Subgradient projection algorithms for convex feasibility problems in the presence of computational errors

被引：4

作者：

Zaslavski, Alexander J. ^{[1
]}

机构：

[1] Technion Israel Inst Technol, Dept Math, IL-32000 Haifa, Israel

来源：

JOURNAL OF APPROXIMATION THEORY | 2013年 / 175卷

关键词：

Approximate solution; Feasibility problem; Hilbert space; Subgradient projection algorithm; CONVERGENCE;

D O I：

10.1016/j.jat.2013.07.012

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In the present paper we study convergence of subgradient projection algorithms for solving convex feasibility problems in a Hilbert space. Our goal is to obtain an approximate solution of the problem in the presence of computational errors. We show that our subgradient projection algorithm generates a good approximate solution, if the sequence of computational errors is bounded from above by a constant. (c) 2013 Elsevier Inc. All rights reserved.

引用

页码：19 / 42

页数：24

共 50 条

[1] Subgradient Projection Algorithms and Approximate Solutions of Convex Feasibility Problems
Zaslavski, A. J.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2013, 157 (03) : 803 - 819
[2] Subgradient Projection Algorithms and Approximate Solutions of Convex Feasibility Problems
A. J. Zaslavski
Journal of Optimization Theory and Applications, 2013, 157 : 803 - 819
[3] DYNAMIC STRING-AVERAGING PROJECTION METHODS FOR CONVEX FEASIBILITY PROBLEMS IN THE PRESENCE OF COMPUTATIONAL ERRORS
Zaslavski, Alexander J.
JOURNAL OF NONLINEAR AND CONVEX ANALYSIS, 2014, 15 (03) : 623 - 636
[4] Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities
Necoara, Ion
Nedic, Angelia
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2021, 80 (01) : 121 - 152
[5] The Projected Subgradient Method for Nonsmooth Convex Optimization in the Presence of Computational Errors
Zaslavski, Alexander J.
NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION, 2010, 31 (05) : 616 - 633
[6] Random minibatch projection algorithms for convex feasibility problems
Nedic, Angelia
Necoara, Ion
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), 2019, : 1507 - 1512
[7] Projection algorithms for solving convex feasibility problems
Bauschke, HH
Borwein, JM
SIAM REVIEW, 1996, 38 (03) : 367 - 426
[8] Extrapolated cyclic subgradient projection methods for the convex feasibility problems and their numerical behaviour
Cegielski, Andrzej
Nimana, Nimit
OPTIMIZATION, 2019, 68 (01) : 145 - 161
[9] Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
Ram, S. Sundhar
Nedic, A.
Veeravalli, V. V.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2010, 147 (03) : 516 - 545
[10] Stochastic block projection algorithms with extrapolation for convex feasibility problems
Necoara, I
OPTIMIZATION METHODS & SOFTWARE, 2022, 37 (05): : 1845 - 1875

← 1 2 3 4 5 →