A Unified Analysis of Descent Sequences in Weakly Convex Optimization, Including Convergence Rates for Bundle Methods
Author:
Affiliation:
1. IMECC – Universidade Estadual de Campinas, Rua Sérgio Buarque DE Holanda 651, Campinas, SP 13083-859, Brazil.
2. IMPA – Instituto de Matemática Pura e Aplicada, Estrada Dona Castorina 110, Jardim Botânico, Rio de Janeiro, RJ 22460-320, Brazil.
Funder
Fundação de Amparo à Pesquisa do Estado de São Paulo
Conselho Nacional de Desenvolvimento Científico e Tecnológico
PRONEX-Optimization
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Subject
Theoretical Computer Science,Software,Applied Mathematics
Reference52 articles.
1. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods
2. The Equivalence of Three Types of Error Bounds for Weakly and Approximately Convex Functions
3. First-Order Methods in Optimization
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1. Linear Convergence of the Derivative-Free Proximal Bundle Method on Convex Nonsmooth Functions, with Application to the Derivative-Free $\mathcal{VU}$-Algorithm;Set-Valued and Variational Analysis;2024-05-20
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