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.
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References in zbMATH (referenced in 87 articles , 1 standard article )
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- Bhaskar, Sonia A.: Probabilistic low-rank matrix completion from quantized measurements (2016)
- 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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- Schneider, Reinhold; Uschmajew, André: Convergence results for projected line-search methods on varieties of low-rank matrices via \Lojasiewiczinequality (2015)
- Zhu, Xiaojing: Computing the nearest low-rank correlation matrix by a simplified SQP algorithm (2015)
- Burer, Samuel; Kim, Sunyoung; Kojima, Masakazu: Faster, but weaker, relaxations for quadratically constrained quadratic programs (2014)
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- Grippo, Luigi; Palagi, Laura; Piacentini, Mauro; Piccialli, Veronica; Rinaldi, Giovanni: SpeeDP: an algorithm to compute SDP bounds for very large max-cut instances (2012)
- Liu, Yong-Jin; Sun, Defeng; Toh, Kim-Chuan: An implementable proximal point algorithmic framework for nuclear norm minimization (2012)