Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/340094
Type: Artigo
Title: A derivative-free -algorithm for convex finite-max problems
Author: Hare, Warren; Planiden, Chayne; Sagastizabal, Claudia
Abstract: The -algorithm is a superlinearly convergent method for minimizing nonsmooth, convex functions. At each iteration, the algorithm works with a certain -space and its orthogonal -space, such that the nonsmoothness of the objective function is concentrated on its projection onto the -space, and on the -space the projection is smooth. This structure allows for an alternation between a Newton-like step where the function is smooth, and a proximal-point step that is used to find iterates with promising -decompositions. We establish a derivative-free variant of the -algorithm for convex finite-max objective functions. We show global convergence and provide numerical results from a proof-of-concept implementation, which demonstrates the feasibility and practical value of the approach. We also carry out some tests using nonconvex functions and discuss the results
Subject: Otimização sem derivadas
Country: Reino Unido
Editor: Taylor & Francis
Rights: Fechado
Identifier DOI: 10.1080/10556788.2019.1668944
Address: https://www.tandfonline.com/doi/full/10.1080/10556788.2019.1668944
Date Issue: 2019
Appears in Collections:IMECC - Artigos e Outros Documentos

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