109 citations · 565 across the 64 of their papers we have counts for
6 papers · 2 filters
An efficient sieving based secant method for sparse optimization problems with least-squares constraints
Qian Li, Defeng Sun, Yancheng Yuan
In this paper, we propose an efficient sieving based secant method to address the computational challenges of solving sparse optimization problems with least-squares constraints. A…
Quantifying low rank approximations of third order symmetric tensors
Shenglong Hu, Defeng Sun, Kim-Chuan Toh
In this paper, we present a method to certify the approximation quality of a low rank tensor to a given third order symmetric tensor. Under mild assumptions, best low rank approxim…
A Highly Efficient Algorithm for Solving Exclusive Lasso Problems
Meixia Lin, Yancheng Yuan, Defeng Sun +1
The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses…
Adaptive sieving: A dimension reduction technique for sparse optimization problems
Yancheng Yuan, Meixia Lin, Defeng Sun +1
In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, whe…
An Accelerated Proximal Alternating Direction Method of Multipliers for Optimal Decentralized Control of Uncertain Systems
Bo Yang, Xinyuan Zhao, Xudong Li +1
To ensure the system stability of the -guaranteed cost optimal decentralized control problem (ODC), an approximate semidefinite programming (SDP) problem is f…
A squared smoothing Newton method for semidefinite programming
Ling Liang, Defeng Sun, Kim-Chuan Toh
This paper proposes a squared smoothing Newton method via the Huber smoothing function for solving semidefinite programming problems (SDPs). We first study the fundamental properti…