SCIP-SDP is a solver for mixed-integer semidefinite programs (SDPs). It implements a SDP-based branch-and-cut approach. The SDP-relaxations are solved using interior-point SDP-solvers like DSDP, Mosek or SDPA. The SCIP-SDP-Package is a plug-in for the branch-and-cut framework SCIP. The goal of SCIP-SDP is to provide a solver for general mixed-integer SPDs. Thus, it provides data handling procedures, presolve routines, primal heuristics, a dual fixing method, and an linear approximation procedure for MISDPs. SCIP-SDP can also be parallelized with ParaSCIP. This software is currently developed at TU Darmstadt.
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References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Bertsimas, Dimitris; Lamperski, Jourdain; Pauphilet, Jean: Certifiably optimal sparse inverse covariance estimation (2020)
- Coey, Chris; Lubin, Miles; Vielma, Juan Pablo: Outer approximation with conic certificates for mixed-integer convex problems (2020)
- Kobayashi, Ken; Takano, Yuich: A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems (2020)
- Gally, Tristan M.: Computational mixed-integer semidefinite programming (2019)
- Kellner, Kai; Pfetsch, Marc E.; Theobald, Thorsten: Irreducible infeasible subsystems of semidefinite systems (2019)
- Nugroho, Sebastian A.; Taha, Ahmad F.; Gatsis, Nikolaos; Summers, Tyler H.; Krishnan, Ram: Algorithms for joint sensor and control nodes selection in dynamic networks (2019)
- Gally, Tristan; Pfetsch, Marc E.; Ulbrich, Stefan: A framework for solving mixed-integer semidefinite programs (2018)
- Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
- Shinano, Yuji: The ubiquity generator framework: 7 years of progress in parallelizing branch-and-bound (2018)
- Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
- Wang, Peng; Shen, Chunhua; van den Hengel, Anton; Torr, Philip H. S.: Efficient semidefinite branch-and-cut for MAP-MRF inference (2016)