109 citations · 579 across the 90 of their papers we have counts for
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On the Asymptotic Superlinear Convergence of the Augmented Lagrangian Method for Semidefinite Programming with Multiple Solutions
Ying Cui, Defeng Sun, Kim-Chuan Toh
Solving large scale convex semidefinite programming (SDP) problems has long been a challenging task numerically. Fortunately, several powerful solvers including SDPNAL, SDPNAL+ and…
Max-Norm Optimization for Robust Matrix Recovery
Ethan X. Fang, Han Liu, Kim-Chuan Toh +1
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on…
A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems
Xudong Li, Defeng Sun, Kim-Chuan Toh
We develop a fast and robust algorithm for solving large scale convex composite optimization models with an emphasis on the -regularized least squares regression (Lasso) pr…
Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity
Tillmann Weisser, Jean-Bernard Lasserre, Kim-Chuan Toh
We provide a sparse version of the bounded degree SOS hierarchy BSOS [7] for polynomial optimization problems. It permits to treat large scale problems which satisfy a structured s…
Fast algorithms for large scale generalized distance weighted discrimination
Xin Yee Lam, J. S. Marron, Defeng Sun +1
High dimension low sample size statistical analysis is important in a wide range of applications. In such situations, the highly appealing discrimination method, support vector mac…