QSDP
This software is designed to solve a convex quadratic semide¯nite programming(QSDP) problem, possibly with a log-determinant term. It employs an infeasible primal-dual predictor-corrector path-following method using the Nesterov-Todd search direction. The basic code is written in Matlab, but key subroutines in Care incorporated via Mex interface. It also uses functions in the software for linear semide¯nite programming, SDPT3-3.1. Here we brie°y describe how to install and run QSDP-0. We should emphasize that the current version is an experimental software and it is not intended to be a general purpose solver. Some numerical results are presented to illustrate the performance of the software on QSDPs arising from the nearest correlation matrix and the Euclidean distance matrix completion problems.
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References in zbMATH (referenced in 34 articles , 1 standard article )
Showing results 1 to 20 of 34.
Sorted by year (- Wang, Chengjing: On how to solve large-scale log-determinant optimization problems (2016)
- Kheirfam, B.; Hasani, F.: A large-update feasible interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function (2013)
- Wang, G.Q.; Yu, C.J.; Teo, K.L.: A new full Nesterov-Todd step feasible interior-point method for convex quadratic symmetric cone optimization (2013)
- Jiang, Kaifeng; Sun, Defeng; Toh, Kim-Chuan: An inexact accelerated proximal gradient method for large scale linearly constrained convex SDP (2012)
- Lin, Huiling: An inexact spectral bundle method for convex quadratic semidefinite programming (2012)
- Malick, Jér^ome; Roupin, Frédéric: Solving $k$-cluster problems to optimality with semidefinite programming (2012)
- He, Bingsheng; Xu, Minghua; Yuan, Xiaoming: Solving large-scale least squares semidefinite programming by alternating direction methods (2011)
- Li, Lu; Toh, Kim-Chuan: A polynomial-time inexact primal-dual infeasible path-following algorithm for convex quadratic SDP (2011)
- Li, Qingna; Qi, Hou-Duo: A sequential semismooth Newton method for the nearest low-rank correlation matrix problem (2011)
- Li, Qingna; Qi, Houduo; Xiu, Naihua: Block relaxation and majorization methods for the nearest correlation matrix with factor structure (2011)
- Qi, Houduo; Sun, Defeng: An augmented Lagrangian dual approach for the $H$-weighted nearest correlation matrix problem (2011)
- Wang, Guoqiang; Zhu, Detong: A unified kernel function approach to primal-dual interior-point algorithms for convex quadratic SDO (2011)
- Wang, Yingnan; Xiu, Naihua; Luo, Ziyan: A regularized strong duality for nonsymmetric semidefinite least squares problem (2011)
- Bai, Y.Q.; Wang, F.Y.; Luo, X.W.: A polynomial-time interior-point algorithm for convex quadratic semidefinite optimization (2010)
- Borsdorf, Rüdiger; Higham, Nicholas J.: A preconditioned Newton algorithm for the nearest correlation matrix (2010)
- Gao, Yan; Sun, Defeng: Calibrating least squares semidefinite programming with equality and inequality constraints (2010)
- Li, Lu; Toh, Kim-Chuan: A polynomial-time inexact interior-point method for convex quadratic symmetric cone programming (2010)
- Li, Lu; Toh, Kim-Chuan: An inexact interior point method for $L_1$-regularized sparse covariance selection (2010)
- Li, Qingna; Li, Donghui; Qi, Houduo: Newton’s method for computing the nearest correlation matrix with a simple upper bound (2010)
- Qi, Houduo; Sun, Defeng: Correlation stress testing for value-at-risk: an unconstrained convex optimization approach (2010)