3 papers
math.OC2021
An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization
Heng Yang, Ling Liang, Luca Carlone +1
We consider solving high-order semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solution…
math.OC2020
An Inexact Augmented Lagrangian Method for Second-order Cone Programming with Applications
Ling Liang, Defeng Sun, Kim-Chuan Toh
In this paper, we adopt the augmented Lagrangian method (ALM) to solve convex quadratic second-order cone programming problems (SOCPs). Fruitful results on the efficiency of the AL…
math.OC2018
A New Homotopy Proximal Variable-Metric Framework for Composite Convex Minimization
Quoc Tran-Dinh, Liang Ling, Kim-Chuan Toh
This paper suggests two novel ideas to develop new proximal variable-metric methods for solving a class of composite convex optimization problems. The first idea is a new parameter…