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.

References in zbMATH (referenced in 194 articles )

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  1. Abdallah, L.; Haddou, M.; Migot, T.: Solving absolute value equation using complementarity and smoothing functions (2018)
  2. 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)
  3. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  4. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  5. Moeini, Mahdi; Wendt, Oliver: A heuristic for solving the maximum dispersion problem (2018)
  6. Abdelkhalek, Ahmed; Tolba, Mohamed; Youssef, Amr M.: Impossible differential attack on reduced round SPARX-64/128 (2017)
  7. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  8. Amadini, Roberto; Flener, Pierre; Pearson, Justin; Scott, Joseph D.; Stuckey, Peter J.; Tack, Guido: Minizinc with strings (2017)
  9. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  10. Anqi Fu, Balasubramanian Narasimhan, Stephen Boyd: CVXR: An R Package for Disciplined Convex Optimization (2017) arXiv
  11. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  12. Bidot, Julien; Karlsson, Lars; Lagriffoul, Fabien; Saffiotti, Alessandro: Geometric backtracking for combined task and motion planning in robotic systems (2017)
  13. Blanco, Víctor; Fernández, Elena; Puerto, Justo: Minimum spanning trees with neighborhoods: mathematical programming formulations and solution methods (2017)
  14. Blanco, Víctor; Puerto, Justo; Ponce, Diego: Continuous location under the effect of `refraction’ (2017)
  15. Chiang, Wei-Fan; Baranowski, Mark; Briggs, Ian; Solovyev, Alexey; Gopalakrishnan, Ganesh; Rakamarić, Zvonimir: Rigorous floating-point mixed-precision tuning (2017)
  16. Diamond, Steven; Boyd, Stephen: Stochastic matrix-free equilibration (2017)
  17. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  18. Geißler, Björn; Morsi, Antonio; Schewe, Lars; Schmidt, Martin: Penalty alternating direction methods for mixed-integer optimization: a new view on feasibility pumps (2017)
  19. Genova, Kyle; Williamson, David P.: An experimental evaluation of the best-of-many Christofides’ algorithm for the traveling salesman problem (2017)
  20. Goebel, Gregor; Allgöwer, Frank: Semi-explicit MPC based on subspace clustering (2017)

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