92 citations · 141 across the 13 of their papers we have counts for
13 papers
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,…
A new insight on augmented Lagrangian method with applications in machine learning
Jianchao Bai, Linyuan Jia, Zheng Peng
By exploiting double-penalty terms for the primal subproblem, we develop a novel relaxed augmented Lagrangian method for solving a family of convex optimization problems subject to…
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…
Convergence on a symmetric accelerated stochastic ADMM with larger stepsizes
Jianchao Bai, Deren Han, Hao Sun +1
In this paper, we develop a symmetric accelerated stochastic Alternating Direction Method of Multipliers (SAS-ADMM) for solving separable convex optimization problems with linear c…
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…
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…
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…
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…
On the generalized low rank approximation of the correlation matrices arising in the asset portfolio
Xuefeng Duan, Jianchao Bai, Maojun Zhang +1
In this paper, we consider the generalized low rank approximation of the correlation matrices problem which arises in the asset portfolio. We first characterize the feasible set by…
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…