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 241 articles )

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  1. Abdallah, L.; Haddou, M.; Migot, T.: Solving absolute value equation using complementarity and smoothing functions (2018)
  2. Atan, Tankut; Eren, Elif: Optimal project duration for resource leveling (2018)
  3. Bamberg, John; Lansdown, Jesse; Lee, Melissa: On $m$-ovoids of regular near polygons (2018)
  4. Boysen, Nils; Fedtke, Stefan; Weidinger, Felix: Optimizing automated sorting in warehouses: the minimum order spread sequencing problem (2018)
  5. Branda, Martin; Bucher, Max; Červinka, Michal; Schwartz, Alexandra: Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization (2018)
  6. Brandstätter, Christian; Reimann, Marc: The line-haul feeder vehicle routing problem: mathematical model formulation and heuristic approaches (2018)
  7. Carrasco, Rodrigo A.; Iyengar, Garud; Stein, Cliff: Resource cost aware scheduling (2018)
  8. Cheng, Jianqiang; Chen, Richard Li-Yang; Najm, Habib N.; Pinar, Ali; Safta, Cosmin; Watson, Jean-Paul: Chance-constrained economic dispatch with renewable energy and storage (2018)
  9. Delfino, A.; de Oliveira, W.: Outer-approximation algorithms for nonsmooth convex MINLP problems (2018)
  10. Diamond, S.; Takapoui, R.; Boyd, S.: A general system for heuristic minimization of convex functions over non-convex sets (2018)
  11. Espuny-Pujol, Ferran; Morrissey, Karyn; Williamson, Paul: A global optimisation approach to range-restricted survey calibration (2018)
  12. Fairbrother, Jamie; Letchford, Adam N.; Briggs, Keith: A two-level graph partitioning problem arising in mobile wireless communications (2018)
  13. 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)
  14. Framinan, Jose M.; Perez-Gonzalez, Paz: Order scheduling with tardiness objective: improved approximate solutions (2018)
  15. Goerigk, Marc; Hamacher, Horst W.; Kinscherff, Anika: Ranking robustness and its application to evacuation planning (2018)
  16. Gugat, Martin; Leugering, Günter; Martin, Alexander; Schmidt, Martin; Sirvent, Mathias; Wintergerst, David: MIP-based instantaneous control of mixed-integer PDE-constrained gas transport problems (2018)
  17. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  18. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  19. Ingels, Jonas; Maenhout, Broos: The impact of overtime as a time-based proactive scheduling and reactive allocation strategy on the robustness of a personnel shift roster (2018)
  20. Kallus, Nathan: Optimal \ita priori balance in the design of controlled experiments (2018)

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