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.


References in zbMATH (referenced in 44 articles , 1 standard article )

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  1. Lu, Si-Tong; Zhang, Miao; Li, Qing-Na: Feasibility and a fast algorithm for Euclidean distance matrix optimization with ordinal constraints (2020)
  2. Qian, Xun; Liao, Li-Zhi; Sun, Jie: A strategy of global convergence for the affine scaling algorithm for convex semidefinite programming (2020)
  3. Zhai, Fengzhen; Li, Qingna: A Euclidean distance matrix model for protein molecular conformation (2020)
  4. Xu, Yi; Yan, Xihong: On a box-constrained linear symmetric cone optimization problem (2019)
  5. Chen, Shuang; Pang, Li-Ping; Lv, Jian; Xia, Zun-Quan: Inexact SA method for constrained stochastic convex SDP and application in Chinese stock market (2018)
  6. Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming (2018)
  7. Yu, Panpan; Li, Qingna: Ordinal distance metric learning with MDS for image ranking (2018)
  8. Chen, Liang; Sun, Defeng; Toh, Kim-Chuan: An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming (2017)
  9. Wang, Chengjing; Tang, Peipei: A primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming (2017)
  10. Wang, Chengjing: On how to solve large-scale log-determinant optimization problems (2016)
  11. Achache, Mohamed; Guerra, Loubna: A full Nesterov-Todd-step feasible primal-dual interior point algorithm for convex quadratic semi-definite optimization (2014)
  12. Kheirfam, B.; Hasani, F.: A large-update feasible interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function (2013)
  13. 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)
  14. Jiang, Kaifeng; Sun, Defeng; Toh, Kim-Chuan: An inexact accelerated proximal gradient method for large scale linearly constrained convex SDP (2012)
  15. Lin, Huiling: An inexact spectral bundle method for convex quadratic semidefinite programming (2012)
  16. Malick, Jérôme; Roupin, Frédéric: Solving (k)-cluster problems to optimality with semidefinite programming (2012)
  17. He, Bingsheng; Xu, Minghua; Yuan, Xiaoming: Solving large-scale least squares semidefinite programming by alternating direction methods (2011)
  18. Li, Lu; Toh, Kim-Chuan: A polynomial-time inexact primal-dual infeasible path-following algorithm for convex quadratic SDP (2011)
  19. Li, Qingna; Qi, Hou-Duo: A sequential semismooth Newton method for the nearest low-rank correlation matrix problem (2011)
  20. Li, Qingna; Qi, Houduo; Xiu, Naihua: Block relaxation and majorization methods for the nearest correlation matrix with factor structure (2011)

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