CSDP

CSDP, A C Library for Semidefinite Programming. This is the project page for the CSDP project of COIN-OR. CSDP is a library of routines that implements a predictor corrector variant of the semidefinite programming algorithm of Helmberg, Rendl, Vanderbei, and Wolkowicz. The main advantages of this code are that it is written to be used as a callable subroutine, it is written in C for efficiency, the code runs in parallel on shared memory multi-processor systems, and it makes effective use of sparsity in the constraint matrices. CSDP has been compiled on many different systems. The code should work on any system with an ANSI C Compiler and BLAS/LAPACK libraries.


References in zbMATH (referenced in 194 articles , 2 standard articles )

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  1. Anjos, Miguel F.; Neto, José: A class of spectral bounds for max (k)-cut (2020)
  2. Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
  3. Cifuentes, Diego; Kahle, Thomas; Parrilo, Pablo: Sums of squares in Macaulay2 (2020)
  4. de Laat, David: Moment methods in energy minimization: new bounds for Riesz minimal energy problems (2020)
  5. Pei, Zhi; Wan, Mingzhong; Wang, Ziteng: A new approximation algorithm for unrelated parallel machine scheduling with release dates (2020)
  6. Anjos, Miguel F.; Neto, José: Spectral bounds for graph partitioning with prescribed partition sizes (2019)
  7. Bennett, Patrick; Dudek, Andrzej; Lidický, Bernard; Pikhurko, Oleg: Minimizing the number of 5-cycles in graphs with given edge-density (2019)
  8. Buchheim, Christoph; Montenegro, Maribel; Wiegele, Angelika: SDP-based branch-and-bound for non-convex quadratic integer optimization (2019)
  9. Coregliano, Leonardo N.; Parente, Roberto F.; Sato, Cristiane M.: On the maximum density of fixed strongly connected subtournaments (2019)
  10. Elloumi, Sourour; Lambert, Amélie: Global solution of non-convex quadratically constrained quadratic programs (2019)
  11. Grzesik, Andrzej; Hu, Ping; Volec, Jan: Minimum number of edges that occur in odd cycles (2019)
  12. Kumagai, Atsuya: Spectral dimensionality reduction for Bregman information (2019)
  13. Liuzzi, G.; Locatelli, M.; Piccialli, Veronica: A new branch-and-bound algorithm for standard quadratic programming problems (2019)
  14. Neto, José; Ben-Ameur, Walid: On fractional cut covers (2019)
  15. Papp, Dávid; Yildiz, Sercan: Sum-of-squares optimization without semidefinite programming (2019)
  16. Behrends, Sönke; Hübner, Ruth; Schöbel, Anita: Norm bounds and underestimators for unconstrained polynomial integer minimization (2018)
  17. Roux, Pierre; Iguernlala, Mohamed; Conchon, Sylvain: A non-linear arithmetic procedure for control-command software verification (2018)
  18. Roux, Pierre; Voronin, Yuen-Lam; Sankaranarayanan, Sriram: Validating numerical semidefinite programming solvers for polynomial invariants (2018)
  19. Adjé, Assalé; Garoche, Pierre-Loïc; Magron, Victor: A sums-of-squares extension of policy iterations (2017)
  20. Arima, Naohiko; Kim, Sunyoung; Kojima, Masakazu; Toh, Kim-Chuan: A robust Lagrangian-DNN method for a class of quadratic optimization problems (2017)

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