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 84 articles , 1 standard article )

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  1. Boyd, Nicholas; Schiebinger, Geoffrey; Recht, Benjamin: The alternating descent conditional gradient method for sparse inverse problems (2017)
  2. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Exact worst-case performance of first-order methods for composite convex optimization (2017)
  3. Bhaskar, Sonia A.: Probabilistic low-rank matrix completion from quantized measurements (2016)
  4. Hu, Jiang; Jiang, Bo; Liu, Xin; Wen, ZaiWen: A note on semidefinite programming relaxations for polynomial optimization over a single sphere (2016)
  5. Lan, Guanghui; Monteiro, Renato D.C.: Iteration-complexity of first-order augmented Lagrangian methods for convex programming (2016)
  6. Mishra, Bamdev; Sepulchre, Rodolphe: Riemannian preconditioning (2016)
  7. Shtern, Shimrit; Ben-Tal, Aharon: Computational methods for solving nonconvex block-separable constrained quadratic problems (2016)
  8. Bahmani, Sohail; Romberg, Justin: Lifting for blind deconvolution in random mask imaging: identifiability and convex relaxation (2015)
  9. Chaudhury, K.N.; Khoo, Y.; Singer, A.: Global registration of multiple point clouds using semidefinite programming (2015)
  10. Zhu, Xiaojing: Computing the nearest low-rank correlation matrix by a simplified SQP algorithm (2015)
  11. Burer, Samuel; Kim, Sunyoung; Kojima, Masakazu: Faster, but weaker, relaxations for quadratically constrained quadratic programs (2014)
  12. Fages, Jean-Guillaume; Lapègue, Tanguy: Filtering AtMostNValue with difference constraints: application to the shift minimisation personnel task scheduling problem (2014)
  13. Huang, Aiqun; Xu, Chengxian: A trust region method for solving semidefinite programs (2013)
  14. Wen, Zaiwen; Yin, Wotao: A feasible method for optimization with orthogonality constraints (2013)
  15. Bomze, Immanuel M.; Grippo, Luigi; Palagi, Laura: Unconstrained formulation of standard quadratic optimization problems (2012)
  16. Grippo, Luigi; Palagi, Laura; Piacentini, Mauro; Piccialli, Veronica; Rinaldi, Giovanni: SpeeDP: an algorithm to compute SDP bounds for very large max-cut instances (2012)
  17. Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan: An implementable proximal point algorithmic framework for nuclear norm minimization (2012)
  18. Lu, Zhaosong; Zhang, Yong: An augmented Lagrangian approach for sparse principal component analysis (2012)
  19. Malick, Jér^ome; Roupin, Frédéric: Solving $k$-cluster problems to optimality with semidefinite programming (2012)
  20. Mitchell, John E.; Pang, Jong-Shi; Yu, Bin: Obtaining tighter relaxations of mathematical programs with complementarity constraints (2012)

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