1 citations · 1 across the 10 of their papers we have counts for
10 papers
Averaged proximal reflected gradient method for monotone variational inequalities
Xiaokai Chang, Jialin Li, Jun Yang
Projected reflected gradient (PRG) method proposed by Malitsky is efficient for solving monotone variational inequality (MVI), while the existing upper bound of step size is not ti…
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…
Sharper Bounds on Four Lattice Constants
Jinming Wen, Xiao-Wen Chang
The Korkine--Zolotareff (KZ) reduction, and its generalisations, are widely used lattice reduction strategies in communications and cryptography. The KZ constant and Schnorr's cons…
Golden ratio primal-dual algorithm with linesearch
Xiaokai Chang, Junfeng Yang, Hongchao Zhang
Golden ratio primal-dual algorithm (GRPDA) is a new variant of the classical Arrow-Hurwicz method for solving structured convex optimization problem, in which the objective functio…
A golden ratio primal-dual algorithm for structured convex optimization
Xiaokai Chang, Junfeng Yang
We design, analyze and test a golden ratio primal-dual algorithm (GRPDA) for solving structured convex optimization problem, where the objective function is the sum of two closed p…
A family of multi-parameterized proximal point algorithms
Jianchao Bai, Ke Guo, Xiaokai Chang
In this paper, a multi-parameterized proximal point algorithm combining with a relaxation step is developed for solving convex minimization problem subject to linear constraints. W…
Convergence Revisit on Generalized Symmetric ADMM
Jianchao Bai, Xiaokai Chang, Jicheng Li +1
In this note, we show a sublinear nonergodic convergence rate for the algorithm developed in [Bai, et al. Generalized symmetric ADMM for separable convex optimization. Comput. Opti…
First-order primal-dual algorithm with correction
Xiaokai Chang, Sanyang Liu
This paper is devoted to the design of efficient primal-dual algorithm (PDA) for solving convex optimization problems with known saddle-point structure. We present a new PDA with l…
Proximal extrapolated gradient methods with prediction and correction for monotone variational inequalities
Xiaokai Chang, Sanyang Liu, Jianchao Bai +1
An efficient proximal-gradient-based method, called proximal extrapolated gradient method, is designed for solving monotone variational inequality in Hilbert space. The proposed me…
A three-operator splitting perspective of a three-block ADMM for convex quadratic semidefinite programming and extensions
Xiaokai Chang, Liang Chen, Sanyang Liu
In recent years, several convergent multi-block variants of the alternating direction method of multipliers (ADMM) have been proposed for solving the convex quadratic semidefinite…