Inertial proximal gradient methods with Bregman regularization for a class of nonconvex optimization problems
- 19 August 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Journal of Global Optimization
- Vol. 79 (3), 617-644
- https://doi.org/10.1007/s10898-020-00943-7
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (11771078)
- Natural Science Foundation of Jiangsu Province (BK20181258)
- National Research Foundation Singapore (NRF-RSS2016-004)
- Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology
This publication has 45 references indexed in Scilit:
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methodsMathematical Programming, 2011
- Nearly unbiased variable selection under minimax concave penaltyThe Annals of Statistics, 2010
- A Singular Value Thresholding Algorithm for Matrix CompletionSIAM Journal on Optimization, 2010
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed SensingSIAM Journal on Imaging Sciences, 2008
- A generalized proximal-point-based prediction–correction method for variational inequality problemsJournal of Computational and Applied Mathematics, 2007
- Projected subgradient methods with non-Euclidean distances for non-differentiable convex minimization and variational inequalitiesMathematical Programming, 2007
- Interior Gradient and Proximal Methods for Convex and Conic OptimizationSIAM Journal on Optimization, 2006
- An Iterative Regularization Method for Total Variation-Based Image RestorationMultiscale Modeling & Simulation, 2005
- Convergence of a splitting inertial proximal method for monotone operatorsJournal of Computational and Applied Mathematics, 2003
- Some methods of speeding up the convergence of iteration methodsUSSR Computational Mathematics and Mathematical Physics, 1964