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 103 articles , 1 standard article )

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  1. Chrétien, Stéphane; Clarkson, Paul: A fast algorithm for the semi-definite relaxation of the state estimation problem in power grids (2020)
  2. Buchheim, Christoph; Montenegro, Maribel; Wiegele, Angelika: SDP-based branch-and-bound for non-convex quadratic integer optimization (2019)
  3. Campos, Juan S.; Misener, Ruth; Parpas, Panos: A multilevel analysis of the Lasserre hierarchy (2019)
  4. Ling, Shuyang; Xu, Ruitu; Bandeira, Afonso S.: On the landscape of synchronization networks: a perspective from nonconvex optimization (2019)
  5. Nayak, Rupaj Kumar; Mohanty, Nirmalya Kumar: Improved row-by-row method for binary quadratic optimization problems (2019)
  6. Amini, Arash A.; Levina, Elizaveta: On semidefinite relaxations for the block model (2018)
  7. Bonami, Pierre; Günlük, Oktay; Linderoth, Jeff: Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods (2018)
  8. Huang, Wen; Hand, Paul: Blind deconvolution by a steepest descent algorithm on a quotient manifold (2018)
  9. Lourenço, Bruno F.; Fukuda, Ellen H.; Fukushima, Masao: Optimality conditions for nonlinear semidefinite programming via squared slack variables (2018)
  10. Park, Dohyung; Kyrillidis, Anastasios; Caramanis, Constantine; Sanghavi, Sujay: Finding low-rank solutions via nonconvex matrix factorization, efficiently and provably (2018)
  11. Vandaele, Arnaud; Glineur, François; Gillis, Nicolas: Algorithms for positive semidefinite factorization (2018)
  12. Zhang, Teng; Yang, Yi: Robust PCA by manifold optimization (2018)
  13. Boyd, Nicholas; Schiebinger, Geoffrey; Recht, Benjamin: The alternating descent conditional gradient method for sparse inverse problems (2017)
  14. Brockmeier, Austin J.; Mu, Tingting; Ananiadou, Sophia; Goulermas, John Y.: Quantifying the informativeness of similarity measurements (2017)
  15. Goldfarb, Donald; Mu, Cun; Wright, John; Zhou, Chaoxu: Using negative curvature in solving nonlinear programs (2017)
  16. Huang, Wen; Gallivan, K. A.; Zhang, Xiangxiong: Solving phaselift by low-rank Riemannian optimization methods for complex semidefinite constraints (2017)
  17. Lee, Timothy; Mitchell, John E.: Approximation algorithms from inexact solutions to semidefinite programming relaxations of combinatorial optimization problems (2017)
  18. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Exact worst-case performance of first-order methods for composite convex optimization (2017)
  19. Bhaskar, Sonia A.: Probabilistic low-rank matrix completion from quantized measurements (2016)
  20. Hu, Jiang; Jiang, Bo; Liu, Xin; Wen, ZaiWen: A note on semidefinite programming relaxations for polynomial optimization over a single sphere (2016)

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