SDPLR

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 92 articles , 1 standard article )

Showing results 1 to 20 of 92.
Sorted by year (citations)

1 2 3 4 5 next

  1. Amini, Arash A.; Levina, Elizaveta: On semidefinite relaxations for the block model (2018)
  2. Lourenço, Bruno F.; Fukuda, Ellen H.; Fukushima, Masao: Optimality conditions for nonlinear semidefinite programming via squared slack variables (2018)
  3. Vandaele, Arnaud; Glineur, François; Gillis, Nicolas: Algorithms for positive semidefinite factorization (2018)
  4. Boyd, Nicholas; Schiebinger, Geoffrey; Recht, Benjamin: The alternating descent conditional gradient method for sparse inverse problems (2017)
  5. Brockmeier, Austin J.; Mu, Tingting; Ananiadou, Sophia; Goulermas, John Y.: Quantifying the informativeness of similarity measurements (2017)
  6. Goldfarb, Donald; Mu, Cun; Wright, John; Zhou, Chaoxu: Using negative curvature in solving nonlinear programs (2017)
  7. Huang, Wen; Gallivan, K. A.; Zhang, Xiangxiong: Solving phaselift by low-rank Riemannian optimization methods for complex semidefinite constraints (2017)
  8. Taylor, Adrien B.; Hendrickx, Julien M.; Glineur, François: Exact worst-case performance of first-order methods for composite convex optimization (2017)
  9. Bhaskar, Sonia A.: Probabilistic low-rank matrix completion from quantized measurements (2016)
  10. Hu, Jiang; Jiang, Bo; Liu, Xin; Wen, ZaiWen: A note on semidefinite programming relaxations for polynomial optimization over a single sphere (2016)
  11. Lan, Guanghui; Monteiro, Renato D. C.: Iteration-complexity of first-order augmented Lagrangian methods for convex programming (2016)
  12. Mishra, Bamdev; Sepulchre, Rodolphe: Riemannian preconditioning (2016)
  13. Shtern, Shimrit; Ben-Tal, Aharon: Computational methods for solving nonconvex block-separable constrained quadratic problems (2016)
  14. Bahmani, Sohail; Romberg, Justin: Lifting for blind deconvolution in random mask imaging: identifiability and convex relaxation (2015)
  15. Chaudhury, K. N.; Khoo, Y.; Singer, A.: Global registration of multiple point clouds using semidefinite programming (2015)
  16. Li, Wenye: Visualizing network communities with a semi-definite programming method (2015)
  17. Schneider, Reinhold; Uschmajew, André: Convergence results for projected line-search methods on varieties of low-rank matrices via \Lojasiewiczinequality (2015)
  18. Zhu, Xiaojing: Computing the nearest low-rank correlation matrix by a simplified SQP algorithm (2015)
  19. Burer, Samuel; Kim, Sunyoung; Kojima, Masakazu: Faster, but weaker, relaxations for quadratically constrained quadratic programs (2014)
  20. Fages, Jean-Guillaume; Lapègue, Tanguy: Filtering AtMostNValue with difference constraints: application to the shift minimisation personnel task scheduling problem (2014)

1 2 3 4 5 next