109 citations · 579 across the 90 of their papers we have counts for
8 papers · 1 filter
Solving graph equipartition SDPs on an algebraic variety
Tianyun Tang, Kim-Chuan Toh
Semidefinite programs are generally challenging to solve due to their high dimensionality. Burer and Monteiro developed a non-convex approach to solve linear SDP problems by applyi…
DC algorithms for a class of sparse group regularized optimization problems
Wenjing Li, Wei Bian, Kim-Chuan Toh
In this paper, we consider a class of sparse group regularized optimization problems. Firstly, we give a continuous relaxation model of the considered problem and establis…
On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations
Hong T. M. Chu, Kim-Chuan Toh, Yangjing Zhang
Square-root (loss) regularized models have recently become popular in linear regression due to their nice statistical properties. Moreover, some of these models can be interpreted…
A Dimension Reduction Technique for Large-scale Structured Sparse Optimization Problems with Application to Convex Clustering
Yancheng Yuan, Tsung-Hui Chang, Defeng Sun +1
In this paper, we propose a novel adaptive sieving (AS) technique and an enhanced AS (EAS) technique, which are solver independent and could accelerate optimization algorithms for…
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…
Bregman Proximal Point Algorithm Revisited: A New Inexact Version and its Inertial Variant
Lei Yang, Kim-Chuan Toh
We study a general convex optimization problem, which covers various classic problems in different areas and particularly includes many optimal transport related problems arising i…
QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems
Ling Liang, Xudong Li, Defeng Sun +1
In this paper, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality and inequality constraints. In or…
Solving Challenging Large Scale QAPs
Koichi Fujii, Naoki Ito, Sunyoung Kim +3
We report our progress on the project for solving larger scale quadratic assignment problems (QAPs). Our main approach to solve large scale NP-hard combinatorial optimization probl…