5 papers · 1 filter
Parameter-Free Adaptive Extragradient Methods for Generalized-Smooth Variational Inequalities
Zhaoyang Chu, Hongwei Liu, Yakui Huang
Variational inequalities (VIs) provide a unified framework for optimization, saddle-point, equilibrium, and learning problems, where operators need not be globally Lipschitz. The $…
A new subspace minimization conjugate gradient method for unconstrained minimization
Zexian Liu, Yan Ni, Hongwei Liu +1
Subspace minimization conjugate gradient (SMCG) methods have become a class of quite efficient iterative methods for unconstrained optimization and have attracted extensive attenti…
A Regularized Limited Memory Subspace Minimization Conjugate Gradient Method for Unconstrained Optimization
Wumei Sun, Hongwei Liu, Zexian Liu
In this paper, based on the limited memory techniques and subspace minimization conjugate gradient (SMCG) methods, a regularized limited memory subspace minimization conjugate grad…
Convergence Rate of Inertial Forward-Backward Algorithms Based on the Local Error Bound Condition
Hongwei Liu, Ting Wang, Zexian Liu
The "Inertial Forward-Backward algorithm" (IFB) is a powerful tool for convex nonsmooth minimization problems, it gives the well known "fast iterative shrinkage-thresholding algori…
Two efficient gradient methods with approximately optimal stepsizes based on regularization models for unconstrained optimization
Zexian Liu, Wangli Chu, Hongwei Liu
It is widely accepted that the stepsize is of great significance to gradient method. Two efficient gradient methods with approximately optimal stepsizes mainly based on regularizat…