SDPLR is an ANSI C package developed S. Burer, C. Choi and R.D.C. Monteiro for solving general semidefinite programs (SDPs) using a nonlinear, first-order algorithm that is based on the idea of low-rank factorization. A specialized version of SDPLR is also available for solving specially structured semidefinite programs (SDPs) such as the MaxCut SDP, the Minimum Bisection SDP, and the (unweighted) Lovasz Theta SDP. The details of the algorithm used by SDPLR can be found in the technical report ”A Nonlinear Programming Algorithm for Semidefinite Programs via Low-rank Factorization” written by S. Burer and R.D.C. Monteiro.

References in zbMATH (referenced in 147 articles , 1 standard article )

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  1. Assoweh, Mohamed Ibrahim; Chrétien, Stéphane; Tamadazte, Brahim: Low tubal rank tensor recovery using the Bürer-Monteiro factorisation approach. Application to optical coherence tomography (2022)
  2. Blekherman, Grigoriy; Sinn, Rainer; Smith, Gregory G.; Velasco, Mauricio: Sums of squares and quadratic persistence on real projective varieties (2022)
  3. Fanuel, Michaël; Aspeel, Antoine; Delvenne, Jean-Charles; Suykens, Johan A. K.: Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions (2022)
  4. Ling, Shuyang: Improved performance guarantees for orthogonal group synchronization via generalized power method (2022)
  5. Nishijima, Mitsuhiro; Nakata, Kazuhide: A block coordinate descent method for sensor network localization (2022)
  6. Souto, Mario; Garcia, Joaquim D.; Veiga, Álvaro: Exploiting low-rank structure in semidefinite programming by approximate operator splitting (2022)
  7. Wang, Alex L.; Kılınç-Karzan, Fatma: On the tightness of SDP relaxations of QCQPs (2022)
  8. Xu, Hang; Li, Song; Lin, Junhong: Low rank matrix recovery with adversarial sparse noise (2022)
  9. Bellavia, Stefania; Gondzio, Jacek; Porcelli, Margherita: A relaxed interior point method for low-rank semidefinite programming problems with applications to matrix completion (2021)
  10. Cifuentes, Diego: A convex relaxation to compute the nearest structured rank deficient matrix (2021)
  11. Cifuentes, Diego: On the Burer-Monteiro method for general semidefinite programs (2021)
  12. Cosse, Augustin; Demanet, Laurent: Stable rank-one matrix completion is solved by the level (2) Lasserre relaxation (2021)
  13. Ding, Lijun; Udell, Madeleine: On the simplicity and conditioning of low rank semidefinite programs (2021)
  14. Ding, Lijun; Yurtsever, Alp; Cevher, Volkan; Tropp, Joel A.; Udell, Madeleine: An optimal-storage approach to semidefinite programming using approximate complementarity (2021)
  15. Hrga, Timotej; Povh, Janez: \textttMADAM: a parallel exact solver for max-cut based on semidefinite programming and ADMM (2021)
  16. Im, Jiyoung; Wolkowicz, Henry: A strengthened Barvinok-Pataki bound on SDP rank (2021)
  17. Kueng, Richard; Tropp, Joel A.: Binary component decomposition. I: The positive-semidefinite case (2021)
  18. Li, Ji; Cai, Jian-Feng; Zhao, Hongkai: Scalable incremental nonconvex optimization approach for phase retrieval (2021)
  19. Rosen, David M.: Scalable low-rank semidefinite programming for certifiably correct machine perception (2021)
  20. Sagan, April; Mitchell, John E.: Low-rank factorization for rank minimization with nonconvex regularizers (2021)

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