7a64..2e8b795 100644 --- a/Project.toml +++ b/Project.toml @@ -1,11 +1,11 @@ name = "BatchPDLP" uuid = "529b9e8a-d2db-4a93-8026-567587707ce3" -authors = ["Robert X. Gottlieb ", "Dimitri Alston ", "Matthew D. Stuber "] -version = "0.1.0" +authors = ["Robert X. Gottlieb ", "Dimitri Alston ", "Alireza Miraliakbar ", "Matthew D. Stuber "] +version = "0.2.0" [deps] CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" [compat] CUDA = "5 - 6" -julia = "1.10" \ No newline at end of file +julia = "1.10" diff --git a/README.md b/README.md index 76cc0d1..d10ffa5 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@ # BatchPDLP.jl -This package applies the PDLP algorithm [[1](#references)] to solve batches of small, structurally similar linear programs (LPs) simultaneously using NVIDIA GPU resources through CUDA.jl [[2](#references)]. It assumes that LPs are provided (or can be generated) in GPU memory, and only stores solution information in GPU memory. I.e., this package does not manage the transfer of LP information to or from the CPU. `BatchPDLP.jl` is meant to be used within a global optimizer such as `EAGO.jl` [[3](#references)] to solve large numbers of LPs of sizes typically seen within global optimization. `BatchPDLP.jl` was tested on batches of LPs with up to roughly 100 variables and 1000 constraints each. +This package includes and applies both the standard and the improved PDLP algorithms [[1](#references)] to solve batches of small, structurally similar linear programs (LPs) simultaneously using NVIDIA GPU resources through CUDA.jl [[2](#references)]. The standard version is based on the original PDLP algorithm which can be called using the version key `:original`. The improved version is based on the restarted Halpern reflection scheme [[3](#references)], which is an improvement on the original PDLP algorithm, and can be called using the version key `:rHalpern`. `BatchPDLP.jl` assumes that LPs are provided (or can be generated) in GPU memory, and only stores solution information in GPU memory. I.e., this package does not manage the transfer of LP information to or from the CPU. `BatchPDLP.jl` is meant to be used within a global optimizer such as `EAGO.jl` [[4](#references)] to solve large numbers of LPs of sizes typically seen within global optimization. `BatchPDLP.jl` was tested on batches of LPs with up to roughly 100 variables and 1000 constraints each. ## Usage Note -`BatchPDLP.jl` is primarily designed to work with `SourceCodeMcCormick.jl` [[4](#references)], which calculates McCormick relaxations and their subgradients for factorable expressions. These relaxations and their subgradients can then be converted into LP constraints through `BatchPDLP.jl`, after which the PDLP algorithm can be run. Internally, `BatchPDLP.jl` works by launching a kernel with the number of blocks set equal to the number of LPs being solved, meaning the number of LPs per batch should generally be larger than the number of streaming multiprocessors (SMs) in the GPU. +`BatchPDLP.jl` is primarily designed to work with `SourceCodeMcCormick.jl` [[5](#references)], which calculates McCormick relaxations and their subgradients for factorable expressions. These relaxations and their subgradients can then be converted into LP constraints through `BatchPDLP.jl`, after which the PDLP algorithm can be run. Internally, `BatchPDLP.jl` works by launching a kernel with the number of blocks set equal to the number of LPs being solved, meaning the number of LPs per batch should generally be larger than the number of streaming multiprocessors (SMs) in the GPU. -More specifically, `BatchPDLP.jl` is not meant to handle single LP instances. It is explicitly not designed to handle the typical "large" LPs seen in, e.g., LP benchmark test sets, which makes it distinct from many other GPU-accelerated LP solvers such as `cuPDLP.jl` [[5](#references)]. `BatchPDLP.jl` is only meant to be performant when solving hundreds, thousands, or tens of thousands of structurally similar LPs. +More specifically, `BatchPDLP.jl` is not meant to handle single LP instances. It is explicitly not designed to handle the typical "large" LPs seen in, e.g., LP benchmark test sets, which makes it distinct from many other GPU-accelerated LP solvers such as `cuPDLP.jl` and `cuPDLPx`[[6](#references), [7](#references)]. `BatchPDLP.jl` is only meant to be performant when solving hundreds, thousands, or tens of thousands of structurally similar LPs. ## Basic Functionality @@ -66,7 +66,7 @@ PDLPData.parameters.termination_criteria.eps_primal_infeasible = 1E-7 PDLPData.parameters.termination_criteria.eps_dual_infeasible = 1E-7 ``` -There are several functions built into `lower_level_subroutines.jl` and `kernels.jl` that can be used to add constraints to the necessary fields in `PDLPData`, although they are written specifically to interact with `SourceCodeMcCormick.jl` and the `ParBB` extension of EAGO. An example of how some of these functions are meant to be used is included in the `./benchmarks` folder, along with the script that was run to generate benchmark results for the paper. Generally, if a constraint is to be added to each LP, the following actions should be taken: +There are several functions built into `lower_level_subroutines.jl` and `kernels.jl` that can be used to add constraints to the necessary fields in `PDLPData`, although they are written specifically to interact with `SourceCodeMcCormick.jl` and the `ParBB` extension of `EAGO.jl` An example of how some of these functions are meant to be used is included in the `./examples/benchmarks` folder, along with the script that was run to generate benchmark results for the paper. Generally, if a constraint is to be added to each LP, the following actions should be taken: 1) Ensure that the LP constraint to be added is in `>=` form, with only a constant on the right-hand side. 2) Add variable coefficients for this constraint to the `PDLPData.original_problem.constraint_matrix` field, in the line `PDLPData.dims.current_LP_length + 1` for each LP. Note that `PDLPData.dims.current_LP_length + 1` should not exceed `PDLPData.dims.total_LP_length`, or else data will spill into the constraint space designated for the following LP(s). @@ -74,7 +74,7 @@ There are several functions built into `lower_level_subroutines.jl` and `kernels 4) Change `PDLPData.active_constraint` at the index `PDLPData.dims.current_LP_length + 1` to `true`, for each LP where a constraint was added. 5) Set `PDLPData.current_LP_length` equal to `PDLPData.current_LP_length + 1`. -Once all necessary constraints have been added, the LPs can be solved by calling the `PDLP` function with the `PDLPData` struct as an argument. Storage for the LP solutions (with a size of (`PDLPData.dims.n_LPs`, `PDLPData.dims.n_vars`)) and objective values (with a size of (`PDLPData.dims.n_LPs`)) are provided as arguments, though are not technically required. The global upper bound used in a global optimization routine may also be provided as an argument, which will cause `BatchPDLP.jl` to terminate individual LPs if dual feasible solutions are found with dual objective values greater than the global upper bound (with termination code `TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT`). The call to `PDLP` may look like the following: +Once all necessary constraints have been added, the LPs can be solved by calling the `PDLP` function with the `PDLPData` struct as an argument. Storage for the LP solutions (with a size of (`PDLPData.dims.n_LPs`, `PDLPData.dims.n_vars`)) and objective values (with a size of (`PDLPData.dims.n_LPs`)) are provided as arguments, though are not technically required. Also, the intended version (either `:rHalpern` or `:original`) can be passed as an argument, where the default is `:rHalpern`. The global upper bound used in a global optimization routine may also be provided as an argument, which will cause `BatchPDLP.jl` to terminate individual LPs if dual feasible solutions are found with dual objective values greater than the global upper bound (with termination code `TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT`). The call to `PDLP` may look like the following: ```julia using BatchPDLP, CUDA @@ -91,8 +91,9 @@ upper_bound = 0.0 # Run PDLP PDLP(PDLP_data, - solutions=PDLP_solutions + solutions=PDLP_solutions, objectives=dual_objectives, + version = :rHalpern, global_upper_bound=upper_bound) ``` @@ -123,6 +124,8 @@ A BibTeX entry is given below: ## References 1. Applegate, D., Díaz, M., Hinder, O., Lu, H., Lubin, M., O’Donoghue, B., Schudy, W.: Practical large-scale linear programming using primal-dual hybrid gradient (2021) https://doi.org/10.48550/ARXIV.2106.04756 arXiv:2106.04756 [math.OC] 2. Besard, T., Foket, C., and De Sutter, B. Effective Extensible Programming: Unleashing Julia on GPUs. IEEE Transactions on Parallel and Distributed Systems (2018). https://doi.org/10.1109/TPDS.2018.2872064 -3. Wilhelm, M.E., Stuber, M.D.: EAGO.jl: easy advanced global optimization in Julia. Optimization Methods and Software 37(2), 425–450 (2022) https://doi.org/10.1080/10556788.2020.1786566 -4. Gottlieb, R.X., Stuber, M.D.: Automatic generation of GPU kernels for evaluators of McCormick-based relaxations and subgradients. Under Revision (2025) -5. Lu, H., Yang, J.: cuPDLP.jl: A GPU implementation of restarted primal-dual hybrid gradient for linear programming in Julia (2024) https://doi.org/10.48550/ARXIV.2311.12180 arXiv:2311.12180 [math.OC] \ No newline at end of file +3. Lu, H., Yang, J.: Restarted Halpern PDHG for Linear Programming (2024) https://doi.org/10.48550/arXiv.2407.16144 arXiv:2407.16144 [math.OC] +4. Wilhelm, M.E., Stuber, M.D.: EAGO.jl: easy advanced global optimization in Julia. Optimization Methods and Software 37(2), 425–450 (2022) https://doi.org/10.1080/10556788.2020.1786566 +5. Gottlieb, R.X., Stuber, M.D.: Automatic generation of GPU kernels for evaluators of McCormick-based relaxations and subgradients. Under Revision (2025) +6. Lu, H., Yang, J.: cuPDLP.jl: A GPU implementation of restarted primal-dual hybrid gradient for linear programming in Julia (2024) https://doi.org/10.48550/ARXIV.2311.12180 arXiv:2311.12180 [math.OC] +7. Lu, H., Peng, Z., Yang, J.: cuPDLPx: A Further Enhanced GPU-Based First-Order Solver for Linear Programming (2025) https://doi.org/10.48550/arXiv.2507.14051 arXiv:2507.14051 [math.OC] diff --git a/benchmarks/Problem List.xlsx b/examples/Problem List.xlsx similarity index 100% rename from benchmarks/Problem List.xlsx rename to examples/Problem List.xlsx diff --git a/benchmarks/all_examples/alkyl.jl b/examples/all_examples/alkyl.jl similarity index 100% rename from benchmarks/all_examples/alkyl.jl rename to examples/all_examples/alkyl.jl diff --git a/benchmarks/all_examples/alkylation.jl b/examples/all_examples/alkylation.jl similarity index 100% rename from benchmarks/all_examples/alkylation.jl rename to examples/all_examples/alkylation.jl diff --git a/benchmarks/all_examples/ann_compressor_exp.jl b/examples/all_examples/ann_compressor_exp.jl similarity index 100% rename from benchmarks/all_examples/ann_compressor_exp.jl rename to examples/all_examples/ann_compressor_exp.jl diff --git 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benchmarks/all_examples/gancns.jl rename to examples/all_examples/gancns.jl diff --git a/benchmarks/all_examples/glider50.jl b/examples/all_examples/glider50.jl similarity index 100% rename from benchmarks/all_examples/glider50.jl rename to examples/all_examples/glider50.jl diff --git a/benchmarks/all_examples/gsg_0001.jl b/examples/all_examples/gsg_0001.jl similarity index 100% rename from benchmarks/all_examples/gsg_0001.jl rename to examples/all_examples/gsg_0001.jl diff --git a/benchmarks/all_examples/hhfair.jl b/examples/all_examples/hhfair.jl similarity index 100% rename from benchmarks/all_examples/hhfair.jl rename to examples/all_examples/hhfair.jl diff --git a/benchmarks/all_examples/hs62.jl b/examples/all_examples/hs62.jl similarity index 100% rename from benchmarks/all_examples/hs62.jl rename to examples/all_examples/hs62.jl diff --git a/benchmarks/all_examples/hybriddynamic_varcc.jl b/examples/all_examples/hybriddynamic_varcc.jl similarity index 100% rename from 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examples/all_examples/kall_ellipsoids_tc02b.jl diff --git a/benchmarks/all_examples/kall_ellipsoids_tc03c.jl b/examples/all_examples/kall_ellipsoids_tc03c.jl similarity index 100% rename from benchmarks/all_examples/kall_ellipsoids_tc03c.jl rename to examples/all_examples/kall_ellipsoids_tc03c.jl diff --git a/benchmarks/all_examples/kall_ellipsoids_tc05a.jl b/examples/all_examples/kall_ellipsoids_tc05a.jl similarity index 100% rename from benchmarks/all_examples/kall_ellipsoids_tc05a.jl rename to examples/all_examples/kall_ellipsoids_tc05a.jl diff --git a/benchmarks/all_examples/korcns.jl b/examples/all_examples/korcns.jl similarity index 100% rename from benchmarks/all_examples/korcns.jl rename to examples/all_examples/korcns.jl diff --git a/benchmarks/all_examples/kriging_peaks_full010.jl b/examples/all_examples/kriging_peaks_full010.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_full010.jl rename to examples/all_examples/kriging_peaks_full010.jl diff --git a/benchmarks/all_examples/kriging_peaks_full020.jl b/examples/all_examples/kriging_peaks_full020.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_full020.jl rename to examples/all_examples/kriging_peaks_full020.jl diff --git a/benchmarks/all_examples/kriging_peaks_full030.jl b/examples/all_examples/kriging_peaks_full030.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_full030.jl rename to examples/all_examples/kriging_peaks_full030.jl diff --git a/benchmarks/all_examples/kriging_peaks_full050.jl b/examples/all_examples/kriging_peaks_full050.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_full050.jl rename to examples/all_examples/kriging_peaks_full050.jl diff --git a/benchmarks/all_examples/kriging_peaks_full100.jl b/examples/all_examples/kriging_peaks_full100.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_full100.jl rename to examples/all_examples/kriging_peaks_full100.jl diff --git a/benchmarks/all_examples/kriging_peaks_full200.jl b/examples/all_examples/kriging_peaks_full200.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_full200.jl rename to examples/all_examples/kriging_peaks_full200.jl diff --git a/benchmarks/all_examples/kriging_peaks_red010.jl b/examples/all_examples/kriging_peaks_red010.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red010.jl rename to examples/all_examples/kriging_peaks_red010.jl diff --git a/benchmarks/all_examples/kriging_peaks_red020.jl b/examples/all_examples/kriging_peaks_red020.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red020.jl rename to examples/all_examples/kriging_peaks_red020.jl diff --git a/benchmarks/all_examples/kriging_peaks_red030.jl b/examples/all_examples/kriging_peaks_red030.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red030.jl rename to examples/all_examples/kriging_peaks_red030.jl diff --git a/benchmarks/all_examples/kriging_peaks_red050.jl b/examples/all_examples/kriging_peaks_red050.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red050.jl rename to examples/all_examples/kriging_peaks_red050.jl diff --git a/benchmarks/all_examples/kriging_peaks_red100.jl b/examples/all_examples/kriging_peaks_red100.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red100.jl rename to examples/all_examples/kriging_peaks_red100.jl diff --git a/benchmarks/all_examples/kriging_peaks_red200.jl b/examples/all_examples/kriging_peaks_red200.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red200.jl rename to examples/all_examples/kriging_peaks_red200.jl diff --git a/benchmarks/all_examples/kriging_peaks_red500.jl b/examples/all_examples/kriging_peaks_red500.jl similarity index 100% rename from benchmarks/all_examples/kriging_peaks_red500.jl rename to examples/all_examples/kriging_peaks_red500.jl diff --git a/benchmarks/all_examples/lakes.jl b/examples/all_examples/lakes.jl similarity index 100% rename from benchmarks/all_examples/lakes.jl rename to examples/all_examples/lakes.jl diff --git a/benchmarks/all_examples/launch.jl b/examples/all_examples/launch.jl similarity index 100% rename from benchmarks/all_examples/launch.jl rename to examples/all_examples/launch.jl diff --git a/benchmarks/all_examples/least.jl b/examples/all_examples/least.jl similarity index 100% rename from benchmarks/all_examples/least.jl rename to examples/all_examples/least.jl diff --git a/benchmarks/all_examples/like.jl b/examples/all_examples/like.jl similarity index 100% rename from benchmarks/all_examples/like.jl rename to examples/all_examples/like.jl diff --git a/benchmarks/all_examples/mathopt1.jl b/examples/all_examples/mathopt1.jl similarity index 100% rename from benchmarks/all_examples/mathopt1.jl rename to examples/all_examples/mathopt1.jl diff --git a/benchmarks/all_examples/mathopt2.jl 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a/benchmarks/all_examples/mathopt5_3.jl b/examples/all_examples/mathopt5_3.jl similarity index 100% rename from benchmarks/all_examples/mathopt5_3.jl rename to examples/all_examples/mathopt5_3.jl diff --git a/benchmarks/all_examples/mathopt5_4.jl b/examples/all_examples/mathopt5_4.jl similarity index 100% rename from benchmarks/all_examples/mathopt5_4.jl rename to examples/all_examples/mathopt5_4.jl diff --git a/benchmarks/all_examples/mathopt5_5.jl b/examples/all_examples/mathopt5_5.jl similarity index 100% rename from benchmarks/all_examples/mathopt5_5.jl rename to examples/all_examples/mathopt5_5.jl diff --git a/benchmarks/all_examples/mathopt5_7.jl b/examples/all_examples/mathopt5_7.jl similarity index 100% rename from benchmarks/all_examples/mathopt5_7.jl rename to examples/all_examples/mathopt5_7.jl diff --git a/benchmarks/all_examples/mathopt5_8.jl b/examples/all_examples/mathopt5_8.jl similarity index 100% rename from benchmarks/all_examples/mathopt5_8.jl rename to examples/all_examples/mathopt5_8.jl diff --git a/benchmarks/all_examples/mathopt6.jl b/examples/all_examples/mathopt6.jl similarity index 100% rename from benchmarks/all_examples/mathopt6.jl rename to examples/all_examples/mathopt6.jl diff --git a/benchmarks/all_examples/maxmin.jl b/examples/all_examples/maxmin.jl similarity index 100% rename from benchmarks/all_examples/maxmin.jl rename to examples/all_examples/maxmin.jl diff --git a/benchmarks/all_examples/mhw4d.jl b/examples/all_examples/mhw4d.jl similarity index 100% rename from benchmarks/all_examples/mhw4d.jl rename to examples/all_examples/mhw4d.jl diff --git a/benchmarks/all_examples/minlphi.jl b/examples/all_examples/minlphi.jl similarity index 100% rename from benchmarks/all_examples/minlphi.jl rename to examples/all_examples/minlphi.jl diff --git a/benchmarks/all_examples/orth_d3m6.jl b/examples/all_examples/orth_d3m6.jl similarity index 100% rename from benchmarks/all_examples/orth_d3m6.jl rename to examples/all_examples/orth_d3m6.jl diff --git a/benchmarks/all_examples/orth_d3m6_pl.jl b/examples/all_examples/orth_d3m6_pl.jl similarity index 100% rename from benchmarks/all_examples/orth_d3m6_pl.jl rename to examples/all_examples/orth_d3m6_pl.jl diff --git a/benchmarks/all_examples/orth_d4m6_pl.jl b/examples/all_examples/orth_d4m6_pl.jl similarity index 100% rename from benchmarks/all_examples/orth_d4m6_pl.jl rename to examples/all_examples/orth_d4m6_pl.jl diff --git a/benchmarks/all_examples/otpop.jl b/examples/all_examples/otpop.jl similarity index 100% rename from benchmarks/all_examples/otpop.jl rename to examples/all_examples/otpop.jl diff --git a/benchmarks/all_examples/polygon25.jl b/examples/all_examples/polygon25.jl similarity index 100% rename from benchmarks/all_examples/polygon25.jl rename to examples/all_examples/polygon25.jl diff --git a/benchmarks/all_examples/polygon50.jl b/examples/all_examples/polygon50.jl similarity index 100% rename from benchmarks/all_examples/polygon50.jl rename to examples/all_examples/polygon50.jl diff --git a/benchmarks/all_examples/powerflow0009p.jl b/examples/all_examples/powerflow0009p.jl similarity index 100% rename from benchmarks/all_examples/powerflow0009p.jl rename to examples/all_examples/powerflow0009p.jl diff --git a/benchmarks/all_examples/pricing050.jl b/examples/all_examples/pricing050.jl similarity index 100% rename from benchmarks/all_examples/pricing050.jl rename to examples/all_examples/pricing050.jl diff --git a/benchmarks/all_examples/prob07.jl b/examples/all_examples/prob07.jl similarity index 100% rename from benchmarks/all_examples/prob07.jl rename to examples/all_examples/prob07.jl diff --git a/benchmarks/all_examples/prob09.jl b/examples/all_examples/prob09.jl similarity index 100% rename from benchmarks/all_examples/prob09.jl rename to examples/all_examples/prob09.jl diff --git a/benchmarks/all_examples/process.jl b/examples/all_examples/process.jl similarity index 100% rename from benchmarks/all_examples/process.jl rename to examples/all_examples/process.jl diff --git a/benchmarks/all_examples/ramsey.jl b/examples/all_examples/ramsey.jl similarity index 100% rename from benchmarks/all_examples/ramsey.jl rename to examples/all_examples/ramsey.jl diff --git a/benchmarks/all_examples/rbrock.jl b/examples/all_examples/rbrock.jl similarity index 100% rename from benchmarks/all_examples/rbrock.jl rename to examples/all_examples/rbrock.jl diff --git a/benchmarks/all_examples/rocket100.jl b/examples/all_examples/rocket100.jl similarity index 100% rename from benchmarks/all_examples/rocket100.jl rename to examples/all_examples/rocket100.jl diff --git a/benchmarks/all_examples/rocket50.jl b/examples/all_examples/rocket50.jl similarity index 100% rename from benchmarks/all_examples/rocket50.jl rename to examples/all_examples/rocket50.jl diff --git a/benchmarks/all_examples/shiporig.jl b/examples/all_examples/shiporig.jl similarity index 100% rename from benchmarks/all_examples/shiporig.jl rename to examples/all_examples/shiporig.jl diff --git a/benchmarks/all_examples/st_e03.jl b/examples/all_examples/st_e03.jl similarity index 100% rename from benchmarks/all_examples/st_e03.jl rename to examples/all_examples/st_e03.jl diff --git a/benchmarks/all_examples/st_e04.jl b/examples/all_examples/st_e04.jl similarity index 100% rename from benchmarks/all_examples/st_e04.jl rename to examples/all_examples/st_e04.jl diff --git a/benchmarks/all_examples/st_e06.jl b/examples/all_examples/st_e06.jl similarity index 100% rename from benchmarks/all_examples/st_e06.jl rename to examples/all_examples/st_e06.jl diff --git a/benchmarks/all_examples/st_e11.jl b/examples/all_examples/st_e11.jl similarity index 100% rename from benchmarks/all_examples/st_e11.jl rename to examples/all_examples/st_e11.jl diff --git a/benchmarks/all_examples/st_e12.jl b/examples/all_examples/st_e12.jl similarity index 100% rename from benchmarks/all_examples/st_e12.jl rename to examples/all_examples/st_e12.jl diff --git a/benchmarks/all_examples/st_e16.jl b/examples/all_examples/st_e16.jl similarity index 100% rename from benchmarks/all_examples/st_e16.jl rename to examples/all_examples/st_e16.jl diff --git a/benchmarks/all_examples/st_e19.jl b/examples/all_examples/st_e19.jl similarity index 100% rename from benchmarks/all_examples/st_e19.jl rename to examples/all_examples/st_e19.jl diff --git a/benchmarks/all_examples/st_e21.jl b/examples/all_examples/st_e21.jl similarity index 100% rename from benchmarks/all_examples/st_e21.jl rename to examples/all_examples/st_e21.jl diff --git a/benchmarks/all_examples/st_e37.jl b/examples/all_examples/st_e37.jl similarity index 100% rename from benchmarks/all_examples/st_e37.jl rename to examples/all_examples/st_e37.jl diff --git a/benchmarks/all_examples/st_e41.jl b/examples/all_examples/st_e41.jl similarity index 100% rename from benchmarks/all_examples/st_e41.jl rename to examples/all_examples/st_e41.jl diff --git a/benchmarks/all_examples/steenbrf.jl b/examples/all_examples/steenbrf.jl similarity index 100% rename from benchmarks/all_examples/steenbrf.jl rename to examples/all_examples/steenbrf.jl diff --git a/benchmarks/all_examples/trig.jl b/examples/all_examples/trig.jl similarity index 100% rename from benchmarks/all_examples/trig.jl rename to examples/all_examples/trig.jl diff --git a/benchmarks/all_examples/trigx.jl b/examples/all_examples/trigx.jl similarity index 100% rename from benchmarks/all_examples/trigx.jl rename to examples/all_examples/trigx.jl diff --git a/benchmarks/all_examples/wallfix.jl b/examples/all_examples/wallfix.jl similarity index 100% rename from benchmarks/all_examples/wallfix.jl rename to examples/all_examples/wallfix.jl diff --git a/examples/benchmarks/Improved Version Benchmarks/Improved BatchPDLP Benchmarking.jl b/examples/benchmarks/Improved Version Benchmarks/Improved BatchPDLP Benchmarking.jl new file mode 100644 index 0000000..427bdab --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/Improved BatchPDLP Benchmarking.jl @@ -0,0 +1,383 @@ + +# Use the necessary packages +using Revise +using CSV, Tables, DataFrames +using BatchPDLP, SourceCodeMcCormick, CUDA, Dates, Random +# const GRB_ENV = Gurobi.Env() + +# Include all files from the master list +include("./examples/benchmarks/Original Version Benchmarks/only_optimal_master_list.jl") + +# Using the number of problems and the dimensionality, create a partition of the +# space of the original problem +function case_generator(lbd, ubd, n, m) + all_lvbs = zeros(m, n) + all_uvbs = zeros(m, n) + all_lvbs[1,:] .= lbd + all_uvbs[1,:] .= ubd + + # Deal with +/-Inf by setting them to +/- 1e6 + for i in eachindex(lbd) + if all_lvbs[1,i]==Inf + all_lvbs[1,i] = 1E6 + elseif all_lvbs[1,i]==-Inf + all_lvbs[1,i] = -1E6 + end + if all_uvbs[1,i]==Inf + all_uvbs[1,i] = 1E6 + elseif all_uvbs[1,i]==-Inf + all_uvbs[1,i] = -1E6 + end + end + + var = 1 + curr_len = 1 + while curr_len < m + # Only make up to m nodes in total, m_new at a time + m_new = min(m - curr_len, curr_len) + active_len = curr_len + + for i = 1:m_new + # Create a copy of the i'th set of bounds + all_lvbs[curr_len+i,:] .= all_lvbs[i,:] + all_uvbs[curr_len+i,:] .= all_uvbs[i,:] + + # Get the midpoint of this subdomain in the `var` dimension + midpoint = (all_uvbs[i,var] + all_lvbs[i,var])/2 + + # Adjust the original and copy of the i'th node to contain + # the two halves of variable `var`, split at the midpoint + all_uvbs[i,var] = midpoint + all_lvbs[curr_len+i,var] = midpoint + + # Increment the current length tracker + active_len += 1 + end + curr_len = active_len + + # Increment the variable index being split, up to varmax + var < n ? (var += 1) : (var = 1) + end + return all_lvbs, all_uvbs +end + +function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchPDLP::Bool=true) + ############################################################################## + ######### Step 1) Get Bounds + ############################################################################## + + # Based on the problem, partition the domain into n_LPs portions. + lvbs, uvbs = case_generator(example.lvbs, example.uvbs, example.nvars, n_LPs) + + # Set up the bounds as CuArrays + lvbs_d = CuArray(lvbs) + uvbs_d = CuArray(uvbs) + + leq_len = length(example.leq_cons) + geq_len = length(example.geq_cons) + eq_len = length(example.eq_cons) + + ############################################################################## + ######### Step 2) Set up the PDLP struct + ############################################################################## + + # Calculate the sparsity for PDLP + cut_height = (1 + leq_len + geq_len + 2*eq_len) + sparsity = zeros(Bool, 1 + n_cuts*cut_height, example.nvars+1) + + # Lower bound, epigraph variable is always 1, other variables are 0 + sparsity[1, 1] = true + + # Objective function + for i = 1:n_cuts + sparsity[2 + (i-1)*cut_height, :] .= [true, example.obj_sp...] + end + + # LEQ constraints + for i = 1:n_cuts + for j = 1:leq_len + sparsity[2 + j + (i-1)*cut_height, :] .= [false, example.leq_sp[j]...] + end + end + + # GEQ constraints + for i = 1:n_cuts + for j = 1:geq_len + sparsity[2 + leq_len + j + (i-1)*cut_height, :] .= [false, example.geq_sp[j]...] + end + end + + # EQ constraints + for i = 1:n_cuts + for j = 1:eq_len + sparsity[2 + leq_len + geq_len + (2j-1) + (i-1)*cut_height, :] .= [false, example.eq_sp[j]...] + sparsity[2 + leq_len + geq_len + (2j-0) + (i-1)*cut_height, :] .= [false, example.eq_sp[j]...] + end + end + + + ############################################################################## + ######### Step 3) Evaluate terms using SCMC + ############################################################################## + + # Fill in eval_points very simply, just doing a straight line through all + # the variables based on the number of cuts + eval_points = CUDA.zeros(Float64, n_LPs*n_cuts, example.nvars) + + points = collect(0:(1/(n_cuts+1)):1) + for i=1:n_cuts + @. eval_points[(i-1)*n_LPs + 1 : i*n_LPs, :] = (lvbs_d + uvbs_d).*points[i+1] + end + + # The objective function and constraints take variables as inputs, where each + # variable needs its eval point, the lower bound, and the upper bound. Create those. + input_storage = [CuArray{Float64}(undef, n_LPs*n_cuts, 3) for i=1:example.nvars] + for i = 1:example.nvars + input_storage[i] = hcat(eval_points[:,i], repeat(lvbs_d[:,i], n_cuts), repeat(uvbs_d[:,i], n_cuts)) + end + + # Calculate relaxations for the objective function and constraints + result_width = 4 + 2*example.nvars + obj_result_storage = CUDA.zeros(Float64, n_LPs*n_cuts, result_width) + leq_result_storage = [CUDA.zeros(Float64, n_LPs*n_cuts, result_width) for i = 1:leq_len] + geq_result_storage = [CUDA.zeros(Float64, n_LPs*n_cuts, result_width) for i = 1:geq_len] + eq_result_storage = [CUDA.zeros(Float64, n_LPs*n_cuts, result_width) for i = 1:eq_len] + + # Objective function + CUDA.@sync example.obj_fun(obj_result_storage, [input_storage[j] for j=1:example.nvars]...) + + # LEQ constraints + for i in 1:leq_len + CUDA.@sync example.leq_cons[i](leq_result_storage[i], [input_storage[j] for j=1:example.nvars]...) + end + + # GEQ constraints + for i in 1:geq_len + CUDA.@sync example.geq_cons[i](geq_result_storage[i], [input_storage[j] for j=1:example.nvars]...) + end + + # EQ constraints + for i in 1:eq_len + CUDA.@sync example.eq_cons[i](eq_result_storage[i], [input_storage[j] for j=1:example.nvars]...) + end + + # Feed in data to the PDLPData struct + PDLP_data = PDLPData(n_LPs, example.nvars+1, 1+n_cuts*cut_height, sparsity=sparsity) + + # Since the PDLP_GPU struct already has an allocated field for PDLP data, we + # only need to update that field with the new LP to solve. The main PDLP + # algorithm already resets all fields except for the original LP, so to prepare + # for this next PDLP run, all we need to do is update the original_problem + # field. + LPs = PDLP_data.original_problem + LPs.variable_lower_bounds .= hcat(CUDA.fill(-Inf, n_LPs), lvbs_d) # The left-most column is for the epigraph variable + LPs.variable_upper_bounds .= hcat(CUDA.fill( Inf, n_LPs), uvbs_d) + CUDA.fill!(LPs.constraint_matrix, 0.0) + CUDA.fill!(LPs.right_hand_side, 0.0) + LPs.objective_vector .= hcat(CUDA.ones(Float64, n_LPs), CUDA.zeros(Float64, n_LPs, example.nvars)) # Minimize the epigraph variable + CUDA.fill!(LPs.objective_constant, 0.0) + CUDA.fill!(PDLP_data.active_constraint, false) + PDLP_data.dims.current_LP_length = Int32(0) # Zero, until we add constraints + # PDLP_data.dims.total_LP_length #unchanged + PDLP_data.dims.n_LPs = Int32(n_LPs) + # PDLP_data.dims.n_vars = Int32(varmax) #unchanged + + + # Now that the original_problem field is fully reset, we can begin filling in constraints. + + # First, add the lower bound from the inclusion monotonic interval extension as a constraint. + # This has a specialized (simpler) function call + add_LP_lower_bound(LPs, obj_result_storage[1:n_LPs,:], PDLP_data.active_constraint, PDLP_data.dims) + + # For each of the cuts... + for i = 1:n_cuts + # Next, add in the objective function's relaxation at the midpoint as a constraint + add_LP_objective_constraint(LPs, obj_result_storage[(i-1)*n_LPs+1:i*n_LPs,:], eval_points[(i-1)*n_LPs+1:i*n_LPs,:], PDLP_data.active_constraint, PDLP_data.dims) + + # Add in each of the regular constraints + # LEQ constraints + for j in 1:leq_len + add_LP_constraint(LPs, leq_result_storage[j][(i-1)*n_LPs+1:i*n_LPs,:], eval_points[(i-1)*n_LPs+1:i*n_LPs,:], PDLP_data.active_constraint, PDLP_data.dims, geq=false) + end + + # GEQ constraints + for j in 1:geq_len + add_LP_constraint(LPs, geq_result_storage[j][(i-1)*n_LPs+1:i*n_LPs,:], eval_points[(i-1)*n_LPs+1:i*n_LPs,:], PDLP_data.active_constraint, PDLP_data.dims, geq=true) + end + + # EQ constraints + for j in 1:eq_len + add_LP_constraint(LPs, eq_result_storage[j][(i-1)*n_LPs+1:i*n_LPs,:], eval_points[(i-1)*n_LPs+1:i*n_LPs,:], PDLP_data.active_constraint, PDLP_data.dims, geq=false) + add_LP_constraint(LPs, eq_result_storage[j][(i-1)*n_LPs+1:i*n_LPs,:], eval_points[(i-1)*n_LPs+1:i*n_LPs,:], PDLP_data.active_constraint, PDLP_data.dims, geq=true) + end + end + + + ############################################################################## + ######### Step 4) Print Problem Data + ############################################################################## + + # Print out problem information + CUDA.memory_status() + println("Example: $(example.name)") + println("Base problem: $(example.nvars) variables, $(example.ncons) constraints") + println("There are $leq_len LEQ constraints, $geq_len GEQ constraints, and $eq_len EQ constraints.") + println("Doing $n_cuts cuts, so each LP has $(PDLP_data.dims.total_LP_length) constraints") + + + ############################################################################## + ######### Step 5) Solve Using BatchPDLP + ############################################################################## + + # Allocate storage for solutions and objectives + PDLP_solutions = CUDA.zeros(Float64, n_LPs, example.nvars+1) + PDLP_objectives = CUDA.zeros(Float64, n_LPs, 2) + PDLP_lowres_solutions = CUDA.zeros(Float64, n_LPs, example.nvars+1) + PDLP_lowres_objectives = CUDA.zeros(Float64, n_LPs, 2) + + # Solve the problems once for compilation + if run_BatchPDLP + try + PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, return_both_obj=true) + catch + nothing + end + end + + # Solve the problem once at higher tolerances (default is 1E-8 for abs,rel,primal/dual infeas) + GC.gc() + GC.enable(false) # Disable garbage collection temporarily to not impact results + + # Set up storage values + PDLP_solving_time = zeros(Float64, n_LPs) + PDLP_lowres_solving_time = zeros(Float64, n_LPs) + PDLP_95pct_solving_time = zeros(Float64, n_LPs) + + # Solve for the first time, but only do (at least) 95% of problems + # (then reset and run all the problems as normal) + if run_BatchPDLP + println("Starting BatchPDLPx") + iterations = Array(PDLP_data.iterations) + + stop_here = 0 + for i in sort(unique(iterations)) + + if count(iterations .<= i) >= 0.95*n_LPs + stop_here = i + break + end + end + + + PDLP_data.parameters.iteration_limit = Int32(stop_here) + try + PDLP_95pct_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, version=:rHalpern, return_both_obj=true) + catch + PDLP_95pct_solving_time[1] = NaN + end + end + + + # Save objectives and termination status + PDLP_95pct_objectives_array = Array(PDLP_objectives) + PDLP_95pct_termination_array = Array(PDLP_data.termination_reason) + PDLP_95pct_iterations_array = Array(PDLP_data.iterations) + + # Reset the iteration limit for future runs + PDLP_data.parameters.iteration_limit = Int32(1000000) + + # Run PDLP with an iteration limit of 1E6 and tolerances of 1E-8 + println("-------------------------------------------------------------------------------------------------------------------------------") + CUDA.memory_status() + println("Solving with tolerance of 1E-8") + if run_BatchPDLP + try + PDLP_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, version=:rHalpern, return_both_obj=true) + catch + PDLP_solving_time[1] = NaN + end + end + + # # Save objectives and termination statuses as CPU arrays + PDLP_objectives_array = Array(PDLP_objectives) + PDLP_termination_array = Array(PDLP_data.termination_reason) + PDLP_iterations_array = Array(PDLP_data.iterations) + + println("-------------------------------------------------------------------------------------------------------------------------------") + CUDA.memory_status() + println("Solving with tolerance of 1E-4") + # Re-run PDLP using lower tolerances (1E-4 instead of 1E-8) + # (Keep infeasibility tolerances at 1E-8) + PDLP_data.parameters.termination_criteria.eps_optimal_absolute = 1E-4 + PDLP_data.parameters.termination_criteria.eps_optimal_relative = 1E-4 + PDLP_data.parameters.termination_criteria.eps_primal_infeasible= 1E-8 + PDLP_data.parameters.termination_criteria.eps_dual_infeasible = 1E-8 + # Solve again with lower tolerances + if run_BatchPDLP + try + PDLP_lowres_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_lowres_solutions, objectives=PDLP_lowres_objectives, version=:rHalpern, return_both_obj=true) + catch + PDLP_lowres_solving_time[1] = NaN + end + end + + + GC.enable(true) # PDLP runs are finished; re-enable garbage collection + GC.gc() + + # Print out some PDLP solve times + if run_BatchPDLP + println("BatchPDLPx Solve Times:") + println("============================================================") + println("All problems (high-res): | $(round.(PDLP_solving_time[1], digits=6))") + println("All problems (low-res): | $(round.(PDLP_lowres_solving_time[1], digits=6))") + println(">95% of problems (high-res): | $(round.(PDLP_95pct_solving_time[1], digits=6))") + println("===========================================================") + end + + + # Save lowres solutions as regular arrays + PDLP_lowres_objectives_array = Array(PDLP_lowres_objectives) + PDLP_lowres_termination_array = Array(PDLP_data.termination_reason) + PDLP_lowres_iterations_array = Array(PDLP_data.iterations) + + res = hcat(PDLP_termination_array, + PDLP_iterations_array, + PDLP_objectives_array, + PDLP_solving_time, + PDLP_lowres_termination_array, + PDLP_lowres_iterations_array, + PDLP_lowres_objectives_array, + PDLP_lowres_solving_time, + PDLP_95pct_termination_array, + PDLP_95pct_iterations_array, + PDLP_95pct_objectives_array, + PDLP_95pct_solving_time + ) + return res + +end + +# Load and run the problem +ex = LoadedProblem(ex4_1_7, overwrite=true) + +data = run_example(ex, 10000, 3) +open("./examples/benchmarks/benchmarking_results/rundata_$(ex.name).csv", "w") do io + CSV.write(io, Tables.table(data), header=[ + "BatchPDLPx (1xGPU) Hi-Res - Term Status", + "BatchPDLPx (1xGPU) Hi-Res - Iterations", + "BatchPDLPx (1xGPU) Hi-Res - Primal Obj", + "BatchPDLPx (1xGPU) Hi-Res - Dual Obj", + "BatchPDLPx (1xGPU) Hi-Res - Time (s)", + "BatchPDLPx (1xGPU) Lo-Res - Term Status", + "BatchPDLPx (1xGPU) Lo-Res - Iterations", + "BatchPDLPx (1xGPU) Lo-Res - Primal Obj", + "BatchPDLPx (1xGPU) Lo-Res - Dual Obj", + "BatchPDLPx (1xGPU) Lo-Res - Time (s)", + "BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status", + "BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations", + "BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj", + "BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj", + "BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s)", + ],) +end \ No newline at end of file diff --git a/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_eq6_1.csv b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_eq6_1.csv new file mode 100644 index 0000000..0d9792d --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_eq6_1.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex4_1_3.csv new file mode 100644 index 0000000..8e810dc --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex4_1_3.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_1_1.csv new file mode 100644 index 0000000..9cd9a6b --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_1_1.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_1_2.csv new file mode 100644 index 0000000..b04002d --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_1_2.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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a/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_4_2.csv b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_4_2.csv new file mode 100644 index 0000000..bb93871 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_4_2.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_4_7.csv new file mode 100644 index 0000000..8c13014 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_4_7.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_6_2.csv new file mode 100644 index 0000000..8fd4858 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_ex8_6_2.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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Benchmarks/benchmarking_results/rundata_kriging_peaks_red010.csv b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_kriging_peaks_red010.csv new file mode 100644 index 0000000..dc75235 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_kriging_peaks_red010.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_mathopt5_3.csv new file mode 100644 index 0000000..f975e87 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_mathopt5_3.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_mathopt6.csv new file mode 100644 index 0000000..67894f6 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_mathopt6.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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Version Benchmarks/benchmarking_results/rundata_maxmin.csv new file mode 100644 index 0000000..587497b --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_maxmin.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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Benchmarks/benchmarking_results/rundata_prob09.csv b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_prob09.csv new file mode 100644 index 0000000..6f77a85 --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/benchmarking_results/rundata_prob09.csv @@ -0,0 +1,10001 @@ +BatchPDLPx (1xGPU) Hi-Res - Term Status,BatchPDLPx (1xGPU) Hi-Res - Iterations,BatchPDLPx (1xGPU) Hi-Res - Primal Obj,BatchPDLPx (1xGPU) Hi-Res - Dual Obj,BatchPDLPx (1xGPU) Hi-Res - Time (s),BatchPDLPx (1xGPU) Lo-Res - Term Status,BatchPDLPx (1xGPU) Lo-Res - Iterations,BatchPDLPx (1xGPU) Lo-Res - Primal Obj,BatchPDLPx (1xGPU) Lo-Res - Dual Obj,BatchPDLPx (1xGPU) Lo-Res - Time (s),BatchPDLPx (1xGPU) Hi-Res 95pct - Term Status,BatchPDLPx (1xGPU) Hi-Res 95pct - Iterations,BatchPDLPx (1xGPU) Hi-Res 95pct - Primal Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Dual Obj,BatchPDLPx (1xGPU) Hi-Res 95pct - Time (s) 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+TERMINATION_REASON_OPTIMAL,320,821859.7835330843,821859.784424315,0.0,TERMINATION_REASON_OPTIMAL,192,821862.5720366022,821856.8927286813,0.0,TERMINATION_REASON_OPTIMAL,320,821859.7835330843,821859.784424315,0.0 +TERMINATION_REASON_OPTIMAL,320,5554.362678216224,5554.36268586868,0.0,TERMINATION_REASON_OPTIMAL,128,5555.689782116856,5554.713909107124,0.0,TERMINATION_REASON_OPTIMAL,320,5554.362678216224,5554.36268586868,0.0 +TERMINATION_REASON_OPTIMAL,896,613651.7961206626,613651.7972430473,0.0,TERMINATION_REASON_OPTIMAL,128,613676.5473031494,613618.789616444,0.0,TERMINATION_REASON_OPTIMAL,896,613651.7961206626,613651.7972430473,0.0 +TERMINATION_REASON_OPTIMAL,512,659.3431494115842,659.3431483060758,0.0,TERMINATION_REASON_OPTIMAL,384,659.3432680780461,659.3501357649395,0.0,TERMINATION_REASON_OPTIMAL,512,659.3431494115842,659.3431483060758,0.0 +TERMINATION_REASON_OPTIMAL,320,122448.88539678113,122448.88554383659,0.0,TERMINATION_REASON_OPTIMAL,192,122449.31198207618,122448.38343214313,0.0,TERMINATION_REASON_OPTIMAL,320,122448.88539678113,122448.88554383659,0.0 +TERMINATION_REASON_OPTIMAL,320,7234.55255075755,7234.552560476568,0.0,TERMINATION_REASON_OPTIMAL,128,7236.227060276952,7235.080829925775,0.0,TERMINATION_REASON_OPTIMAL,320,7234.55255075755,7234.552560476568,0.0 +TERMINATION_REASON_OPTIMAL,768,55474.88514215663,55474.8850539534,0.0,TERMINATION_REASON_OPTIMAL,576,55474.99101653175,55473.943368498774,0.0,TERMINATION_REASON_OPTIMAL,768,55474.88514215663,55474.8850539534,0.0 +TERMINATION_REASON_OPTIMAL,512,224.6648585285147,224.66485940148593,0.0,TERMINATION_REASON_OPTIMAL,384,224.66576199550192,224.66256476603763,0.0,TERMINATION_REASON_OPTIMAL,512,224.6648585285147,224.66485940148593,0.0 diff --git a/examples/benchmarks/Improved Version Benchmarks/only_optimal_master_list.jl b/examples/benchmarks/Improved Version Benchmarks/only_optimal_master_list.jl new file mode 100644 index 0000000..769dd7d --- /dev/null +++ b/examples/benchmarks/Improved Version Benchmarks/only_optimal_master_list.jl @@ -0,0 +1,99 @@ + +# Bring in the packages we need +using SourceCodeMcCormick + +# Create a "problem" struct that contains useful information. +mutable struct Problem + name::String + vars::Vector{Num} + nvars::Int + ncons::Int + lvbs::Vector{Float64} + uvbs::Vector{Float64} + obj::Num + eq::Vector{Num} + leq::Vector{Num} + geq::Vector{Num} + sense::String +end + +function Problem(name::String, vars::Vector{Num}, obj::Num, lvbs, uvbs; eq=Num[], leq=Num[], geq=Num[], sense="min") + return Problem(name, vars, length(vars), length(eq)+length(leq)+length(geq), Float64.(lvbs), Float64.(uvbs), obj, eq, leq, geq, sense) +end + +mutable struct LoadedProblem + name::String + vars::Vector{Num} + nvars::Int + ncons::Int + lvbs::Vector{Float64} + uvbs::Vector{Float64} + obj_fun::Function + eq_cons::Vector{Function} + leq_cons::Vector{Function} + geq_cons::Vector{Function} + obj_sp::Vector{Bool} + eq_sp::Vector{Vector{Bool}} + leq_sp::Vector{Vector{Bool}} + geq_sp::Vector{Vector{Bool}} + sense::String +end + +function LoadedProblem(prob::Problem; overwrite::Bool=false) + return LoadedProblem(prob.name, prob.vars, prob.nvars, prob.ncons, prob.lvbs, prob.uvbs, + kgen(prob.obj, prob.vars, overwrite=overwrite), + kgen.(prob.eq, Ref(prob.vars), overwrite=overwrite), + kgen.(prob.leq, Ref(prob.vars), overwrite=overwrite), + kgen.(prob.geq, Ref(prob.vars), overwrite=overwrite), + [x in string.(pull_vars(prob.obj)) ? true : false for x in string.(prob.vars)], + [[x in string.(pull_vars(prob.eq[i])) ? true : false for x in string.(prob.vars)] for i in eachindex(prob.eq)], + [[x in string.(pull_vars(prob.leq[i])) ? true : false for x in string.(prob.vars)] for i in eachindex(prob.leq)], + [[x in string.(pull_vars(prob.geq[i])) ? true : false for x in string.(prob.vars)] for i in eachindex(prob.geq)], + prob.sense) +end + + +# Create symbolics variables for the examples +for i=1:1001 + var = Symbol("x$i") + @eval Symbolics.@variables $var +end + +# Create oft-used functions +sqr(x) = x^2 + +# List of included instances, and the number of variables and constraints +included = [ + "ex4_1_1" 1 0; + "rbrock" 2 0; + "ex8_4_2" 24 10; + "prob09" 3 1; + "eq6_1" 16 60; + "ex4_1_3" 1 0; + "ex4_1_4" 1 0; + "ex8_1_1" 1 1; + "ex8_1_2" 1 0; + "ex8_1_6" 2 0; + "ex8_4_7" 62 40; + "ex8_6_2" 30 0; + "kriging_peaks_red010" 2 0; + "mathopt5_1" 1 0; + "mathopt5_2" 1 0; + "mathopt5_3" 1 0; + "mathopt5_4" 1 0; + "mathopt5_5" 1 0; + "mathopt5_7" 1 0; + "mathopt5_8" 1 0; + "mathopt6" 1 0; + "maxmin" 27 78; +] + + + +for i in included[:,1] + try + include("./all_examples/$i.jl") + catch + println("$i failed") + end +end \ No newline at end of file diff --git a/benchmarks/BatchPDLP Benchmarking.jl b/examples/benchmarks/Original Version Benchmarks/BatchPDLP Benchmarking.jl similarity index 97% rename from benchmarks/BatchPDLP Benchmarking.jl rename to examples/benchmarks/Original Version Benchmarks/BatchPDLP Benchmarking.jl index f7fadd1..36e0dc7 100644 --- a/benchmarks/BatchPDLP Benchmarking.jl +++ b/examples/benchmarks/Original Version Benchmarks/BatchPDLP Benchmarking.jl @@ -5,7 +5,7 @@ using BatchPDLP, SourceCodeMcCormick, CUDA, JuMP, GLPK, Gurobi, HiGHS, Dates, Ra const GRB_ENV = Gurobi.Env() # Include all files from the master list -include("master_list.jl") +include("./examples/benchmarks/Original Version Benchmarks/master_list.jl") # Using the number of problems and the dimensionality, create a partition of the # space of the original problem @@ -239,7 +239,7 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP # Solve the problems once for compilation if run_BatchPDLP try - PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, return_both_obj=true) + PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, version=:original, return_both_obj=true) catch nothing end @@ -268,7 +268,7 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP end PDLP_data.parameters.iteration_limit = Int32(stop_here) try - PDLP_95pct_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, return_both_obj=true) + PDLP_95pct_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, version=:original, return_both_obj=true) catch PDLP_95pct_solving_time[1] = NaN end @@ -287,7 +287,7 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP # Run PDLP with an iteration limit of 1E6 and tolerances of 1E-8 if run_BatchPDLP try - PDLP_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, return_both_obj=true) + PDLP_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_solutions, objectives=PDLP_objectives, version=:original, return_both_obj=true) catch PDLP_solving_time[1] = NaN end @@ -308,7 +308,7 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP # Solve again with lower tolerances if run_BatchPDLP try - PDLP_lowres_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_lowres_solutions, objectives=PDLP_lowres_objectives, return_both_obj=true) + PDLP_lowres_solving_time[1] = @elapsed PDLP(PDLP_data, solutions=PDLP_lowres_solutions, objectives=PDLP_lowres_objectives, version=:original, return_both_obj=true) catch PDLP_lowres_solving_time[1] = NaN end @@ -393,11 +393,11 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP @sync begin @async begin device!(0) - PDLP(PDLP_data_A, solutions=PDLP_A_solutions, objectives=PDLP_A_objectives, return_both_obj=true) + PDLP(PDLP_data_A, solutions=PDLP_A_solutions, objectives=PDLP_A_objectives, version=:original, return_both_obj=true) end @async begin device!(1) - PDLP(PDLP_data_B, solutions=PDLP_B_solutions, objectives=PDLP_B_objectives, return_both_obj=true) + PDLP(PDLP_data_B, solutions=PDLP_B_solutions, objectives=PDLP_B_objectives, version=:original, return_both_obj=true) end end catch @@ -411,11 +411,11 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP @sync begin @async begin device!(0) - PDLP(PDLP_data_A, solutions=PDLP_A_solutions, objectives=PDLP_A_objectives, return_both_obj=true) + PDLP(PDLP_data_A, solutions=PDLP_A_solutions, objectives=PDLP_A_objectives, version=:original, return_both_obj=true) end @async begin device!(1) - PDLP(PDLP_data_B, solutions=PDLP_B_solutions, objectives=PDLP_B_objectives, return_both_obj=true) + PDLP(PDLP_data_B, solutions=PDLP_B_solutions, objectives=PDLP_B_objectives, version=:original, return_both_obj=true) end end end @@ -432,11 +432,11 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP @sync begin @async begin device!(0) - PDLP(PDLP_data_A, solutions=PDLP_A_solutions, objectives=PDLP_A_objectives, return_both_obj=true) + PDLP(PDLP_data_A, solutions=PDLP_A_solutions, objectives=PDLP_A_objectives, version=:original, return_both_obj=true) end @async begin device!(1) - PDLP(PDLP_data_B, solutions=PDLP_B_solutions, objectives=PDLP_B_objectives, return_both_obj=true) + PDLP(PDLP_data_B, solutions=PDLP_B_solutions, objectives=PDLP_B_objectives, version=:original, return_both_obj=true) end end end @@ -464,11 +464,11 @@ function run_example(example::LoadedProblem, n_LPs::Int, n_cuts::Int; run_BatchP @sync begin @async begin device!(0) - PDLP(PDLP_data_A, solutions=PDLP_A_lowres_solutions, objectives=PDLP_A_lowres_objectives, return_both_obj=true) + PDLP(PDLP_data_A, solutions=PDLP_A_lowres_solutions, objectives=PDLP_A_lowres_objectives, version=:original, return_both_obj=true) end @async begin device!(1) - PDLP(PDLP_data_B, solutions=PDLP_B_lowres_solutions, objectives=PDLP_B_lowres_objectives, return_both_obj=true) + PDLP(PDLP_data_B, solutions=PDLP_B_lowres_solutions, objectives=PDLP_B_lowres_objectives, version=:original, return_both_obj=true) end end end @@ -892,7 +892,7 @@ for i = 1:size(included, 1) end # Write data to a CSV - open("./benchmarking_results/rundata_$(included[i,1]).csv", "w") do io + open("./examples/benchmarks/benchmarking_results/rundata_$(included[i,1]).csv", "w") do io CSV.write(io, Tables.table(data), header=[ "BatchPDLP (1xGPU) Hi-Res - Term Status", "BatchPDLP (1xGPU) Hi-Res - Iterations", diff --git a/benchmarks/benchmarking_results/rundata_alkyl.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_alkyl.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_alkyl.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_alkyl.csv diff --git a/benchmarks/benchmarking_results/rundata_alkylation.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_alkylation.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_alkylation.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_alkylation.csv diff --git a/benchmarks/benchmarking_results/rundata_ann_compressor_exp.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ann_compressor_exp.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ann_compressor_exp.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ann_compressor_exp.csv diff --git a/benchmarks/benchmarking_results/rundata_ann_peaks_exp.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ann_peaks_exp.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ann_peaks_exp.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ann_peaks_exp.csv diff --git a/benchmarks/benchmarking_results/rundata_chakra.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chakra.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_chakra.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chakra.csv diff --git a/benchmarks/benchmarking_results/rundata_chance.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chance.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_chance.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chance.csv diff --git a/benchmarks/benchmarking_results/rundata_chem.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chem.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_chem.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chem.csv diff --git a/benchmarks/benchmarking_results/rundata_chenery.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chenery.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_chenery.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_chenery.csv diff --git a/benchmarks/benchmarking_results/rundata_eq6_1.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_eq6_1.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_eq6_1.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_eq6_1.csv diff --git a/benchmarks/benchmarking_results/rundata_etamac.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_etamac.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_etamac.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_etamac.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_1.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_1.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_1.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_1.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_2.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_2.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_2.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_2.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_3.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_3.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_3.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_3.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_4.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_4.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_4.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_4.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_5.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_5.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_5.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_5.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_7.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_7.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_7.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_7.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_8.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_8.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_8.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_8.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_1_9.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_9.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_1_9.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_1_9.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_1.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_1.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_2_1.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_1.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_2.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_2.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_2_2.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_2.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_3.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_3.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_2_3.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_3.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_4.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_4.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_2_4.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_4.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_5.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_5.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_2_5.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_5.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_6.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_6.csv similarity index 100% rename from benchmarks/benchmarking_results/rundata_ex14_2_6.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_ex14_2_6.csv diff --git a/benchmarks/benchmarking_results/rundata_ex14_2_7.csv 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rename from benchmarks/benchmarking_results/rundata_trigx.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results/rundata_trigx.csv diff --git a/benchmarks/benchmarking_results_rerun/rundata_kriging_peaks_full020.csv b/examples/benchmarks/Original Version Benchmarks/benchmarking_results_rerun/rundata_kriging_peaks_full020.csv similarity index 100% rename from benchmarks/benchmarking_results_rerun/rundata_kriging_peaks_full020.csv rename to examples/benchmarks/Original Version Benchmarks/benchmarking_results_rerun/rundata_kriging_peaks_full020.csv diff --git a/benchmarks/master_list.jl b/examples/benchmarks/Original Version Benchmarks/master_list.jl similarity index 99% rename from benchmarks/master_list.jl rename to examples/benchmarks/Original Version Benchmarks/master_list.jl index 178f7ce..e39dd40 100644 --- a/benchmarks/master_list.jl +++ b/examples/benchmarks/Original Version Benchmarks/master_list.jl @@ -257,7 +257,7 @@ included = original_included[(!).(in.(original_included[:,1], Ref(exclude_list)) for i in included[:,1] try - include("./all_examples/$i.jl") + include("./exampels/all_examples/$i.jl") catch println("$i failed") end diff --git a/src/BatchPDLP.jl b/src/BatchPDLP.jl index 954d81a..0322667 100644 --- a/src/BatchPDLP.jl +++ b/src/BatchPDLP.jl @@ -1,26 +1,19 @@ # This package contains a version of the PDLP algorithm that is designed to # solve multiple LPs simultaneously and take CuArrays as inputs. This section -# borrows extremely heavily from cuPDLP.jl: -# https://github.com/jinwen-yang/cuPDLP.jl +# borrows extremely heavily from cuPDLPx: +# https://github.com/MIT-Lu-Lab/cuPDLPx/ # -# which utilizes FirstOrderLp.jl: -# https://github.com/google-research/FirstOrderLp.jl -# -# The version implemented here is meant to be the same as cuPDLP.jl, but with +# The version implemented here is meant to be the same as cuPDLPx, but with # the lowest-level functions modified explicitly to solve multiple LPs. Additionally, # the following assumptions can be made: # 1) There are no equality constraints. Because the relaxations and subgradients # are relaxed constraints, we always have either less-than or greater-than # constraints. Equality constraints in the original problem formulation are # split into pairs of less-than and greater-than constraints. -# 2) Only the adaptive stepsize method will be used. The purpose isn't to recreate -# cuPDLP for general use; it's only being used as a subsolver. Making only -# one of the stepsize cases is simpler. -# 3) The original cuPDLP can solve quadratic programming problems, but we will only -# ever need to solve LPs. So, we can remove the `objective_matrix` in the -# cuPDLP.QuadraticProgrammingProblem and any lines of code that address that -# field. -# 4) Since we're getting relaxations of the original optimization problem, it's +# 2) cuPDLPx does bounds and objective vector rescaling in addition to Ruiz and Pock-Chambolle +# rescaling methods. Here, we do not do bounds and objective rescaling. +# +# 3) Since we're getting relaxations of the original optimization problem, it's # easiest to structure the LP using an epigraph reformulation. So, the objective # functions of the LPs being constructed will always be identical. @@ -40,6 +33,7 @@ module BatchPDLP include(joinpath(@__DIR__, "structs.jl")) include(joinpath(@__DIR__, "kernels.jl")) include(joinpath(@__DIR__, "main_loop.jl")) + include(joinpath(@__DIR__, "main_loop_rHalpern.jl")) include(joinpath(@__DIR__, "lower_level_subroutines.jl")) include(joinpath(@__DIR__, "primary_subroutines.jl")) end \ No newline at end of file diff --git a/src/kernels.jl b/src/kernels.jl index 8b7c303..0de6e41 100644 --- a/src/kernels.jl +++ b/src/kernels.jl @@ -523,7 +523,7 @@ function ruiz_variable_kernel( end # Set the value to 1.0 if the maximum was zero. - if iszero(maxval) + if maxval < 1e-12 maxval = 1.0 end @@ -543,8 +543,8 @@ function ruiz_constraint_kernel( idx = threadIdx().x + (blockIdx().x - Int32(1)) * blockDim().x stride = blockDim().x * gridDim().x - len = Int32(size(constraint_matrix, 1)) - width = Int32(size(constraint_matrix, 2)) + len = Int32(size(constraint_matrix, 1)) # number of constraints + width = Int32(size(constraint_matrix, 2)) # number of variables # For each row of the constraint matrix, get the maximum value of the absolute # values of the elements in that row, and set the result storage to the sqrt of @@ -562,7 +562,7 @@ function ruiz_constraint_kernel( maxval = max(abs(constraint_matrix[new_ID, col]), maxval) col += Int32(1) end - if iszero(maxval) + if maxval < 1e-12 maxval = 1.0 end result_storage[new_ID] = sqrt(maxval) @@ -673,7 +673,7 @@ function pock_chambolle_variable_kernel( end # Set the value to 1.0 if the result was zero. - if iszero(result) + if result < 1e-12 result = 1.0 end @@ -712,7 +712,7 @@ function pock_chambolle_constraint_kernel( result += abs(constraint_matrix[new_ID, col])^alpha col += Int32(1) end - if iszero(result) + if result < 1e-12 result = 1.0 end result_storage[new_ID] = sqrt(result) @@ -723,7 +723,7 @@ end # This kernel calculates the initial primal weight by taking the norm of the objective # vector and the right-hand side, for each LP, and using their ratio to set the -# primal weight +# primal weight as cupdlp.jl does function primal_weight_kernel( result, objective_vector, @@ -765,6 +765,52 @@ function primal_weight_kernel( return nothing end +# This kernel calculates the initial primal weight by taking the norm of the objective +# vector and the right-hand side, for each LP, and using their ratio to set the +# primal weight as cupdlpx does +function primal_weight_kernel_rHalpern( + result, + objective_vector, + right_hand_side, + n_groups, + group_length, + current_length + ) + + idx = threadIdx().x + (blockIdx().x - Int32(1)) * blockDim().x + stride = blockDim().x * gridDim().x + width = Int32(size(objective_vector, 2)) + + while idx <= n_groups + # Calculate the objective norm first + obj_norm = 0.0 + col_or_row = Int32(1) + while col_or_row <= width + obj_norm += objective_vector[idx,col_or_row]^2 + col_or_row += Int32(1) + end + + # Then calculate the right-hand side norm + rhs_norm = 0.0 + col_or_row = Int32(1) + while col_or_row <= current_length + rhs_norm += right_hand_side[(idx - Int32(1))*group_length + col_or_row]^2 + col_or_row += Int32(1) + end + + # And finally, calculate the primal importance and save it to the result + if obj_norm > 0.0 && rhs_norm > 0.0 + # result[idx] = sqrt(obj_norm)/sqrt(rhs_norm) + result[idx] = (sqrt(obj_norm) + 1) / (sqrt(rhs_norm) + 1) + else + result[idx] = 1.0 + end + + idx += stride + end + return nothing +end + # This function finds 1/norm(matrix, Inf) by scanning each row up to current_length # in each LP for the maximum. function group_max_kernel( @@ -795,4 +841,204 @@ function group_max_kernel( idx += stride end return nothing +end + +# this kernel function is for doing power iterations to estimate ||G||_2. +function group_power_kernel( + result, # [n_LPs] - output step sizes + matrix, + n_LPs, + n_vars, + nz_count, + nz_rows, + nz_cols, + active_constraint, + total_LP_length, + current_LP_length, + u_vec, # [n_LPs, n_vars] - intermediate: A·v + eigenvector_d, # [n_LPs * total_LP_length] - current eigenvector + new_eigenvector_d, # [n_LPs * total_LP_length] - next eigenvector (A·A^T·v) + iterations, + tolerance + ) + + LP = blockIdx().x + idx = threadIdx().x + + block_stride = blockDim().x + grid_stride = gridDim().x + + shared_space = @cuDynamicSharedMem(Float64, max(current_LP_length, n_vars)) + + + var_stride = Int32(1) << floor(Int32, log2(n_vars)) + len_stride = Int32(1) << floor(Int32, log2(current_LP_length)) + + while LP <= n_LPs + + # LP-specific starting position in the 1D workspace array (1-based) + active_row = (LP - Int32(1)) * total_LP_length + + iter = Int32(1) + sigma_max_sq = 1.0 + + # Power iterations + while iter <= iterations + + ######################################################### + # STEP 1: Copy and normalize guess_vector to new_vector # + ######################################################### + # Copy eigenvector_d into new_eigenvector_d + while idx <= current_LP_length + new_eigenvector_d[active_row + idx] = eigenvector_d[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + # Compute L2 norm of new_eigenvector_d: ||new_eigenvector_d||_2 + + while idx <= current_LP_length + shared_space[idx] = new_eigenvector_d[active_row + idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + + eigenvector_norm = sqrt(shared_space[1]) + inv_eigenvector_norm = 1 / eigenvector_norm + # Normalize: new_vector = new_vector / ||new_vector||_2 + + while idx <= current_LP_length + new_eigenvector_d[active_row + idx] = new_eigenvector_d[active_row + idx] * inv_eigenvector_norm + idx += block_stride + end + + idx = threadIdx().x + sync_threads() + + ############################################################################## + # STEP 2: Compute u_vec = A^T * new_eigenvector_d ([n*1] = [n*m] x [m*1]) # + ############################################################################## + + while idx <= n_vars + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), matrix[active_row + nz_rows[idx], nz_cols[idx]] * new_eigenvector_d[active_row + nz_rows[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= n_vars + u_vec[LP, idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + ################################################################################ + # STEP 3: Compute eigenvector_d = A * u_vec ([m*1] = [m*n] x [n*1]) # + ################################################################################ + + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_rows[idx]), matrix[active_row + nz_rows[idx], nz_cols[idx]] * u_vec[LP, nz_cols[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= current_LP_length + eigenvector_d[active_row + idx] = shared_space[idx] + idx += block_stride + end + + idx = threadIdx().x + sync_threads() + + ######################################################### + # STEP 4: Compute Rayleigh quotient # + # sigma_max_sq = guess_vector * next_vector # + ######################################################### + + while idx <= current_LP_length + shared_space[idx] = new_eigenvector_d[active_row + idx] * eigenvector_d[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + + sigma_max_sq = shared_space[1] + sync_threads() + + ######################################################### + # STEP 5: Compute residual for convergence check # + # residual = guess_vector - sigma_max_sq * new_vector # + ######################################################### + + while idx <= current_LP_length + shared_space[idx] = (eigenvector_d[active_row + idx] - sigma_max_sq * new_eigenvector_d[active_row + idx]) ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + sync_threads() + + residual_norm = sqrt(shared_space[1]) + + # Check convergence + if residual_norm < tolerance + break + end + + iter += Int32(1) + end + + # Compute step size: tau = 0.998 / sqrt(sigma_max_sq) + if idx == 1 + result[LP] = 0.998 / sqrt(sigma_max_sq) + + end + LP += grid_stride + end + return nothing +end + +function compute_bound_contrib_kernel( + results, + right_hand_side, + total_LP_length, +) + + idx = threadIdx().x + (blockIdx().x - Int32(1)) * blockDim().x + stride = blockDim().x * gridDim().x + + while idx <= total_LP_length + Li = right_hand_side[idx] + acc = 0.0 + + # Since there are no upper bounds, we only check if the lower bound (RHS) is finite. + if isfinite(Li) + acc += Li * Li + end + + results[idx] = acc + + # CRITICAL: Advance the index to prevent an infinite loop + idx += stride + end + + return nothing end \ No newline at end of file diff --git a/src/lower_level_subroutines.jl b/src/lower_level_subroutines.jl index fbe9d8a..6d8b7f7 100644 --- a/src/lower_level_subroutines.jl +++ b/src/lower_level_subroutines.jl @@ -2,13 +2,13 @@ # Rescaling and update functions called from `primal_subroutines.jl`. These # are generally based on cuPDLP.jl. function ruiz_rescaling( - problem::LinearProgramSet, + problem::LinearProgramSet, # problem that is being fed here is original problem technically n_iterations::Int, variable_rescaling::CuArray{Float64}, constraint_rescaling::CuArray{Float64}, dims::PDLPDims, ) - + # Identify the number of blocks to use GPU_blocks = Int32(CUDA.attribute(CUDA.device(), CUDA.DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT)) @@ -16,7 +16,7 @@ function ruiz_rescaling( temp_variable_rescaling = CuArray{Float64}(undef, size(variable_rescaling)) temp_constraint_rescaling = CuArray{Float64}(undef, size(constraint_rescaling)) - for _ in 1:n_iterations + for j in 1:n_iterations # Variable rescaling. sqrt of the maximum value of each column in each LP CUDA.@sync @cuda blocks=GPU_blocks threads=512 ruiz_variable_kernel( temp_variable_rescaling, @@ -24,16 +24,22 @@ function ruiz_rescaling( dims.current_LP_length, dims.total_LP_length ) - + # we got temp_variable_rescaling from ruiz_variable_kernel + # Constraint resscaling. sqrt of the maximum value of each row of the constraint matrix CUDA.@sync @cuda blocks=GPU_blocks threads=512 ruiz_constraint_kernel( - temp_constraint_rescaling, - problem.constraint_matrix, - dims.current_LP_length, - dims.total_LP_length + temp_constraint_rescaling, # result_storage + problem.constraint_matrix, # constraint_matrix + dims.current_LP_length, # current_LP_length + dims.total_LP_length # total_LP_length ) - - # Use the variable and constraint rescaling values to scale the problem + # we got temp_constraint_rescaling from ruiz_constraint_kernel + # println("iteration $j of Ruiz rescaling method: \n") + # println(" - temp_variable_rescaling = $temp_variable_rescaling \n") + # println(" - temp_constraint_rescaling = $temp_constraint_rescaling \n") + + + # Use the temp variable and constraint rescaling values to scale the problem scale_problem( problem, temp_variable_rescaling, @@ -44,6 +50,8 @@ function ruiz_rescaling( # Update the overall rescaling variables variable_rescaling .*= temp_variable_rescaling constraint_rescaling .*= temp_constraint_rescaling + + end # Free up temporary variables @@ -53,7 +61,7 @@ function ruiz_rescaling( end function pock_chambolle_rescaling( - problem::LinearProgramSet, + problem::LinearProgramSet, # the scaled problem from ruiz method is fed here alpha::Float64, variable_rescaling::CuArray{Float64}, constraint_rescaling::CuArray{Float64}, @@ -83,6 +91,8 @@ function pock_chambolle_rescaling( alpha, ) + # println(" - temp_variable_rescaling at pock_chambolle = $temp_variable_rescaling \n") + # println(" - temp_constraint_rescaling at pock_chambolle = $temp_constraint_rescaling \n") # Apply the rescaling values to the problem scale_problem( problem, @@ -91,6 +101,8 @@ function pock_chambolle_rescaling( dims ) + + # Update the overall rescaling variables variable_rescaling .*= temp_variable_rescaling constraint_rescaling .*= temp_constraint_rescaling @@ -144,23 +156,36 @@ end function select_initial_primal_weight( primal_weight::CuArray{Float64}, problem::LinearProgramSet, - dims::PDLPDims + dims::PDLPDims, + version::Symbol ) # Theoretically the primal importance can change, but the default in the MOI_wrapper # is to set it to 1.0. The other parameters are un-settable in cuPDLP but theoretically # could be changed as well GPU_blocks = Int32(CUDA.attribute(CUDA.device(), CUDA.DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT)) - CUDA.@sync @cuda blocks=GPU_blocks threads=512 primal_weight_kernel( - primal_weight, - problem.objective_vector, - problem.right_hand_side, - dims.n_LPs, - dims.total_LP_length, - dims.current_LP_length - ) + if version == :original + CUDA.@sync @cuda blocks=GPU_blocks threads=512 primal_weight_kernel( + primal_weight, + problem.objective_vector, + problem.right_hand_side, + dims.n_LPs, + dims.total_LP_length, + dims.current_LP_length + ) + elseif version == :rHalpern + CUDA.@sync @cuda blocks=GPU_blocks threads=512 primal_weight_kernel_rHalpern( + primal_weight, + problem.objective_vector, + problem.right_hand_side, + dims.n_LPs, + dims.total_LP_length, + dims.current_LP_length + ) + end return nothing end + function update_step_size(problem::LinearProgramSet, step_size::CuArray{Float64}, dims::PDLPDims) GPU_blocks = Int32(CUDA.attribute(CUDA.device(), CUDA.DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT)) CUDA.@sync @cuda blocks=GPU_blocks threads=512 group_max_kernel( @@ -479,3 +504,42 @@ function add_multiple_LP_lower_bound( end +function update_constant_step_size(problem::LinearProgramSet, + step_size::CuArray{Float64}, + eigenvector_d::CuArray{Float64}, + next_eigenvector_d::CuArray{Float64}, + u_vec::CuArray{Float64}, + nz_counts::Int, + nz_rows::CuArray{Int32}, + nz_cols::CuArray{Int32}, + active_constraint::CuArray{Bool}, + dims::PDLPDims) + + n_vars = dims.n_vars + max_value = max(dims.current_LP_length, n_vars) + shmem_bytes = max_value * sizeof(Float64) + + threads_per_block = 256 + iterations = Int32(5000) + + CUDA.@sync @cuda blocks=dims.n_LPs threads=threads_per_block shmem=shmem_bytes group_power_kernel( + step_size, + problem.constraint_matrix, + dims.n_LPs, + dims.n_vars, + nz_counts, + nz_rows, + nz_cols, + active_constraint, + dims.total_LP_length, + dims.current_LP_length, + u_vec, + eigenvector_d, + next_eigenvector_d, + iterations, + 1e-4, + ) + + return nothing +end + diff --git a/src/main_loop.jl b/src/main_loop.jl index d83c4bf..3d5f3e4 100644 --- a/src/main_loop.jl +++ b/src/main_loop.jl @@ -1,1423 +1,1435 @@ - -function main_loop_kernel( - solutions, # Holds solutions to LPs - objectives, # Holds dual objectives - original_variable_lower_bounds, # Used for getting unscaled convergence info - original_variable_upper_bounds, # "" - original_right_hand_side, # "" - original_objective_vector, # "" - original_objective_constant, # "" - scaled_variable_lower_bounds, # Lower bounds after initial rescaling - scaled_variable_upper_bounds, # Upper bounds after initial rescaling - scaled_constraint_matrix, # Constraint matrix after initial rescaling - scaled_right_hand_side, # Right-hand side after initial rescaling - scaled_objective_vector, # Objective vector after initial rescaling - scaled_objective_constant, # Objective constant after initial rescaling - nz_count, # Total number of nonzeros in the constraint matrix - nz_rows, # Rows containing nonzeros in the constraint matrix - nz_cols, # Columns containing nonzeros in the constraint matrix - active_constraint, # Flag to indicate whether a constraint is in use. - variable_rescaling, # Total value used for variable rescaling - constraint_rescaling, # Total value used for constraint rescaling - # input_current_primal_solution, # Usually 0s, but used for hot-starts (if desired in the future) - # input_current_dual_solution, # Usually 0s, but used for hot-starts (if desired in the future) - # input_current_primal_product, # Usually 0s, but used for hot-starts (if desired in the future) - # input_current_dual_product, # Usually 0s, but used for hot-starts (if desired in the future) - last_restart_primal_solution, # Solution state the last time a restart occurred - last_restart_primal_gradient, # "" - last_restart_dual_solution, # "" - last_restart_primal_product, # "" - current_primal_solution, # Currently active solution state - current_dual_solution, # "" - current_dual_product, # "" - current_primal_product, # "" - buffer_primal_gradient, # Generally the avg_primal_gradient, but used for some other purposes - avg_primal_solution, # Running average solution state information, kept since last restart - avg_primal_gradient, # "" - avg_dual_solution, # "" - avg_primal_product, # "" - sum_primal_solutions, # Running sum solution state information, used to calculate averages, since last restart - sum_dual_solutions, # "" - sum_primal_product, # "" - sum_dual_product, # "" - original_primal_solution, # Unscaled solution state information, used to calculate actual infeasibility - original_primal_gradient, # "" - original_dual_solution, # "" - original_primal_product, # "" - buffer_kkt_primal_solution, # Intermediate storage for calculating infeasibility - buffer_kkt_primal_product, # "" - buffer_kkt_lower_variable_violation, # Infeasible variable/constraint violation storage - buffer_kkt_upper_variable_violation, # "" - buffer_kkt_reduced_costs, # Storage used for calculating, e.g., dual objective values - delta_primal, # Value used in calculating PDLP steps - delta_primal_product, # "" - delta_dual, # "" - input_primal_weight, # The original primal weight (could be moved internally if needed) - input_step_size, # The original step size (could be moved internally if needed) - termination_reason, # Field to let the user know why the LP terminated - current_LP_length, # The current length of each individual LP (i.e., number of constraints) - total_LP_length, # The total possible size of each individual LP (i.e., max allowed number of constraints) - n_LPs, # The current number of LPs being solved (fewer than the max may be used during B&B) - n_vars, # The number of variables in the problem - iteration_limit, # The maximum number of PDLP steps allowed before termination - kkt_matrix_pass_limit, # The maximum number of KKT matrix passes (generally unused) - termination_evaluation_frequency, # Number of PDLP steps to take before checking termination criteria (default: 64) - necessary_reduction_for_restart, # KKT error decrease factor needed for a restart if no progress is being made (default: 0.8) - sufficient_reduction_for_restart, # KKT error decrease factor that would trigger a restart immediately (default: 0.2) - extrapolation_coefficient, # Value used in PDLP steps (default: 1.0) - reduction_exponent, # Step size adjustment based on step iteration count (default: 0.3) - growth_exponent, # Step size adjustment based on step iteration count (default: 0.6) - abs_tol, # Absolute tolerance for termination - rel_tol, # Relative tolerance for termination - eps_primal_infeasible, # Primal infeasibility tolerance - eps_dual_infeasible, # Dual infeasibility tolerance - return_code, # Indicator for returning primal obj (1), dual obj (2), or both (3) - global_upper_bound, # Information about the B&B upper bound (PDLP terminates if a dual feasible solution is above this value) - skip_hard_problems, # Flag to skip problems with too many iterations - global_counter, # Count of LPs being solved - iteration_counter, # Count of total iterations for solved LPs - skip_flag, # Flag to completely skip an individual LP - iterations, # The final number of iterations needed for each LP - ) - # In this kernel, assume that the number of blocks is equal to the number - # of LPs, so that all threads are working on one LP - idx = threadIdx().x - LP = blockIdx().x - block_stride = blockDim().x - grid_stride = gridDim().x - - # Calculate strides for parallel reductions as the largest power of 2 - # less than n_vars (or current_LP_length) - var_stride = Int32(1) << floor(Int32, log2(n_vars)) - len_stride = Int32(1) << floor(Int32, log2(current_LP_length)) - - # Set up dynamic shared space - shared_space = @cuDynamicSharedMem(Float64, max(n_vars, current_LP_length)) - - while LP <= n_LPs - # Check if we're supposed to skip this LP - if skip_flag[LP] - LP += grid_stride - continue - end - - # Initialize basic information for this LP - if idx==1 - cumulative_kkt_passes = 0.5 - - # Initialize temporary Float64 values needed for calculation - last_restart_primal_distance_moved = 0.0 - last_restart_dual_distance_moved = 0.0 - buffer_kkt_dual_objective = 0.0 - buffer_kkt_dual_res_inf = 0.0 - CI_primal_objective = 0.0 # Needed for optimality termination check - CI_dual_objective = 0.0 - CI_l2_primal_residual = 0.0 # Needed for optimality termination check - CI_l2_dual_residual = 0.0 - last_restart_length = 1.0 - last_reduction_ratio = 1.0 - avg_kkt_residual = 0.0 - current_kkt_residual = 0.0 - candidate_kkt_residual = 0.0 - last_kkt_residual = 0.0 - kkt_reduction_ratio = 0.0 - restart_choice = RESTART_CHOICE_NO_RESTART - end - - # Other information that every thread needs - step_iterations = Int32(0) - iteration = Int32(1) - sum_primal_solution_weights = 0.0 - sum_dual_solution_weights = 0.0 - primal_ray_norm = 0.0 - sum_primal_solutions_count = Int32(0) - sum_dual_solutions_count = Int32(0) - - # Information that is much easier to save as static shared memory - do_restart = @cuStaticSharedMem(Bool, 1) - reset_to_average = @cuStaticSharedMem(Bool, 1) - primal_weight = @cuStaticSharedMem(Float64, 1) - step_size = @cuStaticSharedMem(Float64, 1) - step_size_limit = @cuStaticSharedMem(Float64, 1) - numerical_error = @cuStaticSharedMem(Bool, 1) - if idx==1 - do_restart[1] = false - reset_to_average[1] = false - primal_weight[1] = input_primal_weight[LP] - step_size[1] = input_step_size[LP] - step_size_limit[1] = 0.0 - numerical_error[1] = false - end - - # Set up the starting row for this LP (minus 1, so that the first - # row to consider is `active_row + 1`) - active_row = (LP-Int32(1))*total_LP_length - - # Set up current values to match inputs - while idx <= n_vars - buffer_primal_gradient[LP, idx] = scaled_objective_vector[LP, idx] - idx += block_stride - end - idx = threadIdx().x - - # Set up last restart info - while idx <= n_vars - last_restart_primal_solution[LP, idx] = current_primal_solution[LP, idx] - last_restart_primal_gradient[LP, idx] = buffer_primal_gradient[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - last_restart_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] - last_restart_primal_product[active_row + idx] = current_primal_product[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - - # Set up objective vector and RHS norms - while idx <= n_vars - shared_space[idx] = original_objective_vector[LP, idx]^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - cache_l2_norm_primal_linear_objective = sqrt(shared_space[1]) - end - - while idx <= current_LP_length - shared_space[idx] = original_right_hand_side[active_row + idx]^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - cache_l2_norm_primal_right_hand_side = sqrt(shared_space[1]) - end - - # Begin the main loop - while true - # Check termination occasionally - if iteration <= 10 || mod(iteration - 1, termination_evaluation_frequency) == 0 || (iteration >= iteration_limit) - # Add to the cumulative kkt pass count - if idx==1 - cumulative_kkt_passes += 2.0 - end - - # Update the average primal/dual solutions, average primal gradient, - # and average primal product - sync_threads() - if iszero(sum_primal_solutions_count) || iszero(sum_dual_solutions_count) || unsafe_load(CUDA.pointer(numerical_error, 1)) - while idx <= n_vars - avg_primal_solution[LP, idx] = current_primal_solution[LP, idx] - avg_primal_gradient[LP, idx] = buffer_primal_gradient[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - avg_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] - avg_primal_product[active_row + idx] = current_primal_product[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - else - while idx <= n_vars - avg_primal_solution[LP, idx] = sum_primal_solutions[LP, idx] / sum_primal_solution_weights - avg_primal_gradient[LP, idx] = (-sum_dual_product[LP, idx] / sum_dual_solution_weights) + scaled_objective_vector[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - avg_dual_solution[active_row + idx] = sum_dual_solutions[active_row + idx] / sum_dual_solution_weights - avg_primal_product[active_row + idx] = sum_primal_product[active_row + idx] / sum_primal_solution_weights - idx += block_stride - end - idx = threadIdx().x - end - - - ################################################################### - ##### Begin "evaluate_unscaled_iteraiton_stats" - ################################################################### - while idx <= n_vars - original_primal_solution[LP, idx] = avg_primal_solution[LP, idx] / variable_rescaling[LP, idx] - original_primal_gradient[LP, idx] = avg_primal_gradient[LP, idx] * variable_rescaling[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - original_dual_solution[active_row + idx] = avg_dual_solution[active_row + idx] / constraint_rescaling[active_row + idx] - original_primal_product[active_row + idx] = avg_primal_product[active_row + idx] * constraint_rescaling[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - - - ################################################################### - ##### Compute Convergence Information - ################################################################### - # Compute primal variable/constraint violations - while idx <= n_vars - buffer_kkt_lower_variable_violation[LP, idx] = max(original_variable_lower_bounds[LP, idx] - original_primal_solution[LP, idx], 0.0) - buffer_kkt_upper_variable_violation[LP, idx] = max(original_primal_solution[LP, idx] - original_variable_upper_bounds[LP, idx], 0.0) - idx += block_stride - end - idx = threadIdx().x - - # Compute the primal objective and residual - while idx <= n_vars - shared_space[idx] = original_objective_vector[LP, idx] * original_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - CI_primal_objective = original_objective_constant[LP] + shared_space[1] - end - - # CI_l_inf_primal_residual - while idx <= n_vars - shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + - abs(buffer_kkt_upper_variable_violation[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - CI_l2_primal_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(original_right_hand_side[active_row + idx] - original_primal_product[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - CI_l2_primal_residual = sqrt(CI_l2_primal_residual + shared_space[1]) - end - - # Compute dual stats - while idx <= n_vars - buffer_kkt_reduced_costs[LP, idx] = max(original_primal_gradient[LP, idx], 0.0) * isfinite(original_variable_lower_bounds[LP, idx]) + - min(original_primal_gradient[LP, idx], 0.0) * isfinite(original_variable_upper_bounds[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - - # Calculate the dual objective - while idx <= n_vars - if buffer_kkt_reduced_costs[LP, idx] > 0 - shared_space[idx] = original_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 - shared_space[idx] = original_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - else - shared_space[idx] = 0.0 - end - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - CI_dual_objective = original_objective_constant[LP] + shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = original_right_hand_side[active_row + idx] * original_dual_solution[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - CI_dual_objective += shared_space[1] - end - - # Calculate the l2 dual residual - while idx <= n_vars - shared_space[idx] = abs(original_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - CI_l2_dual_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(-original_dual_solution[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - CI_l2_dual_residual = sqrt(CI_l2_dual_residual + shared_space[1]) - end - - - ################################################################### - ##### Compute Infeasibility Information - ################################################################### - while idx <= n_vars - shared_space[idx] = abs(original_primal_solution[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, var_stride, n_vars) - # Everyone needs to know the primal ray norm - primal_ray_norm = shared_space[1] - - # Calculate infeasibility primal solution and primal product - if !iszero(primal_ray_norm) - while idx <= n_vars - buffer_kkt_primal_solution[LP, idx] = original_primal_solution[LP, idx] / primal_ray_norm - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - buffer_kkt_primal_product[active_row + idx] = original_primal_product[active_row + idx] / primal_ray_norm - idx += block_stride - end - idx = threadIdx().x - else - while idx <= n_vars - buffer_kkt_primal_solution[LP, idx] = original_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - buffer_kkt_primal_product[active_row + idx] = original_primal_product[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - end - - # Compute infeasible variable/constraint violations - while idx <= n_vars - if isfinite(original_variable_lower_bounds[LP, idx]) - buffer_kkt_lower_variable_violation[LP, idx] = max(-buffer_kkt_primal_solution[LP, idx], 0.0) - else - buffer_kkt_lower_variable_violation[LP, idx] = 0.0 - end - if isfinite(original_variable_upper_bounds[LP, idx]) - buffer_kkt_upper_variable_violation[LP, idx] = max(buffer_kkt_primal_solution[LP, idx], 0.0) - else - buffer_kkt_upper_variable_violation[LP, idx] = 0.0 - end - idx += block_stride - end - idx = threadIdx().x - - # Calculate the max primal ray infeasibility - while idx <= n_vars - shared_space[idx] = max(abs(buffer_kkt_lower_variable_violation[LP, idx]), - abs(buffer_kkt_upper_variable_violation[LP, idx])) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, var_stride, n_vars) - if idx==1 - II_max_primal_ray_infeasibility = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(-buffer_kkt_primal_product[active_row + idx], 0.0)) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - II_max_primal_ray_infeasibility = max(shared_space[1], II_max_primal_ray_infeasibility) - end - - # Calculate the primal ray linear objective - while idx <= n_vars - shared_space[idx] = original_objective_vector[LP, idx] * buffer_kkt_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - II_primal_ray_linear_objective = shared_space[1] - end - - # Compute reduced costs and reduced costs violation - while idx <= n_vars - buffer_kkt_reduced_costs[LP, idx] = max(original_primal_gradient[LP, idx] - original_objective_vector[LP, idx], 0.0) * isfinite(original_variable_lower_bounds[LP, idx]) + - min(original_primal_gradient[LP, idx] - original_objective_vector[LP, idx], 0.0) * isfinite(original_variable_upper_bounds[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - - # Compute the dual residual - while idx <= n_vars - shared_space[idx] = abs(original_primal_gradient[LP, idx] - original_objective_vector[LP, idx] - buffer_kkt_reduced_costs[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, var_stride, n_vars) - if idx==1 - buffer_kkt_dual_res_inf = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(-original_dual_solution[active_row + idx], 0.0)) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - buffer_kkt_dual_res_inf = max(shared_space[1], buffer_kkt_dual_res_inf) - end - - # Compute the dual objective - while idx <= n_vars - if buffer_kkt_reduced_costs[LP, idx] > 0.0 - shared_space[idx] = original_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 - shared_space[idx] = original_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - else - shared_space[idx] = 0.0 - end - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - buffer_kkt_dual_objective = shared_space[1] + original_objective_constant[LP] - end - while idx <= current_LP_length - shared_space[idx] = original_right_hand_side[active_row + idx] * original_dual_solution[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - buffer_kkt_dual_objective += shared_space[1] - end - - # Compute infeasibility information using a scaling factor - while idx <= n_vars - shared_space[idx] = abs(buffer_kkt_reduced_costs[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, var_stride, n_vars) - if idx==1 - scaling_factor = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(original_dual_solution[active_row + idx]) - idx += block_stride - end - idx = threadIdx().x - parallel_max(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - scaling_factor = max(shared_space[1], scaling_factor) - if scaling_factor==0.0 - II_max_dual_ray_infeasibility = 0.0 - II_dual_ray_objective = 0.0 - else - II_max_dual_ray_infeasibility = buffer_kkt_dual_res_inf / scaling_factor - II_dual_ray_objective = buffer_kkt_dual_objective / scaling_factor - end - end - - - ################################################################### - ##### Set up Primal and Dual Norm Params - ################################################################### - if idx==1 - primal_norm_params = 1 / step_size[1] * primal_weight[1] - dual_norm_params = 1 / step_size[1] / primal_weight[1] - end - - - ################################################################### - ##### Check Termination Criteria - ################################################################### - # Check optimality criteria - if idx==1 - # Check if the dual objective value is above the B&B global upper bound and we - # satisfy the tolerance for dual feasibility - # (if we're past the first 10 iterations) - if (CI_dual_objective > global_upper_bound + abs_tol) && - (CI_l2_dual_residual < abs_tol + rel_tol*cache_l2_norm_primal_linear_objective) && - (iteration >= 10) - termination_reason[LP] = TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT - end - - # If we want to skip harder-than-average problems, and we've already solved at least - # 100 problems, check if the current number of iterations is over two times the running - # average - if skip_hard_problems && (unsafe_load(CUDA.pointer(global_counter, 1)) > 100) - if iteration > 2*(unsafe_load(CUDA.pointer(iteration_counter, 1))/unsafe_load(CUDA.pointer(global_counter, 1))) - termination_reason[LP] = TERMINATION_REASON_IMPATIENCE - end - end - - # Check iteration limit - if iteration >= iteration_limit - termination_reason[LP] = TERMINATION_REASON_ITERATION_LIMIT - end - - # Check KKT matrix pass limit - if cumulative_kkt_passes >= kkt_matrix_pass_limit - termination_reason[LP] = TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT - end - - # Check for numerical errors - if numerical_error[1] - termination_reason[LP] = TERMINATION_REASON_NUMERICAL_ERROR - end - - # Check if we're within the tolerances for primal and dual infeasibility, and that there's - # a sufficiently small gap between the primal and dual objective values. - if (CI_l2_dual_residual < abs_tol + rel_tol*cache_l2_norm_primal_linear_objective) && - (CI_l2_primal_residual < abs_tol + rel_tol*cache_l2_norm_primal_right_hand_side) && - (abs(CI_primal_objective - CI_dual_objective) < abs_tol + rel_tol*(abs(CI_primal_objective)+abs(CI_dual_objective))) - termination_reason[LP] = TERMINATION_REASON_OPTIMAL - end - - # Check primal infeasibility (if we're past the first 10 iterations) - if (II_dual_ray_objective > 0.0) && - ((II_max_dual_ray_infeasibility / II_dual_ray_objective) <= eps_primal_infeasible) && - (iteration >= 10) - termination_reason[LP] = TERMINATION_REASON_PRIMAL_INFEASIBLE - end - - # Check dual infeasibility (if we're past the first 10 iterations) - if (II_primal_ray_linear_objective < 0.0) && - ((II_max_primal_ray_infeasibility / (-II_primal_ray_linear_objective)) <= eps_dual_infeasible) && - (iteration >= 10) - termination_reason[LP] = TERMINATION_REASON_DUAL_INFEASIBLE - end - end - - - ################################################################### - ##### Update Solutions - ################################################################### - - # If the LP is finished, update the solutions and objective(s), and - # then break out of the iteration loop and move on to the next LP - sync_threads() - reason = unsafe_load(CUDA.pointer(termination_reason, LP)) - if reason == TERMINATION_REASON_OPTIMAL - while idx < n_vars # Intentionally skipping the epigraph variable - solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] - idx += block_stride - end - idx = threadIdx().x - if idx==1 - if return_code==Int32(1) - objectives[LP] = CI_primal_objective - elseif return_code==Int32(2) - objectives[LP] = CI_dual_objective - else - objectives[LP, Int32(1)] = CI_primal_objective - objectives[LP, Int32(2)] = CI_dual_objective - end - iterations[LP] = iteration - CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) - CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) - end - LP += grid_stride - break - elseif (reason == TERMINATION_REASON_PRIMAL_INFEASIBLE) || - (reason == TERMINATION_REASON_DUAL_INFEASIBLE) - while idx < n_vars # Intentionally skipping the epigraph variable - solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] - idx += block_stride - end - idx = threadIdx().x - if idx==1 - if return_code != Int32(3) - objectives[LP] = Inf - else - objectives[LP, Int32(1)] = Inf - objectives[LP, Int32(2)] = Inf - end - iterations[LP] = iteration - CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) - CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) - end - LP += grid_stride - break - elseif (reason == TERMINATION_REASON_ITERATION_LIMIT) || - (reason == TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT) || - (reason == TERMINATION_REASON_NUMERICAL_ERROR) - while idx < n_vars # Intentionally skipping the epigraph variable - solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] - idx += block_stride - end - idx = threadIdx().x - if idx==1 - if return_code != Int32(3) - objectives[LP] = -Inf - else - objectives[LP, Int32(1)] = -Inf - objectives[LP, Int32(2)] = -Inf - end - iterations[LP] = iteration - CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) - CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) - end - LP += grid_stride - break - elseif (reason == TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT) - while idx < n_vars - solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] - idx += block_stride - end - idx = threadIdx().x - if idx==1 - # Special case: This checks for a feasible dual objective value - # above the global upper bound in a B&B algorithm. The primal - # objective value may not be meaningful/feasible. - if return_code==Int32(1) - objectives[LP] = CI_primal_objective - elseif return_code==Int32(2) - objectives[LP] = CI_dual_objective - else - objectives[LP, Int32(1)] = CI_primal_objective - objectives[LP, Int32(2)] = CI_dual_objective - end - iterations[LP] = iteration - CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) - CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) - end - LP += grid_stride - break - elseif (reason == TERMINATION_REASON_IMPATIENCE) - while idx < n_vars - solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] - idx += block_stride - end - idx = threadIdx().x - if idx==1 - if return_code != Int32(3) - objectives[LP] = -Inf - else - objectives[LP, Int32(1)] = -Inf - objectives[LP, Int32(2)] = -Inf - end - iterations[LP] = iteration - CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) - CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) - end - LP += grid_stride - break - end - - - ################################################################### - ##### Update Buffer Primal Gradient - ################################################################### - while idx <= n_vars - buffer_primal_gradient[LP, idx] = scaled_objective_vector[LP, idx] - current_dual_product[LP, idx] - idx += block_stride - end - idx = threadIdx().x - - - ################################################################### - ##### Run the Restart Scheme - ################################################################### - if (sum_primal_solutions_count <= 0) || (sum_dual_solutions_count <= 0) - if idx==1 - restart_choice = RESTART_CHOICE_NO_RESTART - end - else - if idx==1 - # Initialize do_restart - do_restart[1] = false - - if sum_primal_solutions_count >= 0.36 * (iteration - 1) - do_restart[1] = true - end - end - - # Check CURRENT residual - - # Compute the current primal residual - while idx <= n_vars - buffer_kkt_lower_variable_violation[LP, idx] = max(scaled_variable_lower_bounds[LP, idx] - current_primal_solution[LP, idx], 0.0) - buffer_kkt_upper_variable_violation[LP, idx] = max(current_primal_solution[LP, idx] - scaled_variable_upper_bounds[LP, idx], 0.0) - idx += block_stride - end - idx = threadIdx().x - - # Compute the current primal objective and l2 primal residual - while idx <= n_vars - shared_space[idx] = scaled_objective_vector[LP, idx] * current_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - primal_objective_storage = shared_space[1] + scaled_objective_constant[LP] - end - - while idx <= n_vars - shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + - abs(buffer_kkt_upper_variable_violation[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - l2_primal_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(scaled_right_hand_side[active_row + idx] - current_primal_product[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - l2_primal_residual = sqrt(shared_space[1] + l2_primal_residual) - end - - # Compute the current dual residual - while idx <= n_vars - buffer_kkt_reduced_costs[LP, idx] = max(buffer_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_lower_bounds[LP, idx]) + - min(buffer_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_upper_bounds[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - - # Calculate the current dual objective - while idx <= n_vars - if buffer_kkt_reduced_costs[LP, idx] > 0.0 - shared_space[idx] = scaled_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 - shared_space[idx] = scaled_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - else - shared_space[idx] = 0.0 - end - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - buffer_kkt_dual_objective = scaled_objective_constant[LP] + shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = scaled_right_hand_side[active_row + idx] * current_dual_solution[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - buffer_kkt_dual_objective += shared_space[1] - end - - # Calculate the l2 dual residual - while idx <= n_vars - shared_space[idx] = abs(buffer_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - l2_dual_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(-current_dual_solution[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - l2_dual_residual += shared_space[1] - end - - # Calculate the "current" kkt residual - if idx==1 - current_kkt_residual = sqrt(primal_weight[1] * l2_primal_residual^2 + - (1/primal_weight[1]) * l2_dual_residual^2 + - abs(primal_objective_storage - buffer_kkt_dual_objective)^2) - end - - # Check AVERAGE residual - - # Compute the current primal residual - while idx <= n_vars - buffer_kkt_lower_variable_violation[LP, idx] = max(scaled_variable_lower_bounds[LP, idx] - avg_primal_solution[LP, idx], 0.0) - buffer_kkt_upper_variable_violation[LP, idx] = max(avg_primal_solution[LP, idx] - scaled_variable_upper_bounds[LP, idx], 0.0) - idx += block_stride - end - idx = threadIdx().x - - # Compute the current primal objective and l2 primal residual - while idx <= n_vars - shared_space[idx] = scaled_objective_vector[LP, idx] * avg_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - primal_objective_storage = shared_space[1] + scaled_objective_constant[LP] - end - - while idx <= n_vars - shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + - abs(buffer_kkt_upper_variable_violation[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - l2_primal_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(scaled_right_hand_side[active_row + idx] - avg_primal_product[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - l2_primal_residual = sqrt(shared_space[1] + l2_primal_residual) - end - - # Compute the current dual residual - while idx <= n_vars - buffer_kkt_reduced_costs[LP, idx] = max(avg_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_lower_bounds[LP, idx]) + - min(avg_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_upper_bounds[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - - # Calculate the current dual objective - while idx <= n_vars - if buffer_kkt_reduced_costs[LP, idx] > 0.0 - shared_space[idx] = scaled_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 - shared_space[idx] = scaled_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - else - shared_space[idx] = 0.0 - end - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - buffer_kkt_dual_objective = scaled_objective_constant[LP] + shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = scaled_right_hand_side[active_row + idx] * avg_dual_solution[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - buffer_kkt_dual_objective += shared_space[1] - end - - # Calculate the l2 dual residual - while idx <= n_vars - shared_space[idx] = abs(avg_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - l2_dual_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(-avg_dual_solution[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - l2_dual_residual += shared_space[1] - end - - # Calculate the "current" kkt residual - if idx==1 - avg_kkt_residual = sqrt(primal_weight[1] * l2_primal_residual^2 + - (1/primal_weight[1]) * l2_dual_residual^2 + - abs(primal_objective_storage - buffer_kkt_dual_objective)^2) - end - - # Determine if we should reset to average - if idx==1 - if avg_kkt_residual < current_kkt_residual - reset_to_average[1] = true - candidate_kkt_residual = avg_kkt_residual - else - reset_to_average[1] = false - candidate_kkt_residual = current_kkt_residual - end - end - - sync_threads() - if unsafe_load(CUDA.pointer(do_restart, 1))==false - # Check LAST RESTART residual - - # Compute the current primal residual - while idx <= n_vars - buffer_kkt_lower_variable_violation[LP, idx] = max(scaled_variable_lower_bounds[LP, idx] - last_restart_primal_solution[LP, idx], 0.0) - buffer_kkt_upper_variable_violation[LP, idx] = max(last_restart_primal_solution[LP, idx] - scaled_variable_upper_bounds[LP, idx], 0.0) - idx += block_stride - end - idx = threadIdx().x - - # Compute the current primal objective and l2 primal residual - while idx <= n_vars - shared_space[idx] = scaled_objective_vector[LP, idx] * last_restart_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - primal_objective_storage = scaled_objective_constant[LP] + shared_space[1] - end - - while idx <= n_vars - shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + - abs(buffer_kkt_upper_variable_violation[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - l2_primal_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(scaled_right_hand_side[active_row + idx] - last_restart_primal_product[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - l2_primal_residual = sqrt(shared_space[1] + l2_primal_residual) - end - - # Compute the current dual residual - while idx <= n_vars - buffer_kkt_reduced_costs[LP, idx] = max(last_restart_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_lower_bounds[LP, idx]) + - min(last_restart_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_upper_bounds[LP, idx]) - idx += block_stride - end - idx = threadIdx().x - - # Calculate the current dual objective - while idx <= n_vars - if buffer_kkt_reduced_costs[LP, idx] > 0.0 - shared_space[idx] = scaled_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 - shared_space[idx] = scaled_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] - else - shared_space[idx] = 0.0 - end - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - buffer_kkt_dual_objective = scaled_objective_constant[LP] + shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = scaled_right_hand_side[active_row + idx] * last_restart_dual_solution[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - buffer_kkt_dual_objective += shared_space[1] - end - - # Calculate the l2 dual residual - while idx <= n_vars - shared_space[idx] = abs(last_restart_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - l2_dual_residual = shared_space[1] - end - while idx <= current_LP_length - shared_space[idx] = abs(max(-last_restart_dual_solution[active_row + idx], 0.0))^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - l2_dual_residual += shared_space[1] - end - - # Calculate the "current" kkt residual - if idx==1 - last_kkt_residual = sqrt(primal_weight[1] * l2_primal_residual^2 + - (1/primal_weight[1]) * l2_dual_residual^2 + - abs(primal_objective_storage - buffer_kkt_dual_objective)^2) - kkt_reduction_ratio = candidate_kkt_residual / last_kkt_residual - - # Check if we need to do a restart - if kkt_reduction_ratio < necessary_reduction_for_restart - if kkt_reduction_ratio < sufficient_reduction_for_restart - do_restart[1] = true - elseif kkt_reduction_ratio > last_reduction_ratio - do_restart[1] = true - end - end - last_reduction_ratio = kkt_reduction_ratio - end - end - - - #>>>################################################################### - #>>>##### Apply the Reset (if do_restart==true) - #>>>################################################################### - sync_threads() - if unsafe_load(CUDA.pointer(do_restart, 1)) == true - if unsafe_load(CUDA.pointer(reset_to_average, 1)) == true - while idx <= n_vars - current_primal_solution[LP, idx] = avg_primal_solution[LP, idx] - current_dual_product[LP, idx] = scaled_objective_vector[LP, idx] - avg_primal_gradient[LP, idx] - buffer_primal_gradient[LP, idx] = avg_primal_gradient[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - current_dual_solution[active_row + idx] = avg_dual_solution[active_row + idx] - current_primal_product[active_row + idx] = avg_primal_product[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - end - - # Reset sum variables - sum_primal_solutions_count = Int32(0) - sum_dual_solutions_count = Int32(0) - sum_primal_solution_weights = 0.0 - sum_dual_solution_weights = 0.0 - while idx <= n_vars - sum_primal_solutions[LP, idx] = 0.0 - sum_dual_product[LP, idx] = 0.0 - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - sum_dual_solutions[active_row + idx] = 0.0 - sum_primal_product[active_row + idx] = 0.0 - idx += block_stride - end - idx = threadIdx().x - - - #>>>>>>>################################################################### - #>>>>>>>##### Update Information About the Last Restart - #>>>>>>>################################################################### - # Primal distance - while idx <= n_vars - shared_space[idx] = (avg_primal_solution[LP, idx] - last_restart_primal_solution[LP, idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - last_restart_primal_distance_moved = sqrt(primal_norm_params)*sqrt(shared_space[1])/sqrt(primal_weight[1]) - end - - # Dual distance - while idx <= current_LP_length - shared_space[idx] = (avg_dual_solution[active_row + idx] - last_restart_dual_solution[active_row + idx])^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - last_restart_dual_distance_moved = sqrt(dual_norm_params)*sqrt(shared_space[1])*sqrt(primal_weight[1]) - end - - # Primal solution - while idx <= n_vars - last_restart_primal_solution[LP, idx] = current_primal_solution[LP, idx] - idx += block_stride - end - idx = threadIdx().x - - # Dual solution - while idx <= current_LP_length - last_restart_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - - # Primal product - while idx <= current_LP_length - last_restart_primal_product[active_row + idx] = current_primal_product[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - - # Primal gradient - while idx <= n_vars - last_restart_primal_gradient[LP, idx] = buffer_primal_gradient[LP, idx] - idx += block_stride - end - idx = threadIdx().x - - if idx==1 - if reset_to_average[1]==true - restart_choice = RESTART_CHOICE_RESTART_TO_AVERAGE - else - restart_choice = RESTART_CHOICE_WEIGHTED_AVERAGE_RESET - end - end - else - if idx==1 - restart_choice = RESTART_CHOICE_NO_RESTART - end - end - end - - - ################################################################### - ##### Compute a New Primal Weight (if a restart was used) - ################################################################### - if idx==1 - if restart_choice != RESTART_CHOICE_NO_RESTART - if (last_restart_primal_distance_moved > eps()) && - (last_restart_dual_distance_moved > eps()) - primal_weight[1] = exp(0.5 * log(last_restart_dual_distance_moved / last_restart_primal_distance_moved) + - (1 - 0.5) * log(primal_weight[1])) - end - end - end - sync_threads() - end - - - ################################################################### - ##### Take n Steps - ################################################################### - if iteration < 10 - n_steps = Int32(1) - else - n_steps = Int32(min(termination_evaluation_frequency - mod(iteration, termination_evaluation_frequency) + 1, - iteration_limit - iteration)) - end - - # Store the primal weight locally in each thread - sync_threads() - local_primal_weight = unsafe_load(CUDA.pointer(primal_weight, 1)) - local_step_size = unsafe_load(CUDA.pointer(step_size, 1)) - - step = Int32(1) - while step <= n_steps - # Prepare a `done` flag for this particular step - done = false - iter = 0 - - while !done - iter += 1 - if iter > 100000 # Prevent an endless loop if no progress is made finding a step size - if idx==1 - numerical_error[1] = true - end - break - end - # Store the current step size locally in each thread - sync_threads() - local_step_size = unsafe_load(CUDA.pointer(step_size, 1)) - - # Step 1) Add one to the total iterations tracker and cumulative kkt passes - step_iterations += Int32(1) - if idx==1 - cumulative_kkt_passes += Int32(1) - end - - # Step 2) Operate on matrices to: - # A) Update delta primal - # B) Calculate the norm of delta primal - while idx <= n_vars - delta_primal[LP, idx] = min(scaled_variable_upper_bounds[LP, idx], max(scaled_variable_lower_bounds[LP, idx], current_primal_solution[LP, idx] - - (local_step_size/local_primal_weight) * (scaled_objective_vector[LP, idx] - current_dual_product[LP, idx]))) - - current_primal_solution[LP, idx] - shared_space[idx] = delta_primal[LP, idx]^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, var_stride, n_vars) - if idx==1 - norm_delta_primal_sq = shared_space[1] - end - - # Step 3) Perform the following steps for each row in the current LP: - # A) Update delta primal product using LP-segmented matrix-vector multiplication (size is (n_constraints,)) - # (delta_primal_product[row] = sum(constraint_matrix[row, :] .* delta_primal[LP, :]) - # B) Update delta dual - # C) Calculate the norm of delta_dual - # D) Compute the interaction and movement terms - - # Reset delta_primal_product and the shared space - while idx <= current_LP_length - delta_primal_product[active_row + idx] = 0.0 - shared_space[idx] = 0.0 - idx += block_stride - end - idx = threadIdx().x - sync_threads() - - # Loop over nonzeros to update delta_primal_product - while idx <= nz_count - if active_constraint[active_row + nz_rows[idx]] - CUDA.atomic_add!(CUDA.pointer(shared_space, nz_rows[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * delta_primal[LP, nz_cols[idx]]) - end - idx += block_stride - end - idx = threadIdx().x - sync_threads() - while idx <= current_LP_length - delta_primal_product[active_row + idx] += shared_space[idx] - idx += block_stride - end - idx = threadIdx().x - - # Compute other terms normally - while idx <= current_LP_length - delta_dual[active_row + idx] = max(0.0, (current_dual_solution[active_row + idx] + - (local_primal_weight * local_step_size) * (scaled_right_hand_side[active_row + idx] - - ((Int32(1) + extrapolation_coefficient) * - delta_primal_product[active_row + idx]) - - (extrapolation_coefficient * current_primal_product[active_row + idx])))) - - current_dual_solution[active_row + idx] - shared_space[idx] = delta_primal_product[active_row + idx] * delta_dual[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - interaction = abs(shared_space[1]) - end - sync_threads() - while idx <= current_LP_length - shared_space[idx] = delta_dual[active_row + idx]^2 - idx += block_stride - end - idx = threadIdx().x - parallel_sum(shared_space, block_stride, len_stride, current_LP_length) - if idx==1 - norm_delta_dual_sq = shared_space[1] - movement = 0.5 * local_primal_weight * norm_delta_primal_sq + (0.5 / local_primal_weight) * norm_delta_dual_sq - end - - # Step 4) Update the step size limit, and check for numerical errors - if idx==1 - if interaction > 0.0 - step_size_limit[1] = movement/interaction - if iszero(movement) - numerical_error[1] = true - end - else - step_size_limit[1] = Inf - end - end - - # Step 5) Update the solution (if we're done) - sync_threads() - if local_step_size <= unsafe_load(CUDA.pointer(step_size_limit, 1)) - done = true # This will be the final iteration of this step - while idx <= n_vars - current_primal_solution[LP, idx] += delta_primal[LP, idx] - idx += block_stride - end - idx = threadIdx().x - while idx <= current_LP_length - current_primal_product[active_row + idx] += delta_primal_product[active_row + idx] - current_dual_solution[active_row + idx] += delta_dual[active_row + idx] - idx += block_stride - end - idx = threadIdx().x - - # Reset current_dual_product and the shared space - while idx <= n_vars - current_dual_product[LP, idx] = 0.0 - shared_space[idx] = 0.0 - idx += block_stride - end - idx = threadIdx().x - sync_threads() - - # Loop over nonzeros to update current_dual_product - while idx <= nz_count - if active_constraint[active_row + nz_rows[idx]] - CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * current_dual_solution[active_row + nz_rows[idx]]) - end - idx += block_stride - end - idx = threadIdx().x - sync_threads() - while idx <= n_vars - current_dual_product[LP, idx] += shared_space[idx] - idx += block_stride - end - idx = threadIdx().x - sync_threads() - - # Also update solution weighted average - while idx <= n_vars - sum_primal_solutions[LP, idx] += current_primal_solution[LP, idx] * local_step_size - sum_dual_product[LP, idx] += current_dual_product[LP, idx] * local_step_size - idx += block_stride - end - idx = threadIdx().x - sum_primal_solutions_count += Int32(1) - sum_primal_solution_weights += local_step_size - while idx <= current_LP_length - sum_primal_product[active_row + idx] += current_primal_product[active_row + idx] * local_step_size - sum_dual_solutions[active_row + idx] += current_dual_solution[active_row + idx] * local_step_size - idx += block_stride - end - idx = threadIdx().x - sum_dual_solutions_count += Int32(1) - sum_dual_solution_weights += local_step_size - end - - - if idx==1 - step_size[1] = min(((1.0 - 1.0/(step_iterations + 1.0)^reduction_exponent)) * step_size_limit[1], - ((1.0 + 1.0/(step_iterations + 1.0)^growth_exponent)) * step_size[1]) - end - sync_threads() - end - - step += Int32(1) - end - iteration += n_steps - end - end - return nothing -end - -# A quick function to calculate a parallel sum over the first max_len elements in the shared space -function parallel_sum(shared, block_stride, reduction_stride, maxlen) - sync_threads() - idx = threadIdx().x - while reduction_stride > 0 - while idx <= reduction_stride && idx + reduction_stride <= maxlen - shared[idx] += shared[idx + reduction_stride] - idx += block_stride - end - idx = threadIdx().x - sync_threads() - reduction_stride >>>= 1 - end - return nothing -end - -# A quick function to calculate a parallel max over the first max_len elements in the shared space -function parallel_max(shared, block_stride, reduction_stride, maxlen) - sync_threads() - idx = threadIdx().x - while reduction_stride > 0 - while idx <= reduction_stride && idx + reduction_stride <= maxlen - shared[idx] = max(shared[idx], shared[idx + reduction_stride]) - idx += block_stride - end - idx = threadIdx().x - sync_threads() - reduction_stride >>>= 1 - end - return nothing -end \ No newline at end of file +function main_loop_original_kernel( + solutions, # Holds solutions to LPs + objectives, # Holds dual objectives + original_variable_lower_bounds, # Used for getting unscaled convergence info + original_variable_upper_bounds, # "" + original_right_hand_side, # "" + original_objective_vector, # "" + original_objective_constant, # "" + scaled_variable_lower_bounds, # Lower bounds after initial rescaling + scaled_variable_upper_bounds, # Upper bounds after initial rescaling + scaled_constraint_matrix, # Constraint matrix after initial rescaling + scaled_right_hand_side, # Right-hand side after initial rescaling + scaled_objective_vector, # Objective vector after initial rescaling + scaled_objective_constant, # Objective constant after initial rescaling + nz_count, # Total number of nonzeros in the constraint matrix + nz_rows, # Rows containing nonzeros in the constraint matrix + nz_cols, # Columns containing nonzeros in the constraint matrix + active_constraint, # Flag to indicate whether a constraint is in use. + variable_rescaling, # Total value used for variable rescaling + constraint_rescaling, # Total value used for constraint rescaling + # input_current_primal_solution, # Usually 0s, but used for hot-starts (if desired in the future) + # input_current_dual_solution, # Usually 0s, but used for hot-starts (if desired in the future) + # input_current_primal_product, # Usually 0s, but used for hot-starts (if desired in the future) + # input_current_dual_product, # Usually 0s, but used for hot-starts (if desired in the future) + last_restart_primal_solution, # Solution state the last time a restart occurred + last_restart_primal_gradient, # "" + last_restart_dual_solution, # "" + last_restart_primal_product, # "" + current_primal_solution, # Currently active solution state + current_dual_solution, # "" + current_dual_product, # "" + current_primal_product, # "" + buffer_primal_gradient, # Generally the avg_primal_gradient, but used for some other purposes + avg_primal_solution, # Running average solution state information, kept since last restart + avg_primal_gradient, # "" + avg_dual_solution, # "" + avg_primal_product, # "" + sum_primal_solutions, # Running sum solution state information, used to calculate averages, since last restart + sum_dual_solutions, # "" + sum_primal_product, # "" + sum_dual_product, # "" + original_primal_solution, # Unscaled solution state information, used to calculate actual infeasibility + original_primal_gradient, # "" + original_dual_solution, # "" + original_primal_product, # "" + buffer_kkt_primal_solution, # Intermediate storage for calculating infeasibility + buffer_kkt_primal_product, # "" + buffer_kkt_lower_variable_violation, # Infeasible variable/constraint violation storage + buffer_kkt_upper_variable_violation, # "" + buffer_kkt_reduced_costs, # Storage used for calculating, e.g., dual objective values + delta_primal, # Value used in calculating PDLP steps + delta_primal_product, # "" + delta_dual, # "" + input_primal_weight, # The original primal weight (could be moved internally if needed) + input_step_size, # The original step size (could be moved internally if needed) + termination_reason, # Field to let the user know why the LP terminated + current_LP_length, # The current length of each individual LP (i.e., number of constraints) + total_LP_length, # The total possible size of each individual LP (i.e., max allowed number of constraints) + n_LPs, # The current number of LPs being solved (fewer than the max may be used during B&B) + n_vars, # The number of variables in the problem + iteration_limit, # The maximum number of PDLP steps allowed before termination + kkt_matrix_pass_limit, # The maximum number of KKT matrix passes (generally unused) + termination_evaluation_frequency, # Number of PDLP steps to take before checking termination criteria (default: 64) + necessary_reduction_for_restart, # KKT error decrease factor needed for a restart if no progress is being made (default: 0.8) + sufficient_reduction_for_restart, # KKT error decrease factor that would trigger a restart immediately (default: 0.2) + extrapolation_coefficient, # Value used in PDLP steps (default: 1.0) + reduction_exponent, # Step size adjustment based on step iteration count (default: 0.3) + growth_exponent, # Step size adjustment based on step iteration count (default: 0.6) + abs_tol, # Absolute tolerance for termination + rel_tol, # Relative tolerance for termination + eps_primal_infeasible, # Primal infeasibility tolerance + eps_dual_infeasible, # Dual infeasibility tolerance + return_code, # Indicator for returning primal obj (1), dual obj (2), or both (3) + global_upper_bound, # Information about the B&B upper bound (PDLP terminates if a dual feasible solution is above this value) + skip_hard_problems, # Flag to skip problems with too many iterations + global_counter, # Count of LPs being solved + iteration_counter, # Count of total iterations for solved LPs + skip_flag, # Flag to completely skip an individual LP + iterations, # The final number of iterations needed for each LP + ) + # In this kernel, assume that the number of blocks is equal to the number + # of LPs, so that all threads are working on one LP + idx = threadIdx().x + LP = blockIdx().x + block_stride = blockDim().x + grid_stride = gridDim().x + + # Calculate strides for parallel reductions as the largest power of 2 + # less than n_vars (or current_LP_length) + var_stride = Int32(1) << floor(Int32, log2(n_vars)) + len_stride = Int32(1) << floor(Int32, log2(current_LP_length)) + + # Set up dynamic shared space + shared_space = @cuDynamicSharedMem(Float64, max(n_vars, current_LP_length)) + + while LP <= n_LPs + # Check if we're supposed to skip this LP + if skip_flag[LP] + LP += grid_stride + continue + end + + # Initialize basic information for this LP + if idx==1 + cumulative_kkt_passes = 0.5 + + # Initialize temporary Float64 values needed for calculation + last_restart_primal_distance_moved = 0.0 + last_restart_dual_distance_moved = 0.0 + buffer_kkt_dual_objective = 0.0 + buffer_kkt_dual_res_inf = 0.0 + CI_primal_objective = 0.0 # Needed for optimality termination check + CI_dual_objective = 0.0 + CI_l2_primal_residual = 0.0 # Needed for optimality termination check + CI_l2_dual_residual = 0.0 + last_restart_length = 1.0 + last_reduction_ratio = 1.0 + avg_kkt_residual = 0.0 + current_kkt_residual = 0.0 + candidate_kkt_residual = 0.0 + last_kkt_residual = 0.0 + kkt_reduction_ratio = 0.0 + restart_choice = RESTART_CHOICE_NO_RESTART + end + + # Other information that every thread needs + step_iterations = Int32(0) + iteration = Int32(1) + sum_primal_solution_weights = 0.0 + sum_dual_solution_weights = 0.0 + primal_ray_norm = 0.0 + sum_primal_solutions_count = Int32(0) + sum_dual_solutions_count = Int32(0) + + # Information that is much easier to save as static shared memory + do_restart = @cuStaticSharedMem(Bool, 1) + reset_to_average = @cuStaticSharedMem(Bool, 1) + primal_weight = @cuStaticSharedMem(Float64, 1) + step_size = @cuStaticSharedMem(Float64, 1) + step_size_limit = @cuStaticSharedMem(Float64, 1) + numerical_error = @cuStaticSharedMem(Bool, 1) + if idx==1 + do_restart[1] = false + reset_to_average[1] = false + primal_weight[1] = input_primal_weight[LP] + step_size[1] = input_step_size[LP] + step_size_limit[1] = 0.0 + numerical_error[1] = false + end + + # Set up the starting row for this LP (minus 1, so that the first + # row to consider is `active_row + 1`) + active_row = (LP-Int32(1))*total_LP_length + + # Set up current values to match inputs + while idx <= n_vars + buffer_primal_gradient[LP, idx] = scaled_objective_vector[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # Set up last restart info + while idx <= n_vars + last_restart_primal_solution[LP, idx] = current_primal_solution[LP, idx] + last_restart_primal_gradient[LP, idx] = buffer_primal_gradient[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + last_restart_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] + last_restart_primal_product[active_row + idx] = current_primal_product[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # Set up objective vector and RHS norms + while idx <= n_vars + shared_space[idx] = original_objective_vector[LP, idx]^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + cache_l2_norm_primal_linear_objective = sqrt(shared_space[1]) + end + + while idx <= current_LP_length + shared_space[idx] = original_right_hand_side[active_row + idx]^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + cache_l2_norm_primal_right_hand_side = sqrt(shared_space[1]) + end + + # Begin the main loop + while true + # Check termination occasionally + if iteration <= 10 || mod(iteration - 1, termination_evaluation_frequency) == 0 || (iteration >= iteration_limit) + # Add to the cumulative kkt pass count + if idx==1 + cumulative_kkt_passes += 2.0 + end + + # Update the average primal/dual solutions, average primal gradient, + # and average primal product + sync_threads() + if iszero(sum_primal_solutions_count) || iszero(sum_dual_solutions_count) || unsafe_load(CUDA.pointer(numerical_error, 1)) + while idx <= n_vars + avg_primal_solution[LP, idx] = current_primal_solution[LP, idx] + avg_primal_gradient[LP, idx] = buffer_primal_gradient[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + avg_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] + avg_primal_product[active_row + idx] = current_primal_product[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + else + while idx <= n_vars + avg_primal_solution[LP, idx] = sum_primal_solutions[LP, idx] / sum_primal_solution_weights + avg_primal_gradient[LP, idx] = (-sum_dual_product[LP, idx] / sum_dual_solution_weights) + scaled_objective_vector[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + avg_dual_solution[active_row + idx] = sum_dual_solutions[active_row + idx] / sum_dual_solution_weights + avg_primal_product[active_row + idx] = sum_primal_product[active_row + idx] / sum_primal_solution_weights + idx += block_stride + end + idx = threadIdx().x + end + + + ################################################################### + ##### Begin "evaluate_unscaled_iteraiton_stats" + ################################################################### + while idx <= n_vars + original_primal_solution[LP, idx] = avg_primal_solution[LP, idx] / variable_rescaling[LP, idx] + original_primal_gradient[LP, idx] = avg_primal_gradient[LP, idx] * variable_rescaling[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + original_dual_solution[active_row + idx] = avg_dual_solution[active_row + idx] / constraint_rescaling[active_row + idx] + original_primal_product[active_row + idx] = avg_primal_product[active_row + idx] * constraint_rescaling[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + + ################################################################### + ##### Compute Convergence Information + ################################################################### + # Compute primal variable/constraint violations + while idx <= n_vars + buffer_kkt_lower_variable_violation[LP, idx] = max(original_variable_lower_bounds[LP, idx] - original_primal_solution[LP, idx], 0.0) + buffer_kkt_upper_variable_violation[LP, idx] = max(original_primal_solution[LP, idx] - original_variable_upper_bounds[LP, idx], 0.0) + idx += block_stride + end + idx = threadIdx().x + + # Compute the primal objective and residual + while idx <= n_vars + shared_space[idx] = original_objective_vector[LP, idx] * original_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + CI_primal_objective = original_objective_constant[LP] + shared_space[1] + end + + # CI_l_inf_primal_residual + while idx <= n_vars + shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + + abs(buffer_kkt_upper_variable_violation[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + CI_l2_primal_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(original_right_hand_side[active_row + idx] - original_primal_product[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + CI_l2_primal_residual = sqrt(CI_l2_primal_residual + shared_space[1]) + end + + # Compute dual stats + while idx <= n_vars + buffer_kkt_reduced_costs[LP, idx] = max(original_primal_gradient[LP, idx], 0.0) * isfinite(original_variable_lower_bounds[LP, idx]) + + min(original_primal_gradient[LP, idx], 0.0) * isfinite(original_variable_upper_bounds[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + + # Calculate the dual objective + while idx <= n_vars + if buffer_kkt_reduced_costs[LP, idx] > 0 + shared_space[idx] = original_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 + shared_space[idx] = original_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + else + shared_space[idx] = 0.0 + end + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + CI_dual_objective = original_objective_constant[LP] + shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = original_right_hand_side[active_row + idx] * original_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + CI_dual_objective += shared_space[1] + end + + # Calculate the l2 dual residual + while idx <= n_vars + shared_space[idx] = abs(original_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + CI_l2_dual_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(-original_dual_solution[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + CI_l2_dual_residual = sqrt(CI_l2_dual_residual + shared_space[1]) + end + + + ################################################################### + ##### Compute Infeasibility Information + ################################################################### + while idx <= n_vars + shared_space[idx] = abs(original_primal_solution[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, var_stride, n_vars) + # Everyone needs to know the primal ray norm + primal_ray_norm = shared_space[1] + + # Calculate infeasibility primal solution and primal product + if !iszero(primal_ray_norm) + while idx <= n_vars + buffer_kkt_primal_solution[LP, idx] = original_primal_solution[LP, idx] / primal_ray_norm + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + buffer_kkt_primal_product[active_row + idx] = original_primal_product[active_row + idx] / primal_ray_norm + idx += block_stride + end + idx = threadIdx().x + else + while idx <= n_vars + buffer_kkt_primal_solution[LP, idx] = original_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + buffer_kkt_primal_product[active_row + idx] = original_primal_product[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + end + + # Compute infeasible variable/constraint violations + while idx <= n_vars + if isfinite(original_variable_lower_bounds[LP, idx]) + buffer_kkt_lower_variable_violation[LP, idx] = max(-buffer_kkt_primal_solution[LP, idx], 0.0) + else + buffer_kkt_lower_variable_violation[LP, idx] = 0.0 + end + if isfinite(original_variable_upper_bounds[LP, idx]) + buffer_kkt_upper_variable_violation[LP, idx] = max(buffer_kkt_primal_solution[LP, idx], 0.0) + else + buffer_kkt_upper_variable_violation[LP, idx] = 0.0 + end + idx += block_stride + end + idx = threadIdx().x + + # Calculate the max primal ray infeasibility + while idx <= n_vars + shared_space[idx] = max(abs(buffer_kkt_lower_variable_violation[LP, idx]), + abs(buffer_kkt_upper_variable_violation[LP, idx])) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, var_stride, n_vars) + if idx==1 + II_max_primal_ray_infeasibility = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(-buffer_kkt_primal_product[active_row + idx], 0.0)) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + II_max_primal_ray_infeasibility = max(shared_space[1], II_max_primal_ray_infeasibility) + end + + # Calculate the primal ray linear objective + while idx <= n_vars + shared_space[idx] = original_objective_vector[LP, idx] * buffer_kkt_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + II_primal_ray_linear_objective = shared_space[1] + end + + # Compute reduced costs and reduced costs violation + while idx <= n_vars + buffer_kkt_reduced_costs[LP, idx] = max(original_primal_gradient[LP, idx] - original_objective_vector[LP, idx], 0.0) * isfinite(original_variable_lower_bounds[LP, idx]) + + min(original_primal_gradient[LP, idx] - original_objective_vector[LP, idx], 0.0) * isfinite(original_variable_upper_bounds[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + + # Compute the dual residual + while idx <= n_vars + shared_space[idx] = abs(original_primal_gradient[LP, idx] - original_objective_vector[LP, idx] - buffer_kkt_reduced_costs[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, var_stride, n_vars) + if idx==1 + buffer_kkt_dual_res_inf = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(-original_dual_solution[active_row + idx], 0.0)) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + buffer_kkt_dual_res_inf = max(shared_space[1], buffer_kkt_dual_res_inf) + end + + # Compute the dual objective + while idx <= n_vars + if buffer_kkt_reduced_costs[LP, idx] > 0.0 + shared_space[idx] = original_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 + shared_space[idx] = original_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + else + shared_space[idx] = 0.0 + end + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + buffer_kkt_dual_objective = shared_space[1] + original_objective_constant[LP] + end + while idx <= current_LP_length + shared_space[idx] = original_right_hand_side[active_row + idx] * original_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + buffer_kkt_dual_objective += shared_space[1] + end + + # Compute infeasibility information using a scaling factor + while idx <= n_vars + shared_space[idx] = abs(buffer_kkt_reduced_costs[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, var_stride, n_vars) + if idx==1 + scaling_factor = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(original_dual_solution[active_row + idx]) + idx += block_stride + end + idx = threadIdx().x + parallel_max(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + scaling_factor = max(shared_space[1], scaling_factor) + if scaling_factor==0.0 + II_max_dual_ray_infeasibility = 0.0 + II_dual_ray_objective = 0.0 + else + II_max_dual_ray_infeasibility = buffer_kkt_dual_res_inf / scaling_factor + II_dual_ray_objective = buffer_kkt_dual_objective / scaling_factor + end + end + + + ################################################################### + ##### Set up Primal and Dual Norm Params + ################################################################### + if idx==1 + primal_norm_params = 1 / step_size[1] * primal_weight[1] + dual_norm_params = 1 / step_size[1] / primal_weight[1] + end + + + ################################################################### + ##### Check Termination Criteria + ################################################################### + # Check optimality criteria + if idx==1 + # Check if the dual objective value is above the B&B global upper bound and we + # satisfy the tolerance for dual feasibility + # (if we're past the first 10 iterations) + if (CI_dual_objective > global_upper_bound + abs_tol) && + (CI_l2_dual_residual < abs_tol + rel_tol*cache_l2_norm_primal_linear_objective) && + (iteration >= 10) + termination_reason[LP] = TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT + end + + # If we want to skip harder-than-average problems, and we've already solved at least + # 100 problems, check if the current number of iterations is over two times the running + # average + if skip_hard_problems && (unsafe_load(CUDA.pointer(global_counter, 1)) > 100) + if iteration > 2*(unsafe_load(CUDA.pointer(iteration_counter, 1))/unsafe_load(CUDA.pointer(global_counter, 1))) + termination_reason[LP] = TERMINATION_REASON_IMPATIENCE + end + end + + # Check iteration limit + if iteration >= iteration_limit + termination_reason[LP] = TERMINATION_REASON_ITERATION_LIMIT + end + + # Check KKT matrix pass limit + if cumulative_kkt_passes >= kkt_matrix_pass_limit + termination_reason[LP] = TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT + end + + # Check for numerical errors + if numerical_error[1] + termination_reason[LP] = TERMINATION_REASON_NUMERICAL_ERROR + end + + # Check if we're within the tolerances for primal and dual infeasibility, and that there's + # a sufficiently small gap between the primal and dual objective values. + if (CI_l2_dual_residual < abs_tol + rel_tol*cache_l2_norm_primal_linear_objective) && + (CI_l2_primal_residual < abs_tol + rel_tol*cache_l2_norm_primal_right_hand_side) && + (abs(CI_primal_objective - CI_dual_objective) < abs_tol + rel_tol*(abs(CI_primal_objective)+abs(CI_dual_objective))) + termination_reason[LP] = TERMINATION_REASON_OPTIMAL + end + + # Check primal infeasibility (if we're past the first 10 iterations) + if (II_dual_ray_objective > 0.0) && + ((II_max_dual_ray_infeasibility / II_dual_ray_objective) <= eps_primal_infeasible) && + (iteration >= 10) + termination_reason[LP] = TERMINATION_REASON_PRIMAL_INFEASIBLE + end + + # Check dual infeasibility (if we're past the first 10 iterations) + if (II_primal_ray_linear_objective < 0.0) && + ((II_max_primal_ray_infeasibility / (-II_primal_ray_linear_objective)) <= eps_dual_infeasible) && + (iteration >= 10) + termination_reason[LP] = TERMINATION_REASON_DUAL_INFEASIBLE + end + end + + + ################################################################### + ##### Update Solutions + ################################################################### + + # If the LP is finished, update the solutions and objective(s), and + # then break out of the iteration loop and move on to the next LP + sync_threads() + reason = unsafe_load(CUDA.pointer(termination_reason, LP)) + if reason == TERMINATION_REASON_OPTIMAL + while idx < n_vars # Intentionally skipping the epigraph variable + solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code==Int32(1) + objectives[LP] = CI_primal_objective + elseif return_code==Int32(2) + objectives[LP] = CI_dual_objective + else + objectives[LP, Int32(1)] = CI_primal_objective + objectives[LP, Int32(2)] = CI_dual_objective + end + iterations[LP] = iteration + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) + end + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_PRIMAL_INFEASIBLE) || + (reason == TERMINATION_REASON_DUAL_INFEASIBLE) + while idx < n_vars # Intentionally skipping the epigraph variable + solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code != Int32(3) + objectives[LP] = Inf + else + objectives[LP, Int32(1)] = Inf + objectives[LP, Int32(2)] = Inf + end + iterations[LP] = iteration + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) + end + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_ITERATION_LIMIT) || + (reason == TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT) || + (reason == TERMINATION_REASON_NUMERICAL_ERROR) + while idx < n_vars # Intentionally skipping the epigraph variable + solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code != Int32(3) + objectives[LP] = -Inf + else + objectives[LP, Int32(1)] = -Inf + objectives[LP, Int32(2)] = -Inf + end + iterations[LP] = iteration + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) + end + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT) + while idx < n_vars + solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + # Special case: This checks for a feasible dual objective value + # above the global upper bound in a B&B algorithm. The primal + # objective value may not be meaningful/feasible. + if return_code==Int32(1) + objectives[LP] = CI_primal_objective + elseif return_code==Int32(2) + objectives[LP] = CI_dual_objective + else + objectives[LP, Int32(1)] = CI_primal_objective + objectives[LP, Int32(2)] = CI_dual_objective + end + iterations[LP] = iteration + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) + end + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_IMPATIENCE) + while idx < n_vars + solutions[LP, idx] = avg_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code != Int32(3) + objectives[LP] = -Inf + else + objectives[LP, Int32(1)] = -Inf + objectives[LP, Int32(2)] = -Inf + end + iterations[LP] = iteration + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(iteration)) + end + LP += grid_stride + break + end + + + ################################################################### + ##### Update Buffer Primal Gradient + ################################################################### + while idx <= n_vars + buffer_primal_gradient[LP, idx] = scaled_objective_vector[LP, idx] - current_dual_product[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + + ################################################################### + ##### Run the Restart Scheme + ################################################################### + if (sum_primal_solutions_count <= 0) || (sum_dual_solutions_count <= 0) + if idx==1 + restart_choice = RESTART_CHOICE_NO_RESTART + end + else + if idx==1 + # Initialize do_restart + do_restart[1] = false + + if sum_primal_solutions_count >= 0.36 * (iteration - 1) + do_restart[1] = true + end + end + + # Check CURRENT residual + + # Compute the current primal residual + while idx <= n_vars + buffer_kkt_lower_variable_violation[LP, idx] = max(scaled_variable_lower_bounds[LP, idx] - current_primal_solution[LP, idx], 0.0) + buffer_kkt_upper_variable_violation[LP, idx] = max(current_primal_solution[LP, idx] - scaled_variable_upper_bounds[LP, idx], 0.0) + idx += block_stride + end + idx = threadIdx().x + + # Compute the current primal objective and l2 primal residual + while idx <= n_vars + shared_space[idx] = scaled_objective_vector[LP, idx] * current_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + primal_objective_storage = shared_space[1] + scaled_objective_constant[LP] + end + + while idx <= n_vars + shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + + abs(buffer_kkt_upper_variable_violation[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + l2_primal_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(scaled_right_hand_side[active_row + idx] - current_primal_product[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + l2_primal_residual = sqrt(shared_space[1] + l2_primal_residual) + end + + # Compute the current dual residual + while idx <= n_vars + buffer_kkt_reduced_costs[LP, idx] = max(buffer_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_lower_bounds[LP, idx]) + + min(buffer_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_upper_bounds[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + + # Calculate the current dual objective + while idx <= n_vars + if buffer_kkt_reduced_costs[LP, idx] > 0.0 + shared_space[idx] = scaled_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 + shared_space[idx] = scaled_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + else + shared_space[idx] = 0.0 + end + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + buffer_kkt_dual_objective = scaled_objective_constant[LP] + shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = scaled_right_hand_side[active_row + idx] * current_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + buffer_kkt_dual_objective += shared_space[1] + end + + # Calculate the l2 dual residual + while idx <= n_vars + shared_space[idx] = abs(buffer_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + l2_dual_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(-current_dual_solution[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + l2_dual_residual += shared_space[1] + end + + # Calculate the "current" kkt residual + if idx==1 + current_kkt_residual = sqrt(primal_weight[1] * l2_primal_residual^2 + + (1/primal_weight[1]) * l2_dual_residual^2 + + abs(primal_objective_storage - buffer_kkt_dual_objective)^2) + end + + # Check AVERAGE residual + + # Compute the current primal residual + while idx <= n_vars + buffer_kkt_lower_variable_violation[LP, idx] = max(scaled_variable_lower_bounds[LP, idx] - avg_primal_solution[LP, idx], 0.0) + buffer_kkt_upper_variable_violation[LP, idx] = max(avg_primal_solution[LP, idx] - scaled_variable_upper_bounds[LP, idx], 0.0) + idx += block_stride + end + idx = threadIdx().x + + # Compute the current primal objective and l2 primal residual + while idx <= n_vars + shared_space[idx] = scaled_objective_vector[LP, idx] * avg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + primal_objective_storage = shared_space[1] + scaled_objective_constant[LP] + end + + while idx <= n_vars + shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + + abs(buffer_kkt_upper_variable_violation[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + l2_primal_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(scaled_right_hand_side[active_row + idx] - avg_primal_product[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + l2_primal_residual = sqrt(shared_space[1] + l2_primal_residual) + end + + # Compute the current dual residual + while idx <= n_vars + buffer_kkt_reduced_costs[LP, idx] = max(avg_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_lower_bounds[LP, idx]) + + min(avg_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_upper_bounds[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + + # Calculate the current dual objective + while idx <= n_vars + if buffer_kkt_reduced_costs[LP, idx] > 0.0 + shared_space[idx] = scaled_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 + shared_space[idx] = scaled_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + else + shared_space[idx] = 0.0 + end + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + buffer_kkt_dual_objective = scaled_objective_constant[LP] + shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = scaled_right_hand_side[active_row + idx] * avg_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + buffer_kkt_dual_objective += shared_space[1] + end + + # Calculate the l2 dual residual + while idx <= n_vars + shared_space[idx] = abs(avg_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + l2_dual_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(-avg_dual_solution[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + l2_dual_residual += shared_space[1] + end + + # Calculate the "current" kkt residual + if idx==1 + avg_kkt_residual = sqrt(primal_weight[1] * l2_primal_residual^2 + + (1/primal_weight[1]) * l2_dual_residual^2 + + abs(primal_objective_storage - buffer_kkt_dual_objective)^2) + end + + # Determine if we should reset to average + if idx==1 + if avg_kkt_residual < current_kkt_residual + reset_to_average[1] = true + candidate_kkt_residual = avg_kkt_residual + else + reset_to_average[1] = false + candidate_kkt_residual = current_kkt_residual + end + end + + sync_threads() + if unsafe_load(CUDA.pointer(do_restart, 1))==false + # Check LAST RESTART residual + + # Compute the current primal residual + while idx <= n_vars + buffer_kkt_lower_variable_violation[LP, idx] = max(scaled_variable_lower_bounds[LP, idx] - last_restart_primal_solution[LP, idx], 0.0) + buffer_kkt_upper_variable_violation[LP, idx] = max(last_restart_primal_solution[LP, idx] - scaled_variable_upper_bounds[LP, idx], 0.0) + idx += block_stride + end + idx = threadIdx().x + + # Compute the current primal objective and l2 primal residual + while idx <= n_vars + shared_space[idx] = scaled_objective_vector[LP, idx] * last_restart_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + primal_objective_storage = scaled_objective_constant[LP] + shared_space[1] + end + + while idx <= n_vars + shared_space[idx] = abs(buffer_kkt_lower_variable_violation[LP, idx])^2 + + abs(buffer_kkt_upper_variable_violation[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + l2_primal_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(scaled_right_hand_side[active_row + idx] - last_restart_primal_product[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + l2_primal_residual = sqrt(shared_space[1] + l2_primal_residual) + end + + # Compute the current dual residual + while idx <= n_vars + buffer_kkt_reduced_costs[LP, idx] = max(last_restart_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_lower_bounds[LP, idx]) + + min(last_restart_primal_gradient[LP, idx], 0.0) * isfinite(scaled_variable_upper_bounds[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + + # Calculate the current dual objective + while idx <= n_vars + if buffer_kkt_reduced_costs[LP, idx] > 0.0 + shared_space[idx] = scaled_variable_lower_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + elseif buffer_kkt_reduced_costs[LP, idx] < 0.0 + shared_space[idx] = scaled_variable_upper_bounds[LP, idx] * buffer_kkt_reduced_costs[LP, idx] + else + shared_space[idx] = 0.0 + end + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + buffer_kkt_dual_objective = scaled_objective_constant[LP] + shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = scaled_right_hand_side[active_row + idx] * last_restart_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + buffer_kkt_dual_objective += shared_space[1] + end + + # Calculate the l2 dual residual + while idx <= n_vars + shared_space[idx] = abs(last_restart_primal_gradient[LP, idx] - buffer_kkt_reduced_costs[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + l2_dual_residual = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = abs(max(-last_restart_dual_solution[active_row + idx], 0.0))^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + l2_dual_residual += shared_space[1] + end + + # Calculate the "current" kkt residual + if idx==1 + last_kkt_residual = sqrt(primal_weight[1] * l2_primal_residual^2 + + (1/primal_weight[1]) * l2_dual_residual^2 + + abs(primal_objective_storage - buffer_kkt_dual_objective)^2) + kkt_reduction_ratio = candidate_kkt_residual / last_kkt_residual + + # Check if we need to do a restart + if kkt_reduction_ratio < necessary_reduction_for_restart + if kkt_reduction_ratio < sufficient_reduction_for_restart + do_restart[1] = true + elseif kkt_reduction_ratio > last_reduction_ratio + do_restart[1] = true + end + end + last_reduction_ratio = kkt_reduction_ratio + end + end + + + #>>>################################################################### + #>>>##### Apply the Reset (if do_restart==true) + #>>>################################################################### + sync_threads() + if unsafe_load(CUDA.pointer(do_restart, 1)) == true + if unsafe_load(CUDA.pointer(reset_to_average, 1)) == true + while idx <= n_vars + current_primal_solution[LP, idx] = avg_primal_solution[LP, idx] + current_dual_product[LP, idx] = scaled_objective_vector[LP, idx] - avg_primal_gradient[LP, idx] + buffer_primal_gradient[LP, idx] = avg_primal_gradient[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + current_dual_solution[active_row + idx] = avg_dual_solution[active_row + idx] + current_primal_product[active_row + idx] = avg_primal_product[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + end + + # Reset sum variables + sum_primal_solutions_count = Int32(0) + sum_dual_solutions_count = Int32(0) + sum_primal_solution_weights = 0.0 + sum_dual_solution_weights = 0.0 + while idx <= n_vars + sum_primal_solutions[LP, idx] = 0.0 + sum_dual_product[LP, idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + sum_dual_solutions[active_row + idx] = 0.0 + sum_primal_product[active_row + idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + + + #>>>>>>>################################################################### + #>>>>>>>##### Update Information About the Last Restart + #>>>>>>>################################################################### + # Primal distance + while idx <= n_vars + shared_space[idx] = (avg_primal_solution[LP, idx] - last_restart_primal_solution[LP, idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + last_restart_primal_distance_moved = sqrt(primal_norm_params)*sqrt(shared_space[1])/sqrt(primal_weight[1]) + end + + # Dual distance + while idx <= current_LP_length + shared_space[idx] = (avg_dual_solution[active_row + idx] - last_restart_dual_solution[active_row + idx])^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx==1 + last_restart_dual_distance_moved = sqrt(dual_norm_params)*sqrt(shared_space[1])*sqrt(primal_weight[1]) + end + + # Primal solution + while idx <= n_vars + last_restart_primal_solution[LP, idx] = current_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # Dual solution + while idx <= current_LP_length + last_restart_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # Primal product + while idx <= current_LP_length + last_restart_primal_product[active_row + idx] = current_primal_product[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # Primal gradient + while idx <= n_vars + last_restart_primal_gradient[LP, idx] = buffer_primal_gradient[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + if idx==1 + if reset_to_average[1]==true + restart_choice = RESTART_CHOICE_RESTART_TO_AVERAGE + else + restart_choice = RESTART_CHOICE_WEIGHTED_AVERAGE_RESET + end + end + else + if idx==1 + restart_choice = RESTART_CHOICE_NO_RESTART + end + end + end + + + ################################################################### + ##### Compute a New Primal Weight (if a restart was used) + ################################################################### + if idx==1 + if restart_choice != RESTART_CHOICE_NO_RESTART + if (last_restart_primal_distance_moved > eps()) && + (last_restart_dual_distance_moved > eps()) + primal_weight[1] = exp(0.5 * log(last_restart_dual_distance_moved / last_restart_primal_distance_moved) + + (1 - 0.5) * log(primal_weight[1])) + end + end + end + sync_threads() + end + + + ################################################################### + ##### Take n Steps + ################################################################### + if iteration < 10 + n_steps = Int32(1) + else + n_steps = Int32(min(termination_evaluation_frequency - mod(iteration, termination_evaluation_frequency) + 1, + iteration_limit - iteration)) + end + + # Store the primal weight locally in each thread + sync_threads() + local_primal_weight = unsafe_load(CUDA.pointer(primal_weight, 1)) + local_step_size = unsafe_load(CUDA.pointer(step_size, 1)) + + step = Int32(0) + while step < n_steps + + #idx==1 && CUDA.@cuprintln("Reached 4: just got into inner loop where epoch is $(epoch_iterations)") + ITERATION_COUNT += 1 + # idx==1 && CUDA.@cuprintln(">>>>>>>>>>>>ITERATION $ITERATION_COUNT HAS BEGUN<<<<<<<<<<<<<<<< \n") + + # idx==1 && CUDA.@cuprintln("\nINITIAL VALUES") + # idx==1 && CUDA.@cuprintln("Initial primal solution: [$(initial_primal_solution[1,1]), $(initial_primal_solution[1,2])]") + # idx==1 && CUDA.@cuprintln("Current primal solution: [$(current_primal_solution[1,1]), $(current_primal_solution[1,2])]") + # idx==1 && CUDA.@cuprintln("Current primal product: [$(current_primal_product[1]), $(current_primal_product[2]), $(current_primal_product[3]), $(current_primal_product[4]), $(current_primal_product[5]), $(current_primal_product[6])]") + # idx==1 && CUDA.@cuprintln("current_dual_product: [$(current_dual_product[1,1]), $(current_dual_product[1,2])]") + # idx==1 && CUDA.@cuprintln("Current dual solution: [$(current_dual_solution[1]), $(current_dual_solution[2]), $(current_dual_solution[3]), $(current_dual_solution[4]), $(current_dual_solution[5]), $(current_dual_solution[6])]\n") + # idx==1 && CUDA.@cuprintln("Initial dual solution: [$(initial_dual_solution[1]), $(initial_dual_solution[2]), $(initial_dual_solution[3]), $(initial_dual_solution[4]), $(initial_dual_solution[5]), $(initial_dual_solution[6])]\n") + + + # idx==1 && CUDA.@cuprintln("PARAMETER VALUES") + local_primal_weight = unsafe_load(CUDA.pointer(primal_weight, 1)) + + #TODO: remove later + # if ITERATION_COUNT == 4 + # local_primal_weight = 8.543563 + # end + local_step_size = unsafe_load(CUDA.pointer(step_size, 1)) + + # idx==1 && CUDA.@cuprintln("Primal Weight = $(local_primal_weight) \n") + # idx==1 && CUDA.@cuprintln("Step Size = $(local_step_size) \n") + + # Step 1) Add one to the total iterations tracker and cumulative kkt passes + + if idx==1 + cumulative_kkt_passes += Int32(1) + end + + # Step 2) Loop over nonzeros to update current_dual_product: + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * current_dual_solution[active_row + nz_rows[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + while idx <= n_vars + current_dual_product[LP, idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + + # Step 3) Calculate the next primal solution information + weight = ((inner_iterations + 1)/ (inner_iterations + 2)) + while idx <= n_vars + temp = current_primal_solution[LP, idx] - (local_step_size/local_primal_weight) * (scaled_objective_vector[LP, idx] - current_dual_product[LP, idx]) + pdhg_primal_solution[LP, idx] = max(scaled_variable_lower_bounds[LP, idx], min(scaled_variable_upper_bounds[LP, idx], temp)) + dual_slack[LP, idx] = (pdhg_primal_solution[LP, idx] - temp) / (local_step_size/local_primal_weight) + reflected_primal_solution[LP, idx] = 2.0 * pdhg_primal_solution[LP, idx] - current_primal_solution[LP, idx] + current_primal_solution[LP, idx] = weight * (reflection_coefficient * reflected_primal_solution[LP, idx] + + (1 - reflection_coefficient) * current_primal_solution[LP, idx]) + (1 - weight) * initial_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # Step 4) Loop over nonzeros to update current_primal_product, based on the reflected primal solution (A(2x(k+1)-xk)) [reflected] + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_rows[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * reflected_primal_solution[LP, nz_cols[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + + sync_threads() + while idx <= current_LP_length + current_primal_product[active_row + idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + + + # Step 5) Compute the next dual solution + # if idx==1 + # scaled_right_hand_side[active_row + 1] = -0.500284 + # scaled_right_hand_side[active_row + 2] = 1.058611 + # scaled_right_hand_side[active_row + 3] = -1.032256 + # scaled_right_hand_side[active_row + 4] = -2.526254 + # scaled_right_hand_side[active_row + 5] = -1.758950 + # scaled_right_hand_side[active_row + 6] = -0.501680 + # end + + weight = ((inner_iterations + 1)/ (inner_iterations + 2)) + while idx <= current_LP_length + # pdhg_dual_solution[active_row + idx] = current_dual_solution[active_row + idx] + (local_primal_weight*local_step_size)*( + # 2*delta_primal_product[active_row + idx] - current_primal_product[active_row + idx] + + # max(scaled_right_hand_side[active_row + idx], (1/(local_primal_weight*local_step_size))*current_dual_solution[active_row + idx] - + # (2*delta_primal_product[active_row + idx] - current_primal_product[active_row + idx]))) + + temp = (current_dual_solution[active_row + idx] / (local_primal_weight*local_step_size) - current_primal_product[active_row + idx]) + pdhg_dual_solution[active_row + idx] = (temp - min(-scaled_right_hand_side[active_row + idx], temp)) * (local_primal_weight*local_step_size) + # pdhg_dual_solution[active_row + idx] = (temp - max(0.0, temp)) * (local_primal_weight*local_step_size) + reflected_dual_solution[active_row + idx] = 2.0 * pdhg_dual_solution[active_row + idx] - current_dual_solution[active_row + idx] + current_dual_solution[active_row + idx] = weight * (reflection_coefficient * reflected_dual_solution[active_row + idx] + (1 - reflection_coefficient) * current_dual_solution[active_row + idx]) + + (1.0 - weight) * initial_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # idx==1 && CUDA.@cuprintln("PRIMAL THINGS AFTER TAKING STEP") + # idx==1 && CUDA.@cuprintln("current_dual_product: [$(current_dual_product[1,1]), $(current_dual_product[1,2])]") + # idx==1 && CUDA.@cuprintln("dual_slack: [$(dual_slack[1,1]), $(dual_slack[1,2])]") + # #idx==1 && CUDA.@cuprintln("Reflected_primal_solution: [$(reflected_primal_solution[1,1]), $(reflected_primal_solution[1,2])]") + # idx==1 && CUDA.@cuprintln("current_primal_solution: [$(current_primal_solution[1,1]), $(current_primal_solution[1,2])]") + # idx==1 && CUDA.@cuprintln("pdhg_primal_solution: [$(pdhg_primal_solution[1,1]), $(pdhg_primal_solution[1,2])]") + + # idx==1 && CUDA.@cuprintln("\nSCALED CONSTRAINT MATRIX") + # idx==1 && CUDA.@cuprintln("scaled_constraint_matrix: [$(scaled_constraint_matrix[1,1]), $(scaled_constraint_matrix[1,2])]") + # idx==1 && CUDA.@cuprintln("scaled_constraint_matrix: [$(scaled_constraint_matrix[2,1]), $(scaled_constraint_matrix[2,2])]") + # idx==1 && CUDA.@cuprintln("scaled_constraint_matrix: [$(scaled_constraint_matrix[3,1]), $(scaled_constraint_matrix[3,2])]") + # idx==1 && CUDA.@cuprintln("scaled_constraint_matrix: [$(scaled_constraint_matrix[4,1]), $(scaled_constraint_matrix[4,2])]") + # idx==1 && CUDA.@cuprintln("scaled_constraint_matrix: [$(scaled_constraint_matrix[5,1]), $(scaled_constraint_matrix[5,2])]") + # idx==1 && CUDA.@cuprintln("scaled_constraint_matrix: [$(scaled_constraint_matrix[6,1]), $(scaled_constraint_matrix[6,2])]") + + + # idx==1 && CUDA.@cuprintln("\nDUAL THINGS AFTER TAKING STEP") + # idx==1 && CUDA.@cuprintln("scaled_right_hand_side: [$(scaled_right_hand_side[1]), $(scaled_right_hand_side[2]), $(scaled_right_hand_side[3]), $(scaled_right_hand_side[4]), $(scaled_right_hand_side[5]), $(scaled_right_hand_side[6])]") + # idx==1 && CUDA.@cuprintln("current_primal_product: [$(current_primal_product[1]), $(current_primal_product[2]), $(current_primal_product[3]), $(current_primal_product[4]), $(current_primal_product[5]), $(current_primal_product[6])]") + # idx==1 && CUDA.@cuprintln("pdhg_dual_solution: [$(pdhg_dual_solution[1]), $(pdhg_dual_solution[2]), $(pdhg_dual_solution[3]), $(pdhg_dual_solution[4]), $(pdhg_dual_solution[5]), $(pdhg_dual_solution[6])]") + # #idx==1 && CUDA.@cuprintln("Reflected_dual_solution: [$(reflected_dual_solution[1]), $(reflected_dual_solution[2]), $(reflected_dual_solution[3]), $(reflected_dual_solution[4]), $(reflected_dual_solution[5]), $(reflected_dual_solution[6])]") + # idx==1 && CUDA.@cuprintln("current_dual_solution: [$(current_dual_solution[1]), $(current_dual_solution[2]), $(current_dual_solution[3]), $(current_dual_solution[4]), $(current_dual_solution[5]), $(current_dual_solution[6])] \n") + # error() + + + + sync_threads() + + + if inner_iterations == Int32(0) + + # Compute Δx = reflected_primal - pdhg_primal, and ||Δx||² + while idx <= n_vars + delta_primal[LP, idx] = reflected_primal_solution[LP, idx] - pdhg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= n_vars + + shared_space[idx] = delta_primal[LP, idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + anchor_squared_delta_primal = shared_space[1] + end + + # Compute Δy = reflected_dual - pdhg_dual, and ||Δy||² + while idx <= current_LP_length + delta_dual[active_row + idx] = reflected_dual_solution[active_row + idx] - pdhg_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + shared_space[idx] = delta_dual[active_row + idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx == 1 + anchor_squared_delta_dual = shared_space[1] + end + + # Compute A^T Δy (store in shared_space, which has n_vars width) + while idx <= n_vars + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * delta_dual[active_row + nz_rows[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + # Compute cross_term = dot(A^T Δy, Δx) + while idx <= n_vars + shared_space[idx] = shared_space[idx] * delta_primal[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + anchor_cross_term = shared_space[1] + end + + if unsafe_load(CUDA.pointer(do_restart, 1)) == true + if idx == 1 + # initial_fixed_error = sqrt((local_primal_weight / local_step_size) * anchor_squared_delta_primal + (1 / (local_step_size * local_primal_weight)) * anchor_squared_delta_dual + 2 * local_step_size * anchor_cross_term) + initial_fixed_error = sqrt((local_primal_weight) * anchor_squared_delta_primal + (1 / (local_primal_weight)) * anchor_squared_delta_dual + 2 * local_step_size * anchor_cross_term) + # CUDA.@cuprintln("POST RESTART -> initial fixed point error set to = $initial_fixed_error \n") + do_restart[1] = false + end + end + end + + inner_iterations += Int32(1) + step += Int32(1) + sync_threads() + end + iteration += n_steps + end + end + return nothing +end diff --git a/src/main_loop_rHalpern.jl b/src/main_loop_rHalpern.jl new file mode 100644 index 0000000..0b42a82 --- /dev/null +++ b/src/main_loop_rHalpern.jl @@ -0,0 +1,976 @@ + +function main_loop_rHalpern_kernel( + solutions, # [n_LPs, n_vars−1] = Holds solutions to LPs + objectives, # [n_LPs] = Holds dual objectives + original_variable_lower_bounds, # [n_LPs, n_vars] = Used for getting unscaled convergence info + original_variable_upper_bounds, # [n_LPs, n_vars] = "" + original_right_hand_side, # [n_LPs × total_LP_length] = RHS h before scaling + original_objective_vector, # [n_LPs, n_vars] = Cost vector c before scaling of each LP objective + original_objective_constant, # [n_LPs] = the offset constant in the objective function of each LP + # =============================================================================================================================# + # The original_* versions are only used to compute unscaled convergence statistics + # the algorithm runs entirely on the scaled_* versions. + # =============================================================================================================================# + scaled_variable_lower_bounds, # [n_LPs, n_vars] = Lower bounds after initial rescaling using Ruiz/Pock-Chambolle rescaling + scaled_variable_upper_bounds, # [n_LPs, n_vars] = Upper bounds after initial rescaling using Ruiz/Pock-Chambolle rescaling + scaled_constraint_matrix, # [n_LPS × total_LP_length, n_vars] = Constraint matrix G~ after initial rescaling + scaled_right_hand_side, # [n_LPs × total_LP_length] = Right-hand h~ side after initial rescaling + scaled_objective_vector, # [n_LPs, n_vars] = Objective vector c~ after initial rescaling + nz_count, # Int = Total number of nonzeros in the constraint matrix + nz_rows, # [nz_count] = Row indices containing nonzeros in the constraint matrix + nz_cols, # [nz_count] = Column indices containing nonzeros in the constraint matrix + active_constraint, # [n_LPs × total_LP_length] = Flag to indicate whether a constraint is in use. + variable_rescaling, # [n_LPs, n_vars] = Total value used for variable rescaling - Per-variable scale factor Dx so that unscaled_x = scaled_x / D_x + constraint_rescaling, # [n_LPs × total_LP_length] = Per-constraint scale factor Dc - ​Total value used for constraint rescaling + # input_current_primal_solution, # Usually 0s, but used for hot-starts (if desired in the future) + # input_current_dual_solution, # Usually 0s, but used for hot-starts (if desired in the future) + # input_current_primal_product, # Usually 0s, but used for hot-starts (if desired in the future) + # input_current_dual_product, # Usually 0s, but used for hot-starts (if desired in the future) + # Last Restart Solutions + # Current Iterate State - These are updated every PDLP step and represent where the algorithm currently is + current_primal_solution, # [n_LPs, n_vars] = Current x~ (scaled) - Currently active solution state + current_dual_solution, # [n_LPs × total_LP_length] = Current y~ (scaled) + current_dual_product, # [n_LPs, n_vars] = Cached G~^T * y~ to avoid recomputing + current_primal_product, # [n_LPs × total_LP_length] = Cached G~ * x~ to avoid recomputing (scaled) + current_primal_gradient, # [n_LPs, n_vars] = + initial_primal_solution, + initial_dual_solution, + pdhg_primal_solution, + pdhg_dual_solution, + reflected_primal_solution, + reflected_dual_solution, + dual_slack, + residual_primal_product, + residual_dual_product, + primal_residual, # [n_LPs × total_LP_length]] + primal_slack, # [n_LPs × total_LP_length]] + dual_residual, # [n_LPs , n_vars]] + # Unscaled Solution for convergence checking + original_primal_solution, # [n_LPs, n_vars] = Unscaled solution state information, used to calculate actual infeasibility + original_primal_gradient, # [n_LPs, n_vars] = Unscaled primal gradient + original_dual_solution, # [n_LPs × total_LP_length] = Unscaled dual solution y + original_primal_product, # [n_LPs × total_LP_length] = G~ * x~ unscaled (== G * x) + # Step Computation Buffers (scratch space per PDLP step) + buffer_kkt_primal_solution, # [n_LPs, n_vars] = normalized x~ with primal_ray_norm for infeasibility ray checks Intermediate storage for calculating infeasibility + buffer_kkt_primal_product, # [n_LPs × total_LP_length] = normalized original_primal_product using primal_ray_norm (G~ * buffer_kkt_primal_solution) + buffer_kkt_lower_variable_violation, # [n_LPs, n_vars] = max(l - x, 0) : Infeasible variable/constraint violation storage + buffer_kkt_upper_variable_violation, # [n_LPs, n_vars] = max(x - u, 0) : Infeasible variable/constraint violation storage + buffer_kkt_reduced_costs, # [n_LPs, n_vars] = + delta_primal, # [n_LPs, n_vars] = Δx~ = x'~ - x~ : the value used in calculating PDLP steps + delta_primal_product, # [n_LPs × total_LP_length] = G~ * Δx~ + delta_dual, # [n_LPs × total_LP_length] = Δy~ = y'~ - y~ : the value used in calculating PDLP steps + # Parameters for Control & Termination + input_primal_weight, # [n_LPs] = The original primal weight (could be moved internally if needed) + input_step_size, # [n_LPs] = The original step size η (could be moved internally if needed) + termination_reason, # [n_LPs] = TerminationReason enum per LP : Field to let the user know why the LP terminated + current_LP_length, # Int = The current length of each individual LP (i.e., number of constraints) + total_LP_length, # Int = The total possible size of each individual LP (i.e., max allowed number of constraints) + n_LPs, # Int = The current number of LPs being solved (fewer than the max may be used during B&B) + n_vars, # Int = The number of primal variables in the problem + iteration_limit, # Int = The maximum number of PDLP steps allowed before termination + kkt_matrix_pass_limit, # Float = The maximum number of KKT matrix passes (generally unused) + termination_evaluation_frequency, # Number of PDLP steps to take before checking termination criteria (default: 200) + necessary_reduction_for_restart, # Float = β_necessary = Necessary KKT error decrease factor needed for a restart if no progress is being made (default: 0.5) + sufficient_reduction_for_restart, # Float = β_sufficient = Sufficient KKT error decrease factor that would trigger a restart immediately (default: 0.2) + artificial_ratio_for_restart, # Float = β_artificial = artificial restart to avoid long inner loop (default = ) + extrapolation_coefficient, # Float = Value used in PDLP steps (default: 1.0) + reflection_coefficient, # Float = Reflection coefficient γ ∈ [0,1] at reflection step (default: 1.0) + pid_KP, # Float = Primal Weight Update PID controller proportional coefficient (default: 0.99) + pid_KI, # Float = Primal Weight Update PID controller integral coefficient (default: 0.01) + pid_KD, # Float = Primal Weight Update PID controller derivative coefficient (default: 0) + i_smooth, # Float = smoothing factor for restart error sum + abs_tol, # Float = Absolute tolerance for termination + rel_tol, # Float = Relative tolerance for termination + eps_primal_infeasible, # Float = Primal infeasibility tolerance + eps_dual_infeasible, # Float = Dual infeasibility tolerance + return_code, # Int = Indicator for returning primal obj (1), dual obj (2), or both (3) + global_upper_bound, # Float = Information about the B&B upper bound (PDLP terminates if a dual feasible solution is above this value) + skip_hard_problems, # Bool = Flag to skip problems with too many iterations + global_counter, # [1] (atomic Int32) = Count of LPs being solved - Running count of successfully completed LPs (shared across blocks via atomics) + iteration_counter, # [1] (atomic Int32) = Count of total iterations for solved LPs - Total iterations over all completed LPs (used to compute average for skip_hard_problems) + skip_flag, # [n_LPs] = Flag to completely skip an individual LP - Per-LP flag to skip entirely (e.g., already pruned in B&B) + iterations, # [n_LPs] = The final number of iterations needed for each LP + temp, + temp_dual, + residual_delta_dual, + ) + # In this kernel, assume that the number of blocks is equal to the number + # of LPs, so that all threads are working on one LP + LP = blockIdx().x + idx = threadIdx().x + + block_stride = blockDim().x # total number of threads in the block + grid_stride = gridDim().x # total number of blocks in the grid + + # Calculate strides for parallel reductions as the largest power of 2 + # less than n_vars (or current_LP_length) + var_stride = Int32(1) << floor(Int32, log2(n_vars)) + len_stride = Int32(1) << floor(Int32, log2(current_LP_length)) + + # Set up dynamic shared space + shared_space = @cuDynamicSharedMem(Float64, max(n_vars, current_LP_length)) + + + while LP <= n_LPs + + # Check if we're supposed to skip this LP + if skip_flag[LP] + LP += grid_stride + continue + end + + # Initialize basic information for this LP + ## information bookkeeping + ## Only thread 1 of this block will store these information + ## the information below does not need to be stored per thread + if idx==1 + cumulative_kkt_passes = 0.0 + + # Initialize temporary Float64 values needed for calculation + restart_primal_distance = 0.0 + restart_dual_distance = 0.0 + + CI_primal_objective = 0.0 # Needed for optimality termination check + CI_dual_objective = 0.0 + CI_l2_primal_residual = 0.0 # Needed for optimality termination check + CI_l2_dual_residual = 0.0 + + + last_fixed_point_error = Inf + restart_error = 0.0 + sum_restart_error = 0.0 + last_restart_error = 0.0 + + best_primal_dual_residual_gap = Inf + primal_dual_residual_gap = Inf + + restart_choice = RESTART_CHOICE_NO_RESTART + cross_term = 0 + squared_delta_dual = 0 + squared_delta_primal = 0 + anchor_cross_term = 0 + anchor_squared_delta_primal = 0 + anchor_squared_delta_dual = 0 + initial_fixed_error = 0.0 + candidate_fixed_error = 0.0 + + relative_dual_residual = 0.0 + relative_primal_residual = 0.0 + objective_gap = 0.0 + relative_objective_gap = 0.0 + + ratio_infeas = Inf + end + + # Other information that every thread needs (stored in the Registery memory) + ## The values are always identical across threads — it is replicated rather than shared for performance purposes. + + total_iterations = Int32(0) # total iteration counter (T) + inner_iterations = Int32(0) + + + # Information that is much easier to save as static shared memory (L1 Block memory) + do_restart = @cuStaticSharedMem(Bool, 1) + primal_weight = @cuStaticSharedMem(Float64, 1) + best_primal_weight = @cuStaticSharedMem(Float64, 1) + numerical_error = @cuStaticSharedMem(Bool, 1) + step_size = @cuStaticSharedMem(Float64, 1) + + if idx==1 + do_restart[1] = false + primal_weight[1] = input_primal_weight[LP] + best_primal_weight[1] = primal_weight[1] + step_size[1] = input_step_size[LP] + numerical_error[1] = false + end + + + # Set up the starting row for this LP (minus 1, so that the first + # row to consider is `active_row + 1`) + active_row = (LP-Int32(1)) * total_LP_length + + + # Set up current values to match inputs + while idx <= n_vars + current_primal_gradient[LP, idx] = scaled_objective_vector[LP, idx] + idx += block_stride + end + # resetting idx to its original thread id after tweaking it to work on different variables above + idx = threadIdx().x + + + # Set up objective vector and RHS norms + while idx <= n_vars + shared_space[idx] = original_objective_vector[LP, idx]^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + + if idx==1 + cache_l2_norm_primal_linear_objective = sqrt(shared_space[1]) + end + + while idx <= current_LP_length + shared_space[idx] = original_right_hand_side[active_row + idx]^2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + + if idx==1 + cache_l2_norm_primal_right_hand_side = sqrt(shared_space[1]) + end + + # Begin the main loop + while total_iterations <= iteration_limit + + ########################################################################################## + # Taking Halpern Reflected PDHG steps # + ########################################################################################## + + epoch_iterations = Int32(0) # counter for inner loop for computation purposes + + while epoch_iterations < termination_evaluation_frequency + + local_primal_weight = unsafe_load(CUDA.pointer(primal_weight, 1)) + local_step_size = unsafe_load(CUDA.pointer(step_size, 1)) + + # Step 1) Add one to the total iterations tracker and cumulative kkt passes + + if idx==1 + cumulative_kkt_passes += Int32(1) + end + + # Step 2) Loop over nonzeros to update current_dual_product: + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * current_dual_solution[active_row + nz_rows[idx]]) # adds one to kkt passes + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + while idx <= n_vars + current_dual_product[LP, idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + + # Step 3) Calculate the next primal solution information + + while idx <= n_vars + temp[LP, idx] = current_primal_solution[LP, idx] - (local_step_size/local_primal_weight) * (scaled_objective_vector[LP, idx] - current_dual_product[LP, idx]) + pdhg_primal_solution[LP, idx] = max(scaled_variable_lower_bounds[LP, idx], min(scaled_variable_upper_bounds[LP, idx], temp[LP, idx])) + dual_slack[LP, idx] = (pdhg_primal_solution[LP, idx] - temp[LP, idx]) / (local_step_size/local_primal_weight) + reflected_primal_solution[LP, idx] = 2.0 * pdhg_primal_solution[LP, idx] - current_primal_solution[LP, idx] + current_primal_solution[LP, idx] = ((inner_iterations + 1)/ (inner_iterations + 2)) * (reflection_coefficient * reflected_primal_solution[LP, idx] + + (1 - reflection_coefficient) * current_primal_solution[LP, idx]) + (1 - ((inner_iterations + 1)/ (inner_iterations + 2))) * initial_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # Step 4) Loop over nonzeros to update current_primal_product, based on the reflected primal solution (A(2x(k+1)-xk)) [reflected] + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_rows[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * reflected_primal_solution[LP, nz_cols[idx]]) # adds one to kkt passes + end + idx += block_stride + end + idx = threadIdx().x + + sync_threads() + while idx <= current_LP_length + current_primal_product[active_row + idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + + + # Step 5) Compute the next dual solution + + while idx <= current_LP_length + + temp_dual[active_row + idx] = (current_dual_solution[active_row + idx] / (local_primal_weight*local_step_size) - current_primal_product[active_row + idx]) + pdhg_dual_solution[active_row + idx] = (temp_dual[active_row + idx] - min(-scaled_right_hand_side[active_row + idx], temp_dual[active_row + idx])) * (local_primal_weight*local_step_size) + reflected_dual_solution[active_row + idx] = 2.0 * pdhg_dual_solution[active_row + idx] - current_dual_solution[active_row + idx] + current_dual_solution[active_row + idx] = ((inner_iterations + 1)/ (inner_iterations + 2)) * (reflection_coefficient * reflected_dual_solution[active_row + idx] + (1 - reflection_coefficient) * current_dual_solution[active_row + idx]) + + (1.0 - ((inner_iterations + 1)/ (inner_iterations + 2))) * initial_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + sync_threads() + + + if inner_iterations == Int32(0) + + # Compute Δx = reflected_primal - pdhg_primal, and ||Δx||² + while idx <= n_vars + delta_primal[LP, idx] = reflected_primal_solution[LP, idx] - pdhg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= n_vars + + shared_space[idx] = delta_primal[LP, idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + anchor_squared_delta_primal = shared_space[1] + end + + # Compute Δy = reflected_dual - pdhg_dual, and ||Δy||² + while idx <= current_LP_length + delta_dual[active_row + idx] = reflected_dual_solution[active_row + idx] - pdhg_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + while idx <= current_LP_length + shared_space[idx] = delta_dual[active_row + idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx == 1 + anchor_squared_delta_dual = shared_space[1] + end + + # Compute A^T Δy (store in shared_space, which has n_vars width) + while idx <= n_vars + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * delta_dual[active_row + nz_rows[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + # Compute cross_term = dot(A^T Δy, Δx) + while idx <= n_vars + shared_space[idx] = shared_space[idx] * delta_primal[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + anchor_cross_term = shared_space[1] + end + + if unsafe_load(CUDA.pointer(do_restart, 1)) == true + if idx == 1 + initial_fixed_error = sqrt( (local_primal_weight) * anchor_squared_delta_primal + (1 / (local_primal_weight)) * anchor_squared_delta_dual + 2 * local_step_size * anchor_cross_term ) + do_restart[1] = false + end + end + + end + + inner_iterations += Int32(1) + epoch_iterations += Int32(1) + sync_threads() + end + + if idx == 1 + cumulative_kkt_passes += Int32(2) + end + + while idx <= n_vars + current_primal_gradient[LP, idx] = scaled_objective_vector[LP, idx] - current_dual_product[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + + ########################################################################################## + # Compute Fixed-point error # + ########################################################################################## + sync_threads() + + while idx <= n_vars + shared_space[idx] = (reflected_primal_solution[LP, idx] - pdhg_primal_solution[LP, idx]) ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + squared_delta_primal = shared_space[1] + end + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= current_LP_length + shared_space[idx] = (reflected_dual_solution[active_row + idx] - pdhg_dual_solution[active_row + idx]) ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx == 1 + squared_delta_dual = shared_space[1] + end + + + while idx <= current_LP_length + residual_delta_dual[active_row + idx] = reflected_dual_solution[active_row + idx] - pdhg_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= n_vars + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * residual_delta_dual[active_row + nz_rows[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= n_vars + shared_space[idx] *= (reflected_primal_solution[LP, idx] - pdhg_primal_solution[LP, idx]) + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + cross_term = shared_space[1] + end + + if idx==1 + candidate_fixed_error = sqrt(primal_weight[1] * squared_delta_primal + squared_delta_dual / (primal_weight[1]) + 2.0 * step_size[1] * cross_term) + end + + + ########################################################################################## + # Compute Residuals # + ########################################################################################## + sync_threads() + + # ++++++++++++++++++++++++++++++++++++++++++++++++ Updating Primal Product and Dual Product ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # + # Recompute primal product as G * pdhg_primal + while idx <= current_LP_length + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_rows[idx]), + scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * pdhg_primal_solution[LP, nz_cols[idx]]) + end + idx += block_stride + end + idx = threadIdx().x + sync_threads() + while idx <= current_LP_length + residual_primal_product[active_row + idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + # Dual Product as G^T * pdhg_dual + while idx <= n_vars + shared_space[idx] = 0.0 + idx += block_stride + end + idx = threadIdx().x + sync_threads() + + # calculate residual dual product + while idx <= nz_count + if active_constraint[active_row + nz_rows[idx]] + CUDA.atomic_add!(CUDA.pointer(shared_space, nz_cols[idx]), scaled_constraint_matrix[active_row + nz_rows[idx], nz_cols[idx]] * pdhg_dual_solution[active_row + nz_rows[idx]]) + end + idx += block_stride + end + + idx = threadIdx().x + sync_threads() + while idx <= n_vars + residual_dual_product[LP, idx] = shared_space[idx] + idx += block_stride + end + idx = threadIdx().x + + sync_threads() + + while idx <= current_LP_length + clamped_val = max(residual_primal_product[active_row + idx], scaled_right_hand_side[active_row + idx]) + primal_residual[active_row + idx] = (residual_primal_product[active_row + idx] - clamped_val) * constraint_rescaling[active_row + idx] + primal_slack[active_row + idx] = max(pdhg_dual_solution[active_row + idx], 0.0) * scaled_right_hand_side[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # calculting dual_residual + while idx <= n_vars + dual_residual[LP, idx] = (scaled_objective_vector[LP, idx] - residual_dual_product[LP, idx] - dual_slack[LP, idx]) * variable_rescaling[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # cuPDLPx has L ∞ as default, I ignored it and used L2 norm only + + while idx <= current_LP_length + shared_space[idx] = primal_residual[active_row + idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) # this sum all squared terms + # taking its sqrt and assiging as primal residual norm + if idx == 1 + CI_l2_primal_residual = sqrt(shared_space[1]) + end + + #TODO: if bound objective rescaling is set to true we need to uncomment the line below to rescale back primal residual norm + # CI_l2_primal_residual /= constraint_bound_norm + + while idx <= n_vars + shared_space[idx] = dual_residual[LP, idx] ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) # this sum all squared terms + # taking its sqrt and assiging as primal residual norm + if idx == 1 + CI_l2_dual_residual = sqrt(shared_space[1]) + end + + #TODO: if bound objective rescaling is set to true we need to uncomment the line below to rescale back primal residual norm + # CI_l2_dual_residual /= objective_vector_norm + + # Compute the primal objective + while idx <= n_vars + shared_space[idx] = scaled_objective_vector[LP, idx] * pdhg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx==1 + CI_primal_objective = original_objective_constant[LP] + shared_space[1] + end + + # Compute the dual objective + while idx <= n_vars + shared_space[idx] = dual_slack[LP, idx] * pdhg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, var_stride, n_vars) + if idx == 1 + CI_dual_objective = shared_space[1] + end + + while idx <= current_LP_length + shared_space[idx] = primal_slack[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + if idx == 1 + CI_dual_objective += shared_space[1] + original_objective_constant[LP] + end + + if idx == 1 + # #CUDA.@cuprint("constraint bound norm = $constraint_bound_norm") + constraint_bound_norm = cache_l2_norm_primal_right_hand_side + relative_primal_residual = CI_l2_primal_residual / Float64(1.0 + constraint_bound_norm) + objective_vector_norm = cache_l2_norm_primal_linear_objective + relative_dual_residual = CI_l2_dual_residual / Float64(1.0 + objective_vector_norm) + objective_gap = abs(CI_primal_objective - CI_dual_objective) + relative_objective_gap = objective_gap / (1.0 + abs(CI_primal_objective) + abs(CI_dual_objective)) + end + + # +++++++++++++++++++++++++++++++++ Infeasibility Detection +++++++++++++++++++++++++++++++++++++++++ # + #TODO: finish feasiblity detection, cuPDLPx does not do it, but we need it in subproblems + + total_iterations += Int32(termination_evaluation_frequency) + + ########################################################################################## + # Check Termination Criteria # + ########################################################################################## + sync_threads() + + # Check optimality criteria + if idx==1 + + # Check if the dual objective value is above the B&B global upper bound and we + # satisfy the tolerance for dual feasibility + # (if we're past the first 10 iterations) + if (CI_dual_objective > global_upper_bound + abs_tol) && + (CI_l2_dual_residual < abs_tol + rel_tol*cache_l2_norm_primal_linear_objective) + termination_reason[LP] = TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT + end + + # If we want to skip harder-than-average problems, and we've already solved at least + # 100 problems, check if the current number of iterations is over two times the running + # average + if skip_hard_problems && (unsafe_load(CUDA.pointer(global_counter, 1)) > 100) + if total_iterations > 2*(unsafe_load(CUDA.pointer(iteration_counter, 1))/unsafe_load(CUDA.pointer(global_counter, 1))) + termination_reason[LP] = TERMINATION_REASON_IMPATIENCE + end + end + + # Check iteration limit + if total_iterations >= iteration_limit + + termination_reason[LP] = TERMINATION_REASON_ITERATION_LIMIT + end + + # Check KKT matrix pass limit + if cumulative_kkt_passes >= kkt_matrix_pass_limit + termination_reason[LP] = TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT + end + + # Check for numerical errors + if numerical_error[1] + termination_reason[LP] = TERMINATION_REASON_NUMERICAL_ERROR + end + + # Check if we're within the tolerances for primal and dual infeasibility, and that there's + # a sufficiently small gap between the primal and dual objective values. + + if (relative_dual_residual < rel_tol) && + (relative_primal_residual < rel_tol) && + (relative_objective_gap < rel_tol) + + termination_reason[LP] = TERMINATION_REASON_OPTIMAL + end + + # TODO: add checking on infeasibility after convergence failure + end + + + # If the LP is finished, update the solutions and objective(s), and + # then break out of the iteration loop and move on to the next LP + sync_threads() + + reason = unsafe_load(CUDA.pointer(termination_reason, LP)) + if reason == TERMINATION_REASON_OPTIMAL + while idx <= n_vars + solutions[LP, idx] = current_primal_solution[LP, idx] / variable_rescaling[LP, idx] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code==Int32(1) + objectives[LP] = CI_primal_objective + elseif return_code==Int32(2) + objectives[LP] = CI_dual_objective + else + objectives[LP, Int32(1)] = CI_primal_objective + objectives[LP, Int32(2)] = CI_dual_objective + end + iterations[LP] = total_iterations + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(total_iterations)) + end + + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_PRIMAL_INFEASIBLE) || + (reason == TERMINATION_REASON_DUAL_INFEASIBLE) + while idx <= n_vars + solutions[LP, idx] = current_primal_solution[LP, idx] / variable_rescaling[LP, idx] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code != Int32(3) + objectives[LP] = Inf + else + objectives[LP, Int32(1)] = Inf + objectives[LP, Int32(2)] = Inf + end + iterations[LP] = total_iterations + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(total_iterations)) + end + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_ITERATION_LIMIT) || + (reason == TERMINATION_REASON_KKT_MATRIX_PASS_LIMIT) || + (reason == TERMINATION_REASON_NUMERICAL_ERROR) + while idx <= n_vars + solutions[LP, idx] = current_primal_solution[LP, idx] / variable_rescaling[LP, idx] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code != Int32(3) + objectives[LP] = -Inf + else + objectives[LP, Int32(1)] = -Inf + objectives[LP, Int32(2)] = -Inf + end + iterations[LP] = total_iterations + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(total_iterations)) + end + + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_GLOBAL_UPPER_BOUND_HIT) + while idx <= n_vars + solutions[LP, idx] = current_primal_solution[LP, idx] / variable_rescaling[LP, idx] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + # Special case: This checks for a feasible dual objective value + # above the global upper bound in a B&B algorithm. The primal + # objective value may not be meaningful/feasible. + if return_code==Int32(1) + objectives[LP] = CI_primal_objective + elseif return_code==Int32(2) + objectives[LP] = CI_dual_objective + else + objectives[LP, Int32(1)] = CI_primal_objective + objectives[LP, Int32(2)] = CI_dual_objective + end + iterations[LP] = total_iterations + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(total_iterations)) + end + LP += grid_stride + break + elseif (reason == TERMINATION_REASON_IMPATIENCE) + while idx < n_vars + solutions[LP, idx] = current_primal_solution[LP, idx+1] / variable_rescaling[LP, idx+1] + idx += block_stride + end + idx = threadIdx().x + if idx==1 + if return_code != Int32(3) + objectives[LP] = -Inf + else + objectives[LP, Int32(1)] = -Inf + objectives[LP, Int32(2)] = -Inf + end + iterations[LP] = total_iterations + CUDA.atomic_add!(CUDA.pointer(global_counter, 1), Int32(1)) + CUDA.atomic_add!(CUDA.pointer(iteration_counter, 1), Int32(total_iterations)) + end + LP += grid_stride + break + end + + ########################################################################################## + # Check Restart Conditions # + ########################################################################################## + sync_threads() + # Check if we need to do a restart + if idx == 1 + if total_iterations == termination_evaluation_frequency + + do_restart[1] = true + + elseif total_iterations > termination_evaluation_frequency + + if candidate_fixed_error < sufficient_reduction_for_restart * initial_fixed_error + + do_restart[1] = true + end + + if candidate_fixed_error < necessary_reduction_for_restart * initial_fixed_error + if candidate_fixed_error > last_fixed_point_error + + do_restart[1] = true + end + end + + + if inner_iterations >= artificial_ratio_for_restart * (total_iterations) + do_restart[1] = true + end + end + + last_fixed_point_error = candidate_fixed_error + + end + + ########################################################################################## + # Perform Restart if do_restart = true # + ########################################################################################## + sync_threads() + + if unsafe_load(CUDA.pointer(do_restart, 1)) == true + + # Primal distance + while idx <= n_vars + shared_space[idx] = (pdhg_primal_solution[LP, idx] - initial_primal_solution[LP, idx]) ^ 2 + idx += block_stride + end + idx = threadIdx().x + + parallel_sum(shared_space, block_stride, var_stride, n_vars) + + if idx == 1 + restart_primal_distance = sqrt(shared_space[1]) + end + + # Dual distance + while idx <= current_LP_length + shared_space[idx] = (pdhg_dual_solution[active_row + idx] - initial_dual_solution[active_row + idx]) ^ 2 + idx += block_stride + end + idx = threadIdx().x + parallel_sum(shared_space, block_stride, len_stride, current_LP_length) + + if idx == 1 + restart_dual_distance = sqrt(shared_space[1]) + ratio_infeas = relative_dual_residual / relative_primal_residual + end + + # initialize the primal anchor + while idx <= n_vars + initial_primal_solution[LP, idx] = pdhg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # initialize the dual anchor + while idx <= current_LP_length + initial_dual_solution[active_row + idx] = pdhg_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # updating the current_primal_solution and current_dual_solution + while idx <= n_vars + current_primal_solution[LP, idx] = pdhg_primal_solution[LP, idx] + idx += block_stride + end + idx = threadIdx().x + + # initialize the dual anchor + while idx <= current_LP_length + current_dual_solution[active_row + idx] = pdhg_dual_solution[active_row + idx] + idx += block_stride + end + idx = threadIdx().x + + # resetting step itertation back to 0 + inner_iterations = Int32(0) + + # we do not restart to average in Halpern Reflection Scheme + if idx==1 + last_fixed_point_error = Inf + restart_choice = RESTART_CHOICE_LAST_ITERATE_RESET + end + else + if idx==1 + restart_choice = RESTART_CHOICE_NO_RESTART + end + end + + + + ################################################################### + ##### Compute a New Primal Weight (if a restart was used) + ################################################################### + sync_threads() + if idx==1 + if restart_choice == RESTART_CHOICE_LAST_ITERATE_RESET + + if restart_primal_distance > 1e-16 && restart_dual_distance > 1e-16 && restart_primal_distance < 1e12 && restart_dual_distance < 1e12 && ratio_infeas > 1e-8 && ratio_infeas < 1e8 + restart_error = log(restart_dual_distance) - log(restart_primal_distance) - log(primal_weight[1]) + sum_restart_error *= i_smooth + sum_restart_error += restart_error + + + primal_weight[1] *= exp(pid_KP * restart_error + pid_KI * sum_restart_error + pid_KD * (restart_error - last_restart_error)) + + # update last restart error + last_restart_error = restart_error + else + primal_weight[1] = best_primal_weight[1] + + sum_restart_error = 0.0 + last_restart_error = 0.0 + end + + # Update best primal weight if residual gap improved + primal_dual_residual_gap = abs(log10(relative_dual_residual / relative_primal_residual)) + + if primal_dual_residual_gap < best_primal_dual_residual_gap + best_primal_dual_residual_gap = primal_dual_residual_gap + best_primal_weight[1] = primal_weight[1] + end + end + end + + sync_threads() + end + + end + + return nothing +end + +# A quick function to calculate a parallel sum over the first max_len elements in the shared space +function parallel_sum(shared, block_stride, reduction_stride, maxlen) + sync_threads() + idx = threadIdx().x + while reduction_stride > 0 + while idx <= reduction_stride && idx + reduction_stride <= maxlen + shared[idx] += shared[idx + reduction_stride] + idx += block_stride + end + idx = threadIdx().x + sync_threads() + reduction_stride >>>= 1 + end + return nothing +end + +# A quick function to calculate a parallel max over the first max_len elements in the shared space +function parallel_max(shared, block_stride, reduction_stride, maxlen) + sync_threads() + idx = threadIdx().x + while reduction_stride > 0 + while idx <= reduction_stride && idx + reduction_stride <= maxlen + shared[idx] = max(shared[idx], shared[idx + reduction_stride]) + idx += block_stride + end + idx = threadIdx().x + sync_threads() + reduction_stride >>>= 1 + end + return nothing +end \ No newline at end of file diff --git a/src/primary_subroutines.jl b/src/primary_subroutines.jl index 6b8cc91..d46e855 100644 --- a/src/primary_subroutines.jl +++ b/src/primary_subroutines.jl @@ -1,12 +1,17 @@ function PDLP( - PDLP_data::PDLPData; - solutions::CuArray{Float64}=CuArray{Float64}(undef, PDLP_data.dims.n_LPs, PDLP_data.dims.n_vars), + PDLP_data::PDLPData; + solutions::CuArray{Float64}=CuArray{Float64}(undef, PDLP_data.dims.n_LPs, PDLP_data.dims.n_vars), objectives::CuArray{Float64}=CuArray{Float64}(undef, PDLP_data.dims.n_LPs), + version::Symbol=:rHalpern, return_dual_obj::Bool=false, return_both_obj::Bool=false, global_upper_bound::Float64=Inf - ) +) + if version + not in [:rHalpern, :original] + error("Version not recognized. Must be :rHalpern or :original") + end # Check return conditions and verify that `objectives` storage is correctly sized if return_dual_obj + return_both_obj == 2 @@ -35,7 +40,7 @@ function PDLP( validate(PDLP_data) # Reset all fields relevant to problem status - reset_all_fields!(PDLP_data) + reset_all_fields!(PDLP_data) # TODO: needs to get updated for the mergeed structs # Perform rescaling. Note that if hot-starting is to be added in the future, you should save all # primal/dual information (rather than only primal solutions and dual objectives), and then here, @@ -43,36 +48,137 @@ function PDLP( # previous run, then do re-scaling, then scale the primal/dual statuses again. This will also # require adding storage for these values, and un-commenting some lines in `main_loop.jl` # to allow hot-starting to impact the main PDLP algorithm. + rescale_problem( - PDLP_data.original_problem, - PDLP_data.scaled_problem, - PDLP_data.variable_rescaling, - PDLP_data.constraint_rescaling, + PDLP_data.original_problem, + PDLP_data.scaled_problem, + PDLP_data.variable_rescaling, + PDLP_data.constraint_rescaling, PDLP_data.dims, PDLP_data.parameters - ) + ) + # Scale the primal weight if desired (otherwise it should be 1.0) - # (Could also put this inside the main kernel) - if PDLP_data.parameters.scale_initial_primal_weight - select_initial_primal_weight(PDLP_data.primal_weight, PDLP_data.scaled_problem, PDLP_data.dims) - else + if PDLP_data.parameters.bound_objective_rescaling + PDLP_data.primal_weight .= 1.0 + + elseif PDLP_data.parameters.scale_initial_primal_weight + select_initial_primal_weight(PDLP_data.primal_weight, PDLP_data.original_problem, PDLP_data.dims, version) + end + + # Come up with the constant step size + if version == :rHalpern + update_constant_step_size(PDLP_data.scaled_problem, + PDLP_data.step_size, + PDLP_data.kernel_storage.eigenvector, + PDLP_data.kernel_storage.new_eigenvector, + PDLP_data.kernel_storage.u_vector, + PDLP_data.sparsity.nz_count[PDLP_data.dims.current_LP_length], + PDLP_data.sparsity.nz_rows, + PDLP_data.sparsity.nz_cols, + PDLP_data.active_constraint, + PDLP_data.dims) + elseif version == :original + update_step_size(PDLP_data.scaled_problem, PDLP_data.step_size, PDLP_data.dims) end - # Come up with a starting step size (Could also put this inside the kernel) - update_step_size(PDLP_data.scaled_problem, PDLP_data.step_size, PDLP_data.dims) # Run the main loop kernel max_size = max(PDLP_data.dims.n_vars, PDLP_data.dims.current_LP_length) - max_req = Int32(min(256, max(32, ceil(Int, max_size/32)*32))) + max_req = Int32(min(256, max(32, ceil(Int, max_size / 32) * 32))) # number of threads per LP based on LP size # Reset total solve and iteration number counters PDLP_data.global_counter .= Int32(0) PDLP_data.iteration_counter .= Int32(0) - # Call the main PDLP kernel - CUDA.@sync @cuda blocks=PDLP_data.dims.n_LPs threads=max_req shmem=max_size*sizeof(Float64) main_loop_kernel( + # Call the main PDLP kernel based on the set version + if version == :rHalpern + CUDA.@sync @cuda blocks = PDLP_data.dims.n_LPs threads = max_req shmem = max_size * sizeof(Float64) main_loop_rHalpern_kernel( + solutions, + objectives, + PDLP_data.original_problem.variable_lower_bounds, + PDLP_data.original_problem.variable_upper_bounds, + PDLP_data.original_problem.right_hand_side, + PDLP_data.original_problem.objective_vector, + PDLP_data.original_problem.objective_constant, + PDLP_data.scaled_problem.variable_lower_bounds, + PDLP_data.scaled_problem.variable_upper_bounds, + PDLP_data.scaled_problem.constraint_matrix, + PDLP_data.scaled_problem.right_hand_side, + PDLP_data.scaled_problem.objective_vector, + PDLP_data.sparsity.nz_count[PDLP_data.dims.current_LP_length], + PDLP_data.sparsity.nz_rows, + PDLP_data.sparsity.nz_cols, + PDLP_data.active_constraint, + PDLP_data.variable_rescaling, + PDLP_data.constraint_rescaling, + PDLP_data.kernel_storage.current_primal_solution, + PDLP_data.kernel_storage.current_dual_solution, + PDLP_data.kernel_storage.current_dual_product, + PDLP_data.kernel_storage.current_primal_product, + PDLP_data.kernel_storage.current_primal_gradient, + PDLP_data.kernel_storage.initial_primal_solution, + PDLP_data.kernel_storage.initial_dual_solution, + PDLP_data.kernel_storage.pdhg_primal_solution, + PDLP_data.kernel_storage.pdhg_dual_solution, + PDLP_data.kernel_storage.reflected_primal_solution, + PDLP_data.kernel_storage.reflected_dual_solution, + PDLP_data.kernel_storage.dual_slack, + PDLP_data.kernel_storage.residual_primal_product, + PDLP_data.kernel_storage.residual_dual_product, + PDLP_data.kernel_storage.primal_residual, + PDLP_data.kernel_storage.primal_slack, + PDLP_data.kernel_storage.dual_residual, + PDLP_data.kernel_storage.original_primal_solution, + PDLP_data.kernel_storage.original_primal_gradient, + PDLP_data.kernel_storage.original_dual_solution, + PDLP_data.kernel_storage.original_primal_product, + PDLP_data.kernel_storage.buffer_kkt_primal_solution, + PDLP_data.kernel_storage.buffer_kkt_primal_product, + PDLP_data.kernel_storage.buffer_kkt_lower_variable_violation, + PDLP_data.kernel_storage.buffer_kkt_upper_variable_violation, + PDLP_data.kernel_storage.buffer_kkt_reduced_costs, + PDLP_data.kernel_storage.delta_primal, + PDLP_data.kernel_storage.delta_primal_product, + PDLP_data.kernel_storage.delta_dual, + PDLP_data.primal_weight, + PDLP_data.step_size, + PDLP_data.termination_reason, + PDLP_data.dims.current_LP_length, + PDLP_data.dims.total_LP_length, + PDLP_data.dims.n_LPs, + PDLP_data.dims.n_vars, + PDLP_data.parameters.iteration_limit, + PDLP_data.parameters.kkt_matrix_pass_limit, + PDLP_data.parameters.termination_evaluation_frequency, + PDLP_data.parameters.necessary_reduction_for_restart, + PDLP_data.parameters.sufficient_reduction_for_restart, + PDLP_data.parameters.artificial_ratio_for_restart, + PDLP_data.parameters.extrapolation_coefficient, + PDLP_data.parameters.reflection_coefficient, + PDLP_data.parameters.pid_KP, + PDLP_data.parameters.pid_KI, + PDLP_data.parameters.pid_KD, + PDLP_data.parameters.i_smooth, + PDLP_data.parameters.termination_criteria.eps_optimal_absolute, + PDLP_data.parameters.termination_criteria.eps_optimal_relative, + PDLP_data.parameters.termination_criteria.eps_primal_infeasible, + PDLP_data.parameters.termination_criteria.eps_dual_infeasible, + return_code, + global_upper_bound, + PDLP_data.parameters.skip_hard_problems, + PDLP_data.global_counter, + PDLP_data.iteration_counter, + PDLP_data.skip_flag, + PDLP_data.iterations, + PDLP_data.kernel_storage.temp, + PDLP_data.kernel_storage.temp_dual, + PDLP_data.kernel_storage.residual_delta_dual, + ) + elseif version == :original + CUDA.@sync @cuda blocks = PDLP_data.dims.n_LPs threads = max_req shmem = max_size * sizeof(Float64) main_loop_original_kernel( solutions, objectives, PDLP_data.original_problem.variable_lower_bounds, @@ -90,8 +196,8 @@ function PDLP( PDLP_data.sparsity.nz_rows, PDLP_data.sparsity.nz_cols, PDLP_data.active_constraint, - PDLP_data.variable_rescaling, - PDLP_data.constraint_rescaling, + PDLP_data.variable_rescaling, + PDLP_data.constraint_rescaling, PDLP_data.kernel_storage.last_restart_primal_solution, PDLP_data.kernel_storage.last_restart_primal_gradient, PDLP_data.kernel_storage.last_restart_dual_solution, @@ -147,7 +253,8 @@ function PDLP( PDLP_data.iteration_counter, PDLP_data.skip_flag, PDLP_data.iterations, - ) + ) + end return nothing end @@ -166,33 +273,33 @@ function validate(PDLP_data::PDLPData) n_vars = PDLP_data.dims.n_vars tot_len = PDLP_data.dims.total_LP_length - if size(PDLP_data.original_problem.variable_lower_bounds, 1) < n_LPs || + if size(PDLP_data.original_problem.variable_lower_bounds, 1) < n_LPs || size(PDLP_data.original_problem.variable_lower_bounds, 2) != n_vars @show size(PDLP_data.original_problem.variable_lower_bounds) @show (n_LPs, n_vars) error("Lower bound matrix is the wrong size, or the listed number of LPs/variables is incorrect.") end - if size(PDLP_data.original_problem.variable_upper_bounds, 1) < n_LPs || + if size(PDLP_data.original_problem.variable_upper_bounds, 1) < n_LPs || size(PDLP_data.original_problem.variable_upper_bounds, 2) != n_vars @show size(PDLP_data.original_problem.variable_upper_bounds) @show (n_LPs, n_vars) error("Lower bound matrix is the wrong size, or the listed number of LPs/variables is incorrect.") end - if size(PDLP_data.original_problem.constraint_matrix, 1) < n_LPs*tot_len || + if size(PDLP_data.original_problem.constraint_matrix, 1) < n_LPs * tot_len || size(PDLP_data.original_problem.constraint_matrix, 2) != n_vars @show size(PDLP_data.original_problem.constraint_matrix) - @show (n_LPs*tot_len, n_vars) + @show (n_LPs * tot_len, n_vars) error("Constraint matrix is the wrong size, or the listed number of LPs/variables is incorrect.") end - if size(PDLP_data.original_problem.right_hand_side, 1) < n_LPs*tot_len + if size(PDLP_data.original_problem.right_hand_side, 1) < n_LPs * tot_len @show size(PDLP_data.original_problem.right_hand_side) - @show (n_LPs*tot_len) + @show (n_LPs * tot_len) error("Right-hand side is the wrong size, or the listed number of LPs/variables is incorrect.") end - if size(PDLP_data.original_problem.objective_vector, 1) < n_LPs || + if size(PDLP_data.original_problem.objective_vector, 1) < n_LPs || size(PDLP_data.original_problem.objective_vector, 2) != n_vars - @show size(PDLP_data.original_problem.objective_vector) - @show (n_LPs, n_vars) + @show size(PDLP_data.original_problem.objective_vector) + @show (n_LPs, n_vars) error("Objective vector is the wrong size, or the listed number of LPs/variables is incorrect.") end if size(PDLP_data.original_problem.objective_constant, 1) < n_LPs @@ -204,13 +311,13 @@ function validate(PDLP_data::PDLPData) end function rescale_problem( - original_problem::LinearProgramSet, - scaled_problem::LinearProgramSet, - variable_rescaling::CuArray{Float64}, - constraint_rescaling::CuArray{Float64}, + original_problem::LinearProgramSet, + scaled_problem::LinearProgramSet, + variable_rescaling::CuArray{Float64}, + constraint_rescaling::CuArray{Float64}, dims::PDLPDims, params::PDLPParams - ) +) # Copy problem info to the scaled problem so as to not overwrite the original copyto!(scaled_problem, original_problem) @@ -222,35 +329,41 @@ function rescale_problem( # Perform L_inf Ruiz rescaling for `ruiz_iterations` iterations if params.ruiz_iterations > 0 # Default is 10 ruiz_rescaling( - scaled_problem, - params.ruiz_iterations, - variable_rescaling, - constraint_rescaling, + scaled_problem, + params.ruiz_iterations, + variable_rescaling, + constraint_rescaling, dims, - ) + ) end - # cuPDLP also has a section for l2 norm rescaling, but it's off by default, so - # I haven't included it here - # Perform Pock-Chambolle rescaling if alpha isn't nothing if !isnothing(params.pock_chambolle_alpha) # Default is 1.0 pock_chambolle_rescaling( - scaled_problem, - params.pock_chambolle_alpha, - variable_rescaling, - constraint_rescaling, + scaled_problem, + params.pock_chambolle_alpha, + variable_rescaling, + constraint_rescaling, dims, - ) + ) end - + + return nothing end function reset_all_fields!(PDLP_data::PDLPData) + # Reset all kernel storage for field in fieldnames(KernelStorage) - CUDA.fill!(getfield(PDLP_data.kernel_storage, field), 0.0) + + if field == :eigenvector + CUDA.fill!(getfield(PDLP_data.kernel_storage, field), 1.0) # same vector for each LP + + else + CUDA.fill!(getfield(PDLP_data.kernel_storage, field), 0.0) + end + end # Reset the termination indicator diff --git a/src/structs.jl b/src/structs.jl index b123268..df9d70c 100644 --- a/src/structs.jl +++ b/src/structs.jl @@ -1,4 +1,3 @@ - # Structs that are needed for BatchPDLP to function """ TerminationReason explains why the solver stopped. See termination.jl for the @@ -52,8 +51,9 @@ RestartChoice specifies whether a restart was performed on a given iteration. @enum RestartChoice begin RESTART_CHOICE_UNSPECIFIED RESTART_CHOICE_NO_RESTART - RESTART_CHOICE_WEIGHTED_AVERAGE_RESET - RESTART_CHOICE_RESTART_TO_AVERAGE + RESTART_CHOICE_WEIGHTED_AVERAGE_RESET # => for original version + RESTART_CHOICE_RESTART_TO_AVERAGE # => for original version + RESTART_CHOICE_LAST_ITERATE_RESET # => for rHalpern version end mutable struct LinearProgramSet @@ -144,14 +144,11 @@ mutable struct TerminationCriteria end mutable struct KernelStorage + # original version fields===================== last_restart_primal_solution::CuArray{Float64} last_restart_primal_gradient::CuArray{Float64} last_restart_dual_solution::CuArray{Float64} last_restart_primal_product::CuArray{Float64} - current_primal_solution::CuArray{Float64} - current_dual_solution::CuArray{Float64} - current_dual_product::CuArray{Float64} - current_primal_product::CuArray{Float64} buffer_primal_gradient::CuArray{Float64} avg_primal_solution::CuArray{Float64} avg_primal_gradient::CuArray{Float64} @@ -162,10 +159,10 @@ mutable struct KernelStorage sum_primal_product::CuArray{Float64} sum_dual_product::CuArray{Float64} original_primal_solution::CuArray{Float64} - original_primal_gradient::CuArray{Float64} - original_dual_solution::CuArray{Float64} - original_primal_product::CuArray{Float64} - buffer_kkt_primal_solution::CuArray{Float64} + original_primal_gradient::CuArray{Float64} + original_dual_solution::CuArray{Float64} + original_primal_product::CuArray{Float64} + buffer_kkt_primal_solution::CuArray{Float64} buffer_kkt_primal_product::CuArray{Float64} buffer_kkt_lower_variable_violation::CuArray{Float64} buffer_kkt_upper_variable_violation::CuArray{Float64} @@ -173,23 +170,59 @@ mutable struct KernelStorage delta_primal::CuArray{Float64} delta_primal_product::CuArray{Float64} delta_dual::CuArray{Float64} + # shared fields ============================= + current_primal_solution::CuArray{Float64} + current_dual_solution::CuArray{Float64} + current_dual_product::CuArray{Float64} + current_primal_product::CuArray{Float64} + # rHalpern version fields ================== + current_primal_gradient::CuArray{Float64} + initial_primal_solution::CuArray{Float64} + initial_dual_solution::CuArray{Float64} + pdhg_primal_solution::CuArray{Float64} + pdhg_dual_solution::CuArray{Float64} + reflected_primal_solution::CuArray{Float64} + reflected_dual_solution::CuArray{Float64} + dual_slack::CuArray{Float64} + residual_primal_product::CuArray{Float64} + residual_dual_product::CuArray{Float64} + primal_residual::CuArray{Float64} + primal_slack::CuArray{Float64} + dual_residual::CuArray{Float64} + + eigenvector::CuArray{Float64} + new_eigenvector::CuArray{Float64} + u_vector::CuArray{Float64} + temp::CuArray{Float64} + temp_dual::CuArray{Float64} + residual_delta_dual::CuArray{Float64} end mutable struct PDLPParams + # shared parameters ========================= ruiz_iterations::Int pock_chambolle_alpha::Union{Nothing,Float64} scale_initial_primal_weight::Bool - termination_evaluation_frequency::Int32 - extrapolation_coefficient::Float64 - reduction_exponent::Float64 - growth_exponent::Float64 kkt_matrix_pass_limit::Float64 + termination_evaluation_frequency::Float64 necessary_reduction_for_restart::Float64 sufficient_reduction_for_restart::Float64 + artificial_ratio_for_restart::Float64 iteration_limit::Int32 skip_hard_problems::Bool termination_criteria::TerminationCriteria + # original parameters ====================== + extrapolation_coefficient::Float64 + reduction_exponent::Float64 + growth_exponent::Float64 + # rHalpern parameters ====================== + reflection_coefficient::Float64 + bound_objective_rescaling::Bool + pid_KP::Float64 + pid_KI::Float64 + pid_KD::Float64 + i_smooth::Float64 end mutable struct PDLPDims @@ -252,17 +285,23 @@ function PDLPData( n_vars::Int, total_LP_length::Int; sparsity::Matrix{Bool} = fill(true, total_LP_length, n_vars), - iteration_limit::Int = Int(typemax(Int32)), - termination_evaluation_frequency::Int = 64, + iteration_limit::Int = 1000000,# or Int(typemax(Int32)), extrapolation_coefficient::Float64 = 1.0, reduction_exponent::Float64 = 0.3, growth_exponent::Float64 = 0.6, + reflection_coefficient::Float64 = 0.9, kkt_matrix_pass_limit::Float64 = Inf, - necessary_reduction_for_restart::Float64 = 0.8, + termination_evaluation_frequency::Int64 = 64, + necessary_reduction_for_restart::Float64 = 0.5, sufficient_reduction_for_restart::Float64 = 0.2, + artificial_ratio_for_restart::Float64 = 0.36, abs_tol::Float64 = 1.0E-8, rel_tol::Float64 = 1.0E-8, skip_hard_problems::Bool = false, + pid_KP::Float64 = 0.1, + pid_KI::Float64 = 0.01, + pid_KD::Float64 = 0.55, + i_smooth::Float64 = 0.3 ) # Call the sparse constructor to get sparsity information nz, nz_rows, nz_cols = sparse_constructor(sparsity) @@ -273,14 +312,16 @@ function PDLPData( CUDA.zeros(Float64, total_LP_length * n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), - CUDA.zeros(Float64, n_LPs)), + CUDA.zeros(Float64, n_LPs) + ), LinearProgramSet( # Scaled LPs CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), - CUDA.zeros(Float64, n_LPs)), + CUDA.zeros(Float64, n_LPs), + ), PDLPSparsity( nz, nz_rows, @@ -292,15 +333,12 @@ function PDLPData( # CuArray{Float64}(undef, n_LPs * total_LP_length), # Initial primal product # CuArray{Float64}(undef, n_LPs, n_vars), # Initial dual product KernelStorage( + # original fields ============================ CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), - CUDA.zeros(Float64, total_LP_length * n_LPs), - CUDA.zeros(Float64, n_LPs, n_vars), - CUDA.zeros(Float64, total_LP_length * n_LPs), - CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), @@ -309,30 +347,54 @@ function PDLPData( CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), + # - this is original_primal_solution forward + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), + CUDA.zeros(Float64, total_LP_length * n_LPs), + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), + CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, n_LPs, n_vars), - CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, total_LP_length * n_LPs), + # shared fields ============================== CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), + # rHalpern fields ============================ + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), CUDA.zeros(Float64, n_LPs, n_vars), CUDA.zeros(Float64, total_LP_length * n_LPs), - CUDA.zeros(Float64, total_LP_length * n_LPs) + CUDA.zeros(Float64, total_LP_length * n_LPs), + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.ones(Float64, total_LP_length * n_LPs),#6 eigenvec + CUDA.zeros(Float64, total_LP_length * n_LPs),#5 new_eigenvec + CUDA.zeros(Float64, n_LPs, n_vars),#4 u_vec + CUDA.zeros(Float64, n_LPs, n_vars), + CUDA.zeros(Float64, total_LP_length * n_LPs), + CUDA.zeros(Float64, total_LP_length * n_LPs), ), PDLPParams( # Parameters 10, # Iterations for Ruiz rescaling - 1.0, # Alpha for Pock Chambolle rescaling + 1, # Alpha for Pock Chambolle rescaling true, # Scale initial primal weight flag - termination_evaluation_frequency, # Termination evaluation frequency - extrapolation_coefficient, # Extrapolation coefficient used for taking steps - reduction_exponent, # Reduction exponent (for step size updates) - growth_exponent, # Growth coefficient (for step size updates) kkt_matrix_pass_limit, # Limit for KKT matrix passes (default Inf) + termination_evaluation_frequency, # Number of PDLP steps to take before checking termination criteria (default: 200) necessary_reduction_for_restart, # Necessary reduction for restart (default 0.8) sufficient_reduction_for_restart, # Sufficient reduction for restart (default 0.2) + artificial_ratio_for_restart, # Long Inner Loop restarting condition ratio iteration_limit, # Iteration limit (default typemax(Int32)) skip_hard_problems, # Flag to skip problems with too many iterations TerminationCriteria( @@ -343,7 +405,18 @@ function PDLPData( Inf, # Time limit (s) iteration_limit, # Iteration limit kkt_matrix_pass_limit # KKT matrix pass limit, - ) + ), + # ============================= Original ========================================== + extrapolation_coefficient, # Extrapolation coefficient used for taking steps + reduction_exponent, # Reduction exponent (for step size updates) + growth_exponent, # Growth coefficient (for step size updates) + # ============================= rHalpern ========================================== + reflection_coefficient, # Reflection Coefficient for Halpern Scheme + false, # Bound Objective Rescaling flag + pid_KP, + pid_KI, + pid_KD, + i_smooth ), PDLPDims( Int32(0), # Current LP length