A dual Bregman proximal gradient method for relatively-strongly convex optimization

DOI10.3934/naco.2021028zbMath1497.90150OpenAlexW3184536587MaRDI QIDQ2092292

Jin-Zan Liu, Xin-Wei Liu

Publication date: 2 November 2022

Published in: Numerical Algebra, Control and Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/naco.2021028

zbMATH Keywords

Bregman distancedual methodrelative smoothnessrelatively strong convexity

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Optimality conditions and duality in mathematical programming (90C46)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• A fast dual proximal-gradient method for separable convex optimization with linear coupled constraints
• On the weak convergence of an ergodic iteration for the solution of variational inequalities for monotone operators in Hilbert space
• A simplified view of first order methods for optimization
• A fast dual proximal gradient algorithm for convex minimization and applications
• Accelerated Bregman proximal gradient methods for relatively smooth convex optimization
• Convergence Analysis of a Proximal-Like Minimization Algorithm Using Bregman Functions
• Optimum Designs in Regression Problems
• Image deblurring with Poisson data: from cells to galaxies
• Splitting Algorithms for the Sum of Two Nonlinear Operators
• A generalized proximal point algorithm for certain non-convex minimization problems
• First-Order Methods in Optimization
• Relatively Smooth Convex Optimization by First-Order Methods, and Applications
• A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications

This page was built for publication: A dual Bregman proximal gradient method for relatively-strongly convex optimization