Gurobi
GUROBI OPTIMIZER: State of the Art Mathematical Programming Solver. The Gurobi Optimizer is a state-of-the-art solver for mathematical programming. It includes the following solvers: linear programming solver (LP), quadratic programming solver (QP), quadratically constrained programming solver (QCP), mixed-integer linear programming solver (MILP), mixed-integer quadratic programming solver (MIQP), and mixed-integer quadratically constrained programming solver (MIQCP). The solvers in the Gurobi Optimizer were designed from the ground up to exploit modern architectures and multi-core processors, using the most advanced implementations of the latest algorithms. To help set you up for success, the Gurobi Optimizer goes beyond fast and reliable solution performance to provide a broad range of interfaces, access to industry-standard modeling languages, flexible licensing together with transparent pricing, and outstanding, easy to reach, support.
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References in zbMATH (referenced in 215 articles )
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Sorted by year (- Abdallah, L.; Haddou, M.; Migot, T.: Solving absolute value equation using complementarity and smoothing functions (2018)
- Espuny-Pujol, Ferran; Morrissey, Karyn; Williamson, Paul: A global optimisation approach to range-restricted survey calibration (2018)
- Fink, Andreas (ed.); Fügenschuh, Armin (ed.); Geiger, Martin Josef (ed.): Operations research proceedings 2016. Selected papers of the annual international conference of the German Operations Research Society (GOR), Helmut Schmidt University Hamburg, Germany, August 30 -- September 2, 2016 (2018)
- Goerigk, Marc; Hamacher, Horst W.; Kinscherff, Anika: Ranking robustness and its application to evacuation planning (2018)
- Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
- Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
- Kallus, Nathan: Optimal \ita priori balance in the design of controlled experiments (2018)
- Lancia, Giuseppe; Serafini, Paolo: Compact extended linear programming models (2018)
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- Stȩpień, Zofia; Szymaszkiewicz, Lucjan: Arcs in $\mathbb Z^2_2p$ (2018)
- Abdelkhalek, Ahmed; Tolba, Mohamed; Youssef, Amr M.: Impossible differential attack on reduced round SPARX-64/128 (2017)
- Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
- Amadini, Roberto; Flener, Pierre; Pearson, Justin; Scott, Joseph D.; Stuckey, Peter J.; Tack, Guido: Minizinc with strings (2017)
- Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
- Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
- Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
- Bertsimas, Dimitris; Dunn, Jack: Optimal classification trees (2017)
- Bertsimas, Dimitris; Griffith, J.Daniel; Gupta, Vishal; Kochenderfer, Mykel J.; Mišić, Velibor V.: A comparison of Monte Carlo tree search and rolling horizon optimization for large-scale dynamic resource allocation problems (2017)