Algorithm 875: DSDP5--software for semidefinite programming. DSDP implements the dual-scaling algorithm for semidefinite programming. The source code for this interior-point algorithm, written entirely in ANSI C, is freely available under an open source license. The solver can be used as a subroutine library, as a function within the Matlab environment, or as an executable that reads and writes to data files. Initiated in 1997, DSDP has developed into an efficient and robust general-purpose solver for semidefinite programming. Its features include a convergence proof with polynomially bounded worst-case complexity, primal and dual feasible solutions when they exist, certificates of infeasibility when solutions do not exist, initial points that can be feasible or infeasible, relatively low memory requirements for an interior-point method, sparse and low-rank data structures, extensibility that allows applications to customize the solver and improve its performance, a subroutine library that enables it to be linked to larger applications, scalable performance for large problems on parallel architectures, and a well-documented interface and examples of its use. The package has been used in many applications and tested for efficiency, robustness, and ease of use. (Source: http://dl.acm.org/)

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Bugarin, Florian; Henrion, Didier; Lasserre, Jean Bernard: Minimizing the sum of many rational functions (2016)
  2. Bugarin, Florian; Bartoli, Adrien; Henrion, Didier; Lasserre, Jean-Bernard; Orteu, Jean-José; Sentenac, Thierry: Rank-constrained fundamental matrix estimation by polynomial global optimization versus the eight-point algorithm (2015)
  3. Bertin, Rémi; Hunold, Sascha; Legrand, Arnaud; Touati, Corinne: Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization (2014)
  4. Mittelmann, Hans D.: The state-of-the-art in conic optimization software (2012)
  5. Yamashita, Makoto; Fujisawa, Katsuki; Fukuda, Mituhiro; Nakata, Kazuhide; Nakata, Maho: Algorithm 925, parallel solver for semidefinite programming problem having sparse Schur complement matrix (2012)
  6. Bao, Xiaowei; Sahinidis, Nikolaos V.; Tawarmalani, Mohit: Semidefinite relaxations for quadratically constrained quadratic programming: A review and comparisons (2011)
  7. Falkeborn, Rikard; Löfberg, Johan; Hansson, Anders: Low-rank exploitation in semidefinite programming for control (2011)
  8. Grippo, Luigi; Palagi, Laura; Piccialli, Veronica: An unconstrained minimization method for solving low-rank SDP relaxations of the maxcut problem (2011)
  9. Monniaux, David; Corbineau, Pierre: On the generation of Positivstellensatz witnesses in degenerate cases (2011)
  10. Andersen, Martin S.; Dahl, Joachim; Vandenberghe, Lieven: Implementation of nonsymmetric interior-point methods for linear optimization over sparse matrix cones (2010)
  11. Atamtürk, Alper; Narayanan, Vishnu: Conic mixed-integer rounding cuts (2010)
  12. Galbiati, Giulia; Gualandi, Stefano; Maffioli, Francesco: Computational experience with a SDP-based algorithm for maximum cut with limited unbalance (2010)
  13. Kleniati, P.M.; Parpas, P.; Rustem, B.: Decomposition-based method for sparse semidefinite relaxations of polynomial optimization problems (2010)
  14. Liu, Zhang; Vandenberghe, Lieven: Interior-point method for nuclear norm approximation with application to system identification (2010)
  15. Singhal, Harsh; Michailidis, George: Optimal experiment design in a filtering context with application to sampled network data (2010)
  16. Wahba, Grace: Encoding dissimilarity data for statistical model building (2010)
  17. Gualandi, Stefano: $k$-clustering minimum biclique completion via a hybrid CP and SDP approach (2009)
  18. Nie, Jiawang: Sum of squares method for sensor network localization (2009)
  19. Benson, Steven J.; Ye, Yinyu: Algorithm 875: DSDP5 - software for semidefinite programming. (2008)
  20. Kim, Seung-Jean; Boyd, Stephen: A minimax theorem with applications to machine learning, signal processing, and finance (2008)

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