7 citations · 9 across the 5 of their papers we have counts for
5 papers
A Newton-bracketing method for a simple conic optimization problem
Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh
For the Lagrangian-DNN relaxation of quadratic optimization problems (QOPs), we propose a Newton-bracketing method to improve the performance of the bisection-projection method imp…
On the R-superlinear convergence of the KKT residues generated by the augmented Lagrangian method for convex composite conic programming
Ying Cui, Defeng Sun, Kim-Chuan Toh
Due to the possible lack of primal-dual-type error bounds, the superlinear convergence for the Karush-Kuhn-Tucker (KKT) residues of the sequence generated by augmented Lagrangian m…
On efficiently solving the subproblems of a level-set method for fused lasso problems
Xudong Li, Defeng Sun, Kim-Chuan Toh
In applying the level-set method developed in [Van den Berg and Friedlander, SIAM J. on Scientific Computing, 31 (2008), pp.~890--912 and SIAM J. on Optimization, 21 (2011), pp.~12…
An Efficient Inexact ABCD Method for Least Squares Semidefinite Programming
Defeng Sun, Kim-Chuan Toh, Liuqin Yang
We consider least squares semidefinite programming (LSSDP) where the primal matrix variable must satisfy given linear equality and inequality constraints, and must also lie in the…
A proximal point algorithm for sequential feature extraction applications
Xuan Vinh Doan, Kim-Chuan Toh, Stephen Vavasis
We propose a proximal point algorithm to solve LAROS problem, that is the problem of finding a "large approximately rank-one submatrix". This LAROS problem is used to sequentially…