求解约束广义方程的拟牛顿方法的收敛性

IF 1.3 3区 数学 Q4 AUTOMATION & CONTROL SYSTEMS Esaim-Control Optimisation and Calculus of Variations Pub Date : 2022-04-08 DOI:10.1051/cocv/2022026
Gilson do Nascimento Silva, R. Andreani, Rui Marques Carvalho, L. Secchin
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引用次数: 3

摘要

本文主要讨论求解约束广义方程的拟牛顿方法。众所周知,这个问题最早是由Robinson和Josephy在70年代研究的。从那时起,它被许多其他研究人员广泛研究,特别是Dontchev和Rockafellar。在这里,我们提出了两种broyden型准牛顿方法来处理约束广义方程,一种需要精确地解决子问题,另一种允许不精确,这更接近于数值现实。在这两种情况下,可行集上的投影也是不精确的。在更新矩阵有界退化和Lipschitz连续性假设下,建立了一般拟牛顿方法的局部收敛性。特别地,我们证明了在适当的假设下,一般格式线性收敛于解。此外,当使用broyden型更新规则时,收敛性是超线性的。一些数值算例说明了所提方法的适用性。
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Convergence of quasi-Newton methods for solving constrained generalized equations                
In this paper, we focus on quasi-Newton methods to solve constrained generalized equations.As is well-known, this problem was firstly studied by Robinson and Josephy in the 70's. Since then, it has been extensively studied by many other researchers, specially Dontchev and Rockafellar. Here, we propose two Broyden-type quasi-Newton approaches to dealing with constrained generalized equations, one that requires the exact resolution of the subproblems, and other that allows inexactness, which is closer to numerical reality. In both cases, projections onto the feasible set are also inexact. The local convergence of general quasi-Newton approaches is established under a bounded deterioration of the update matrix and Lipschitz continuity hypotheses. In particular, we prove that a general scheme converges linearly to the solution under suitable assumptions. Furthermore, when a Broyden-type update rule is used, the convergence is superlinearly. Some numerical examples illustrate the applicability of the proposed methods.
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来源期刊
Esaim-Control Optimisation and Calculus of Variations
Esaim-Control Optimisation and Calculus of Variations Mathematics-Computational Mathematics
自引率
7.10%
发文量
77
期刊介绍: ESAIM: COCV strives to publish rapidly and efficiently papers and surveys in the areas of Control, Optimisation and Calculus of Variations. Articles may be theoretical, computational, or both, and they will cover contemporary subjects with impact in forefront technology, biosciences, materials science, computer vision, continuum physics, decision sciences and other allied disciplines. Targeted topics include: in control: modeling, controllability, optimal control, stabilization, control design, hybrid control, robustness analysis, numerical and computational methods for control, stochastic or deterministic, continuous or discrete control systems, finite-dimensional or infinite-dimensional control systems, geometric control, quantum control, game theory; in optimisation: mathematical programming, large scale systems, stochastic optimisation, combinatorial optimisation, shape optimisation, convex or nonsmooth optimisation, inverse problems, interior point methods, duality methods, numerical methods, convergence and complexity, global optimisation, optimisation and dynamical systems, optimal transport, machine learning, image or signal analysis; in calculus of variations: variational methods for differential equations and Hamiltonian systems, variational inequalities; semicontinuity and convergence, existence and regularity of minimizers and critical points of functionals, relaxation; geometric problems and the use and development of geometric measure theory tools; problems involving randomness; viscosity solutions; numerical methods; homogenization, multiscale and singular perturbation problems.
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