Proximal-like incremental aggregated gradient method with Bregman distance in weakly convex optimization problems

DOI10.1007/s10898-021-01044-9zbMath1475.90067OpenAlexW3170531556MaRDI QIDQ2046311

Jieru Huang, Zehui Jia, Xing-Ju Cai

Publication date: 17 August 2021

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-021-01044-9

zbMATH Keywords

Bregman distanceweakly convexKL propertyextended descent lemmaproximal-like incremental aggregated gradient algorithms

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26)

Related Items (1)

Inertial proximal incremental aggregated gradient method with linear convergence guarantees

Cites Work

• Unnamed Item
• Unnamed Item
• An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions
• Proximal alternating linearized minimization for nonconvex and nonsmooth problems
• On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
• On gradients of functions definable in o-minimal structures
• An inexact proximal gradient algorithm with extrapolation for a class of nonconvex nonsmooth optimization problems
• Nonconvex proximal incremental aggregated gradient method with linear convergence
• An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
• Convergence Analysis of a Proximal-Like Minimization Algorithm Using Bregman Functions
• Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
• Splitting Algorithms for the Sum of Two Nonlinear Operators
• Variational Analysis
• First Order Methods Beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems
• Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods
• Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence Under Bregman Distance Growth Conditions
• Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
• A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications

This page was built for publication: Proximal-like incremental aggregated gradient method with Bregman distance in weakly convex optimization problems