7 citations · 26 across the 14 of their papers we have counts for
30 papers · 1 filter
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…
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…
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…
Adaptive Sieving with PPDNA: Generating Solution Paths of Exclusive Lasso Models
Meixia Lin, Yancheng Yuan, Defeng Sun +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on structured sparsity. Its complex nature poses diffic…
Estimation of sparse Gaussian graphical models with hidden clustering structure
Meixia Lin, Defeng Sun, Kim-Chuan Toh +1
Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph. The sparsity and clust…