109 citations · 565 across the 64 of their papers we have counts for
6 papers · 2 filters
A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization
Min Li, Defeng Sun, Kim-Chuan Toh
This paper presents a majorized alternating direction method of multipliers (ADMM) with indefinite proximal terms for solving linearly constrained -block convex composite optimi…
A Convergent -Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block
Min Li, Defeng Sun, Kim-Chuan Toh
In this paper, we present a semi-proximal alternating direction method of multipliers (ADMM) for solving -block separable convex minimization problems with the second block in t…
A Schur Complement Based Semi-Proximal ADMM for Convex Quadratic Conic Programming and Extensions
Xudong Li, Defeng Sun, Kim-Chuan Toh
This paper is devoted to the design of an efficient and convergent {semi-proximal} alternating direction method of multipliers (ADMM) for finding a solution of low to medium accura…
SDPNAL: A Majorized Semismooth Newton-CG Augmented Lagrangian Method for Semidefinite Programming with Nonnegative Constraints
Liuqin Yang, Defeng Sun, Kim-Chuan Toh
In this paper, we present a majorized semismooth Newton-CG augmented Lagrangian method, called SDPNAL, for semidefinite programming (SDP) with partial or full nonnegative constr…
A Convergent 3-Block Semi-Proximal Alternating Direction Method of Multipliers for Conic Programming with -Type of Constraints
Defeng Sun, Kim-Chuan Toh, Liuqin Yang
The objective of this paper is to design an efficient and convergent alternating direction method of multipliers (ADMM) for finding a solution of medium accuracy to conic programmi…
Spectral Operators of Matrices
Chao Ding, Defeng Sun, Jie Sun +1
The class of matrix optimization problems (MOPs) has been recognized in recent years to be a powerful tool by researchers far beyond the optimization community to model many import…