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mrava87 merged 1 commit into from
Oct 18, 2026
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Doc: removed wrong comments about tau limits in ADMM solvers #279

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39 changes: 20 additions & 19 deletions pyproximal/optimization/cls_primal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2218,9 +2218,9 @@ def setup( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector (not required when ``gfirst=False``, can pass ``None``)
tau : :obj:`float`
Positive scalar weight, which should satisfy the following condition
to guarantees convergence: :math:`\tau \in (0, 1/L]` where ``L`` is
the Lipschitz constant of :math:`\nabla f`.
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
z0 : :obj:`numpy.ndarray`, optional
Initial z vector (not required when ``gfirst=True``)
gfirst : :obj:`bool`, optional
Expand Down Expand Up @@ -2425,9 +2425,9 @@ def solve( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector (not required when ``gfirst=False``, can pass ``None``)
tau : :obj:`float`
Positive scalar weight, which should satisfy the following condition
to guarantees convergence: :math:`\tau \in (0, 1/L]` where ``L`` is
the Lipschitz constant of :math:`\nabla f`.
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
z0 : :obj:`numpy.ndarray`, optional
Initial z vector (not required when ``gfirst=True``)
gfirst : :obj:`bool`, optional
Expand Down Expand Up @@ -2582,8 +2582,9 @@ def setup( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`
Positive scalar weight, which should satisfy the following condition
to guarantees convergence: :math:`\tau \in (0, 1/\lambda_{max}(\mathbf{A}^H\mathbf{A})]`.
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
z0 : :obj:`numpy.ndarray`
Initial auxiliary vector. If ``None``, initialized to ``A @ x0``.
gfirst : :obj:`bool`, optional
Expand Down Expand Up @@ -2840,9 +2841,9 @@ def solve( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector (not required when ``gfirst=False``, can pass ``None``)
tau : :obj:`float`
Positive scalar weight, which should satisfy the following condition
to guarantees convergence: :math:`\tau \in (0, 1/L]` where ``L`` is
the Lipschitz constant of :math:`\nabla f`.
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
z0 : :obj:`numpy.ndarray`, optional
Initial z vector (not required when ``gfirst=True``)
gfirst : :obj:`bool`, optional
Expand Down Expand Up @@ -3002,9 +3003,7 @@ def setup( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`, optional
Positive scalar weight, which should satisfy the following
condition to guarantee convergence: :math:`\mu \in (0,
\tau/\lambda_{max}(\mathbf{A}^H\mathbf{A})]`.
Positive scalar weight.
mu : :obj:`float`, optional
Second positive scalar weight, which should satisfy the following
condition to guarantees convergence: :math:`\mu \in (0,
Expand Down Expand Up @@ -3209,9 +3208,7 @@ def solve( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`, optional
Positive scalar weight, which should satisfy the following
condition to guarantee convergence: :math:`\mu \in (0,
\tau/\lambda_{max}(\mathbf{A}^H\mathbf{A})]`.
Positive scalar weight.
mu : :obj:`float`, optional
Second positive scalar weight, which should satisfy the following
condition to guarantees convergence: :math:`\mu \in (0,
Expand Down Expand Up @@ -4447,7 +4444,9 @@ def setup( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`
Positive scalar weight
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
niter : :obj:`int`, optional
Number of iterations (default to ``None`` in case a user wants to
manually step over the solver)
Expand Down Expand Up @@ -4639,7 +4638,9 @@ def solve( # type: ignore[override]
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`
Positive scalar weight
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
niter : :obj:`int`, optional
Number of iterations of iterative scheme.
tol : :obj:`float`, optional
Expand Down
19 changes: 10 additions & 9 deletions pyproximal/optimization/primal.py
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Expand Up @@ -698,9 +698,9 @@ def ADMM(
x0 : :obj:`numpy.ndarray`
Initial vector (not required when ``gfirst=False``, can pass ``None``)
tau : :obj:`float`
Positive scalar weight, which should satisfy the following condition
to guarantees convergence: :math:`\tau \in (0, 1/L]` where ``L`` is
the Lipschitz constant of :math:`\nabla f`.
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
niter : :obj:`int`
Number of iterations of iterative scheme
z0 : :obj:`numpy.ndarray`, optional
Expand Down Expand Up @@ -822,8 +822,9 @@ def ADMML2(
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`
Positive scalar weight, which should satisfy the following condition
to guarantees convergence: :math:`\tau \in (0, 1/\lambda_{max}(\mathbf{A}^H\mathbf{A})]`.
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
niter : :obj:`int`, optional
Number of iterations of iterative scheme
z0 : :obj:`numpy.ndarray`
Expand Down Expand Up @@ -944,9 +945,7 @@ def LinearizedADMM(
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`, optional
Positive scalar weight, which should satisfy the following
condition to guarantee convergence: :math:`\mu \in (0,
\tau/\lambda_{max}(\mathbf{A}^H\mathbf{A})]`.
Positive scalar weight.
mu : :obj:`float`, optional
Second positive scalar weight, which should satisfy the following
condition to guarantees convergence: :math:`\mu \in (0,
Expand Down Expand Up @@ -1372,7 +1371,9 @@ def ConsensusADMM( # pylint: disable=invalid-name
x0 : :obj:`numpy.ndarray`
Initial vector
tau : :obj:`float`
Positive scalar weight
Positive scalar weight. Convergence holds for any
:math:`\tau > 0` in the convex case, but its value affects the
convergence speed.
niter : :obj:`int`, optional
Number of iterations of iterative scheme.
tol : :obj:`float`, optional
Expand Down
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