3 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
An Accelerated Stochastic ADMM for Nonconvex and Nonsmooth Finite-Sum Optimization
Yuxuan Zeng, Zhiguo Wang, Jianchao Bai +1
The nonconvex and nonsmooth finite-sum optimization problem with linear constraint has attracted much attention in the fields of artificial intelligence, computer, and mathematics,…
An Inexact Accelerated Stochastic ADMM for Separable Convex Optimization
Jianchao Bai, William W. Hager, Hongchao Zhang
An inexact accelerated stochastic Alternating Direction Method of Multipliers (AS-ADMM) scheme is developed for solving structured separable convex optimization problems with linea…
Generalized Symmetric ADMM for Separable Convex Optimization
Jianchao Bai, Jicheng Li, Fengmin Xu +1
The Alternating Direction Method of Multipliers (ADMM) has been proved to be effective for solving separable convex optimization subject to linear constraints. In this paper, we pr…
General parameterized proximal point algorithm with applications in statistical learning
Jianchao Bai, Jicheng Li, Pingfan Dai +1
In the literature, there are a few researches to design some parameters in the Proximal Point Algorithm (PPA), especially for the multi-objective convex optimizations. Introducing…
A parameterized proximal point algorithm for separable convex optimization
Jianchao Bai, Hongchao Zhang, Jicheng Li
In this paper, we develop a parameterized proximal point algorithm (P-PPA) for solving a class of separable convex programming problems subject to linear and convex constraints. Th…
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…