Source code for autolyap.algorithms.davis_yin

# SPDX-FileCopyrightText: 2025-2026 AutoLyap contributors
# SPDX-License-Identifier: GPL-3.0-only

import numpy as np
from typing import Tuple
from .algorithm import Algorithm

[docs] class DavisYin(Algorithm): r""" Davis--Yin three-operator splitting :cite:`davis2017threeoperatorsplitting`. See :doc:`3. Algorithm representation </theory/algorithm_representation>` for mathematical notation and definitions. Notation-driven assumptions are declared by the user via :class:`~autolyap.problemclass.InclusionProblem`: when present, terms written with :math:`\nabla` use differentiable functions, terms written with :math:`\prox_{\gamma f}` use proper, lower semicontinuous, convex functions, and terms written with :math:`J_{\gamma G}` use maximally monotone operators. Standard form ------------- For an initial point :math:`x^0 \in \calH`, step size :math:`\gamma \in \reals_{++}`, and relaxation parameter :math:`\lambda \in \reals`, define, for all :math:`k \in \naturals`, .. math:: \left[ \begin{aligned} v^k &= \prox_{\gamma f_1}(x^k), \\ w^k &= \prox_{\gamma f_3}\!\left(2v^k - x^k - \gamma \nabla f_2(v^k)\right), \\ x^{k+1} &= x^k + \lambda (w^k - v^k). \end{aligned} \right. State-space representation -------------------------- The update can be written in the algorithm representation with .. math:: \begin{aligned} \bx^k &= x^k, \\ \bu^k &= \left( \frac{x^k-v^k}{\gamma},\; \nabla f_2(v^k),\; \frac{2v^k-x^k-\gamma\nabla f_2(v^k)-w^k}{\gamma} \right), \\ \by^k &= (v^k, v^k, w^k). \end{aligned} With this representation, the system matrices are .. math:: \begin{aligned} A_k &= \begin{bmatrix} 1 \end{bmatrix}, & B_k &= \begin{bmatrix} -\gamma\lambda & -\gamma\lambda & -\gamma\lambda \end{bmatrix}, \\ C_k &= \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix}, & D_k &= \begin{bmatrix} -\gamma & 0 & 0 \\ -\gamma & 0 & 0 \\ -2\gamma & -\gamma & -\gamma \end{bmatrix}. \end{aligned} These are the system matrices returned by :meth:`~autolyap.algorithms.Algorithm.get_ABCD`. Structural parameters --------------------- .. math:: n = 1,\quad m = 3,\quad (\bar{m}_i)_{i=1}^{m} = (1,1,1),\quad \bar{m} = 3. .. math:: I_{\text{func}} = \{1,2,3\},\quad I_{\text{op}} = \varnothing. """ def __init__(self, gamma: float, lambda_value: float) -> None: r""" Initialize the Davis--Yin method. Structural inputs passed to :class:`~autolyap.algorithms.Algorithm` are .. math:: n = 1,\quad m = 3,\quad (\bar m_i)_{i=1}^{m} = (1,1,1),\quad \bar m = 3,\quad I_{\mathrm{func}} = \{1,2,3\},\quad I_{\mathrm{op}} = \varnothing. """ super().__init__(1, 3, [1, 1, 1], [1, 2, 3], []) self.gamma = gamma self.lambda_value = lambda_value
[docs] def set_gamma(self, gamma: float) -> None: r""" Set the step-size parameter :math:`\gamma`. Shared notation follows the class-level reference in :class:`~autolyap.algorithms.DavisYin`. **Parameters** - `gamma` (:class:`~typing.Union`\[:class:`int`, :class:`float`\]): The value corresponding to :math:`\gamma`. **Raises** - `ValueError`: If `gamma` is not a finite real number or if :math:`\gamma \le 0`. """ gamma = self._validate_positive_finite_real(gamma, "gamma") self._set_dynamic_parameter("gamma", gamma)
[docs] def set_lambda(self, lambda_value: float) -> None: r""" Set the relaxation parameter :math:`\lambda`. Shared notation follows the class-level reference in :class:`~autolyap.algorithms.DavisYin`. **Parameters** - `lambda_value` (:class:`~typing.Union`\[:class:`int`, :class:`float`\]): The value corresponding to :math:`\lambda`. **Raises** - `ValueError`: If `lambda_value` is not a finite real number. """ lambda_value = self._validate_finite_real(lambda_value, "lambda_value") self._set_dynamic_parameter("lambda_value", lambda_value)
[docs] def get_ABCD(self, k: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: A = np.array([[1]]) B = -self.gamma*self.lambda_value*np.array([[1,1,1]]) C = np.array([[1],[1],[1]]) D = -self.gamma*np.array([[1,0,0],[1,0,0],[2,1,1]]) return (A, B, C, D)