Format (#280)

* Format

* add undersocres

Author: joaquimg

src/NonLinearProgram/NonLinearProgram.jl

@@ -294,8 +294,8 @@ function Model()
     )
 end
 
-objective_sense(form::Form) = form.sense
-objective_sense(model::Model) = objective_sense(model.model)
+_objective_sense(form::Form) = form.sense
+_objective_sense(model::Model) = _objective_sense(model.model)
 
 function MOI.set(
     model::Model,

src/NonLinearProgram/nlp_utilities.jl

@@ -445,7 +445,9 @@ function _compute_derivatives_no_relax(
     return ∂s, K, N
 end
 
-_sense_mult(model::Model) = objective_sense(model) == MOI.MIN_SENSE ? 1.0 : -1.0
+function _sense_mult(model::Model)
+    return _objective_sense(model) == MOI.MIN_SENSE ? 1.0 : -1.0
+end
 
 """
     _compute_sensitivity(model::Model; tol=1e-6)

src/moi_wrapper.jl

@@ -521,7 +521,11 @@ function reverse_differentiate!(model::Optimizer)
         )
     end
     diff = _diff(model)
-    MOI.set(diff, NonLinearKKTJacobianFactorization(), model.input_cache.factorization)
+    MOI.set(
+        diff,
+        NonLinearKKTJacobianFactorization(),
+        model.input_cache.factorization,
+    )
     for (vi, value) in model.input_cache.dx
         MOI.set(diff, ReverseVariablePrimal(), model.index_map[vi], value)
     end
@@ -551,7 +555,11 @@ function forward_differentiate!(model::Optimizer)
         )
     end
     diff = _diff(model)
-    MOI.set(diff, NonLinearKKTJacobianFactorization(), model.input_cache.factorization)
+    MOI.set(
+        diff,
+        NonLinearKKTJacobianFactorization(),
+        model.input_cache.factorization,
+    )
     if model.input_cache.objective !== nothing
         MOI.set(
             diff,
@@ -746,7 +754,7 @@ function MOI.get(
     model::Optimizer,
     ::ReverseVariablePrimal,
     vi::MOI.VariableIndex,
-    )
+)
     return get(model.input_cache.dx, vi, 0.0)
 end
 
@@ -857,12 +865,16 @@ end
 function MOI.supports(
     model::Optimizer,
     ::NonLinearKKTJacobianFactorization,
-    ::Function
+    ::Function,
 )
     return true
 end
 
-function MOI.set(model::Optimizer, ::NonLinearKKTJacobianFactorization, factorization)
+function MOI.set(
+    model::Optimizer,
+    ::NonLinearKKTJacobianFactorization,
+    factorization,
+)
     model.input_cache.factorization = factorization
     return
 end

test/nlp_program.jl

@@ -825,15 +825,23 @@ function test_changing_factorization()
     )
 
     # wrong type
-    @test_throws MethodError MOI.set(m, DiffOpt.NonLinearKKTJacobianFactorization(), 2)
+    @test_throws MethodError MOI.set(
+        m,
+        DiffOpt.NonLinearKKTJacobianFactorization(),
+        2,
+    )
 
     # correct type but wrong number of arguments
     MOI.set(m, DiffOpt.NonLinearKKTJacobianFactorization(), SparseArrays.lu)
 
     @test_throws MethodError DiffOpt.forward_differentiate!(m)
 
     # correct type and correct number of arguments
-    MOI.set(m, DiffOpt.NonLinearKKTJacobianFactorization(), (M, model) -> SparseArrays.lu(M))
+    MOI.set(
+        m,
+        DiffOpt.NonLinearKKTJacobianFactorization(),
+        (M, model) -> SparseArrays.lu(M),
+    )
 
     # Compute derivatives
     DiffOpt.forward_differentiate!(m)