109 citations · 579 across the 90 of their papers we have counts for
10 papers · 2 filters
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…
A Unified Algorithmic Framework of Symmetric Gauss-Seidel Decomposition based Proximal ADMMs for Convex Composite Programming
Liang Chen, Defeng Sun, Kim-Chuan Toh +1
This paper aims to present a fairly accessible generalization of several symmetric Gauss-Seidel decomposition based multi-block proximal alternating direction methods of multiplier…
Best Nonnegative Rank-One Approximations of Tensors
Shenglong Hu, Defeng Sun, Kim-Chuan Toh
In this paper, we study the polynomial optimization problem of multi-forms over the intersection of the multi-spheres and the nonnegative orthants. This class of problems is NP-har…
Spectral operators of matrices: semismoothness and characterizations of the generalized Jacobian
Chao Ding, Defeng Sun, Jie Sun +1
Spectral operators of matrices proposed recently in [C. Ding, D.F. Sun, J. Sun, and K.C. Toh, Math. Program. {\bf 168}, 509--531 (2018)] are a class of matrix valued functions, whi…
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters
Lei Yang, Jia Li, Defeng Sun +1
We consider the problem of computing a Wasserstein barycenter for a set of discrete probability distributions with finite supports, which finds many applications in areas such as s…
BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems with Binary, Box and Complementarity Constraints
Naoki Ito, Sunyoung Kim, Masakazu Kojima +2
The software package BBCPOP is a MATLAB implementation of a hierarchy of sparse doubly nonnegative (DNN) relaxations of a class of polynomial optimization (minimization) problems (…
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method
Ziyan Luo, Defeng Sun, Kim-Chuan Toh +1
The octagonal shrinkage and clustering algorithm for regression (OSCAR), equipped with the -norm and a pair-wise -norm regularizer, is a useful tool for feat…
Computing the Best Approximation Over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone
Ying Cui, Defeng Sun, Kim-Chuan Toh
This paper introduces an efficient algorithm for computing the best approximation of a given matrix onto the intersection of linear equalities, inequalities and the doubly nonnegat…
On the Equivalence of Inexact Proximal ALM and ADMM for a Class of Convex Composite Programming
Liang Chen, Xudong Li, Defeng Sun +1
In this paper, we show that for a class of linearly constrained convex composite optimization problems, an (inexact) symmetric Gauss-Seidel based majorized multi-block proximal alt…
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…