1 citations · 1 across the 9 of their papers we have counts for
9 papers
A New Primal-Dual Algorithm with Convex Combination and Extrapolation for Convex-Concave Saddle Point Problems with Nonlinear Coupling Term
Jialong Li, Zexian Liu, Xiaokai Chang
Convex-concave saddle point problems with nonlinear coupling term have wide applications in signal processing, machine learning, robust optimization, and generative models, among o…
A unified framework for inexact adaptive stepsizes in the gradient methods, the conjugate gradient methods and the quasi-Newton methods for strictly convex quadratic optimization
Zexian Liu
The inexact adaptive stepsizes for the conjugate gradient method and the quasi-Newton method are very rare. The exact stepsizes in the gradient method, the conjugate gradient metho…
On the properties of the linear conjugate gradient method
Zexian Liu, Qiao Li
The linear conjugate gradient method is an efficient iterative method for the convex quadratic minimization problems $ \mathop {\min }\limits_{x \in { \mathbb R^n}} f(x) =\dfrac{1}…
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…
New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization
Ting Zhao, Hongwei Liu, Zexian Liu
In this paper, two new subspace minimization conjugate gradient methods based on regularization models are proposed, where a special scaled norm in regularization model…
An Improved Gradient Method with Approximately Optimal Stepsize Based on Conic model for Unconstrained Optimization
Zexian Liu, Hongwei Liu
A new type of stepsize, which was recently introduced by Liu and Liu (Optimization, 67(3), 427-440, 2018), is called approximately optimal stepsize and is quit efficient for gradie…
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…