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 679 articles )

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  1. Adurthi, Naga Venkat; Majji, Manoranjan: Estimation of dynamic systems using a method of characteristics filter (2022)
  2. Ahmed, Shabbir; Cabral, Filipe Goulart; Freitas Paulo da Costa, Bernardo: Stochastic Lipschitz dynamic programming (2022)
  3. Azar, Macarena; Carrasco, Rodrigo A.; Mondschein, Susana: Dealing with uncertain surgery times in operating room scheduling (2022)
  4. Berger, Mathias; Radu, David; Dubois, Antoine; Pandžić, Hrvoje; Dvorkin, Yury; Louveaux, Quentin; Ernst, Damien: Siting renewable power generation assets with combinatorial optimisation (2022)
  5. Bomze, Immanuel; Gabl, Markus: Uncertainty preferences in robust mixed-integer linear optimization with endogenous uncertainty (2022)
  6. Bravo-Ferreira, José F. S.; Cowburn, David; Khoo, Yuehaw; Singer, Amit: NMR assignment through linear programming (2022)
  7. Burlacu, Robert: On refinement strategies for solving (\textscMINLP)s by piecewise linear relaxations: a generalized red refinement (2022)
  8. Ceselli, Alberto; Létocart, Lucas; Traversi, Emiliano: Dantzig-Wolfe reformulations for binary quadratic problems (2022)
  9. Däubel, Karl: An improved upper bound for the ring loading problem (2022)
  10. De Marchi, Alberto: On a primal-dual Newton proximal method for convex quadratic programs (2022)
  11. Drezner, Zvi; Kalczynski, Pawel; Misevičius, Alfonsas; Palubeckis, Gintaras: Finding optimal solutions to several gray pattern instances (2022)
  12. Duarte, Jorge A.; González, Andrés D.; Gourley, Jonathan J.: Wildfire burn scar encapsulation. Subsetting common spatial domains for post-wildfire debris flow predictions over the United States (2022)
  13. El Hajj, Hussein; Bish, Douglas R.; Bish, Ebru K.: Optimal genetic screening for cystic fibrosis (2022)
  14. Fabiani, Filippo; Margellos, Kostas; Goulart, Paul J.: Probabilistic feasibility guarantees for solution sets to uncertain variational inequalities (2022)
  15. Fairbrother, Jamie; Turner, Amanda; Wallace, Stein W.: Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure (2022)
  16. Fischer, Anja; Fischer, Frank; McCormick, S. Thomas: Matroid optimization problems with monotone monomials in the objective (2022)
  17. Glomb, Lukas; Liers, Frauke; Rösel, Florian: A rolling-horizon approach for multi-period optimization (2022)
  18. Kulkarni, Kartik; Bansal, Manish: Discrete multi-module capacitated lot-sizing problems with multiple items (2022)
  19. Liu, Xiaoyuan; Ushijima-Mwesigwa, Hayato; Mandal, Avradip; Upadhyay, Sarvagya; Safro, Ilya; Roy, Arnab: Leveraging special-purpose hardware for local search heuristics (2022)
  20. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)

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