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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

On the Local Convergence Analysis of the Gradient Sampling Method for Finite Max-Functions

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Author(s):
Helou, Elias Salomao [1] ; Santos, Sandra A. [2] ; Simoes, Lucas E. A. [2]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math Sci & Computat, Sao Carlos, SP - Brazil
[2] Univ Estadual Campinas, Dept Appl Math, Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS; v. 175, n. 1, p. 137-157, OCT 2017.
Web of Science Citations: 1
Abstract

The gradient sampling method is a recently developed tool for solving unconstrained nonsmooth optimization problems. Using just first-order information about the objective function, it generalizes the steepest descent method, one of the most classical methods for minimizing a smooth function. This study aims at determining under which circumstances one can expect the same local convergence result of the Cauchy method for the gradient sampling algorithm under the assumption that the problem is stated by a finite max-function around the optimal point. Additionally, at the end, we show how to practically accomplish the required hypotheses during the execution of the algorithm. (AU)

FAPESP's process: 13/05475-7 - Computational methods in optimization
Grantee:Sandra Augusta Santos
Support type: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/22989-2 - A sampling method for constrained nonsmooth optimization problems
Grantee:Lucas Eduardo Azevedo Simões
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 13/14615-7 - On the nonmonotone line search in gradient sampling methods for nonconvex and nonsmooth optimization
Grantee:Lucas Eduardo Azevedo Simões
Support type: Scholarships in Brazil - Doctorate