579 citations · 698 across the 20 of their papers we have counts for
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A New Perspective on Clustering: A Mixed-norm Model and its Solution by Progressive Integer Programming
Junyi Liu, Yulin Peng, Yao Xie +2
Extending the classical -means and -medians models, this paper introduces an mixed-norm clustering model where the centroid updates and cluster assignments are u…
HOT: An Efficient Halpern Accelerating Algorithm for Optimal Transport Problems
Guojun Zhang, Zhexuan Gu, Yancheng Yuan +1
This paper proposes an efficient HOT algorithm for solving the optimal transport (OT) problems with finite supports. We particularly focus on an efficient implementation of the HOT…
Accelerating preconditioned ADMM via degenerate proximal point mappings
Defeng Sun, Yancheng Yuan, Guojun Zhang +1
In this paper, we aim to accelerate a preconditioned alternating direction method of multipliers (pADMM), whose proximal terms are convex quadratic functions, for solving linearly…
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…
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 Efficient HPR Algorithm for the Wasserstein Barycenter Problem with Computational Complexity
Guojun Zhang, Yancheng Yuan, Defeng Sun
In this paper, we propose and analyze an efficient Halpern-Peaceman-Rachford (HPR) algorithm for solving the Wasserstein barycenter problem (WBP) with fixed supports. While the Pea…
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…
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…
A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
Meixia Lin, Defeng Sun, Kim-Chuan Toh +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regulari…
An Efficient Semismooth Newton Based Algorithm for Convex Clustering
Yancheng Yuan, Defeng Sun, Kim-Chuan Toh
Clustering may be the most fundamental problem in unsupervised learning which is still active in machine learning research because its importance in many applications. Popular meth…
SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0)
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan +1
SDPNAL+ is a {\sc Matlab} software package that implements an augmented Lagrangian based method to solve large scale semidefinite programming problems with bound constraints. The i…