activity
20182021
most citedAccelerated Symmetric ADMM and Its Applications in Signal Processing

3 citations · 3 across the 4 of their papers we have counts for

collaborators

9 papers

math.NA2021

Iteration complexity analysis of a partial LQP-based alternating direction method of multipliers

Jianchao Bai, Yuxue Ma, Hao Sun +1

In this paper, we consider a prototypical convex optimization problem with multi-block variables and separable structures. By adding the Logarithmic Quadratic Proximal (LQP) regula…

math.OC2020

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…

math.NA2019

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…

math.NA20193 cited

Accelerated Symmetric ADMM and Its Applications in Signal Processing

Jianchao Bai, Junli Liang, Ke Guo +1

The alternating direction method of multipliers (ADMM) were extensively investigated in the past decades for solving separable convex optimization problems. Fewer researchers focus…

math.NA2019

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…

math.OC2018

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…