109 citations · 579 across the 90 of their papers we have counts for
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QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming
Xudong Li, Defeng Sun, Kim-Chuan Toh
In this paper, we present a two-phase augmented Lagrangian method, called QSDPNAL, for solving convex quadratic semidefinite programming (QSDP) problems with constraints consisting…
A Note on the Convergence of ADMM for Linearly Constrained Convex Optimization Problems
Liang Chen, Defeng Sun, Kim-Chuan Toh
This note serves two purposes. Firstly, we construct a counterexample to show that the statement on the convergence of the alternating direction method of multipliers (ADMM) for so…
An Efficient Inexact Symmetric Gauss-Seidel Based Majorized ADMM for High-Dimensional Convex Composite Conic Programming
Liang Chen, Defeng Sun, Kim-Chuan Toh
In this paper, we propose an inexact multi-block ADMM-type first-order method for solving a class of high-dimensional convex composite conic optimization problems to moderate accur…
An Efficient Inexact ABCD Method for Least Squares Semidefinite Programming
Defeng Sun, Kim-Chuan Toh, Liuqin Yang
We consider least squares semidefinite programming (LSSDP) where the primal matrix variable must satisfy given linear equality and inequality constraints, and must also lie in the…
On the convergence properties of a majorized ADMM for linearly constrained convex optimization problems with coupled objective functions
Ying Cui, Xudong Li, Defeng Sun +1
In this paper, we establish the convergence properties for a majorized alternating direction method of multipliers (ADMM) for linearly constrained convex optimization problems whos…
A bounded degree SOS hierarchy for polynomial optimization
Jean-Bernard Lasserre, Toh Kim-Chuan, Yang Shouguang
We consider a new hierarchy of semidefinite relaxations for the general polynomial optimization problem on a compact basic semi-algebraic…