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.


References in zbMATH (referenced in 150 articles )

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  1. Abdallah, L.; Haddou, M.; Migot, T.: Solving absolute value equation using complementarity and smoothing functions (2018)
  2. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  3. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  4. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  5. Bidot, Julien; Karlsson, Lars; Lagriffoul, Fabien; Saffiotti, Alessandro: Geometric backtracking for combined task and motion planning in robotic systems (2017)
  6. Blanco, Víctor; Puerto, Justo; Ponce, Diego: Continuous location under the effect of `refraction’ (2017)
  7. Diamond, Steven; Boyd, Stephen: Stochastic matrix-free equilibration (2017)
  8. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  9. Geißler, Björn; Morsi, Antonio; Schewe, Lars; Schmidt, Martin: Penalty alternating direction methods for mixed-integer optimization: a new view on feasibility pumps (2017)
  10. Gould, Nicholas I.M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  11. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  12. Ingels, Jonas; Maenhout, Broos: Employee substitutability as a tool to improve the robustness in personnel scheduling (2017)
  13. Kwon, Roy H.; Wu, Dexiang: Factor-based robust index tracking (2017)
  14. Li, Han-Lin; Huang, Yao-Huei; Fang, Shu-Cherng: Linear reformulation of polynomial discrete programming for fast computation (2017)
  15. Liu, Meijiao; Liu, Yong-Jin: Fast algorithm for singly linearly constrained quadratic programs with box-like constraints (2017)
  16. Palacio, Juan D.; Larrea, Olga L.: A lexicographic approach to the robust resource-constrained project scheduling problem (2017)
  17. Xudong Li, Defeng Sun, Kim-Chuan Toh: On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (2017) arXiv
  18. Ahmadi, Amir Ali; Majumdar, Anirudha: Some applications of polynomial optimization in operations research and real-time decision making (2016)
  19. Alabdulmohsin, Ibrahim; Cisse, Moustapha; Gao, Xin; Zhang, Xiangliang: Large margin classification with indefinite similarities (2016)
  20. Anjos, Miguel F.; Fischer, Anja; Hungerländer, Philipp: Solution approaches for the double-row equidistant facility layout problem (2016)

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