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 347 articles )

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  1. Grübel, Julia; Kleinert, Thomas; Krebs, Vanessa; Orlinskaya, Galina; Schewe, Lars; Schmidt, Martin; Thürauf, Johannes: On electricity market equilibria with storage: modeling, uniqueness, and a distributed ADMM (2020)
  2. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  3. Aswani, Anil; Kaminsky, Philip; Mintz, Yonatan; Flowers, Elena; Fukuoka, Yoshimi: Behavioral modeling in weight loss interventions (2019)
  4. Bagger, Niels-Christian F.; Sørensen, Matias; Stidsen, Thomas R.: Dantzig-Wolfe decomposition of the daily course pattern formulation for curriculum-based course timetabling (2019)
  5. Bean, Christian; Gudmundsson, Bjarki; Ulfarsson, Henning: Automatic discovery of structural rules of permutation classes (2019)
  6. Becker, Henrique; Buriol, Luciana S.: An empirical analysis of exact algorithms for the unbounded knapsack problem (2019)
  7. Benítez-Peña, S.; Blanquero, R.; Carrizosa, E.; Ramírez-Cobo, P.: Cost-sensitive feature selection for support vector machines (2019)
  8. Beresnev, Vladimir; Melnikov, Andrey: Approximation of the competitive facility location problem with MIPs (2019)
  9. Braun, Gábor; Pokutta, Sebastian; Zink, Daniel: Lazifying conditional gradient algorithms (2019)
  10. Burgelman, Jeroen; Vanhoucke, Mario: Computing project makespan distributions: Markovian PERT networks revisited (2019)
  11. Contardo, Claudio; Iori, Manuel; Kramer, Raphael: A scalable exact algorithm for the vertex (p)-center problem (2019)
  12. Demiröz, Barış Evrim; Altınel, İ. Kuban; Akarun, Lale: Rectangle blanket problem: binary integer linear programming formulation and solution algorithms (2019)
  13. Dowson, Oscar; Philpott, Andy; Mason, Andrew; Downward, Anthony: A multi-stage stochastic optimization model of a pastoral dairy farm (2019)
  14. Dursun, Pınar; Taşkın, Z. Caner; Altınel, İ. Kuban: The determination of optimal treatment plans for volumetric modulated arc therapy (VMAT) (2019)
  15. Grimm, Veronika; Kleinert, Thomas; Liers, Frauke; Schmidt, Martin; Zöttl, Gregor: Optimal price zones of electricity markets: a mixed-integer multilevel model and global solution approaches (2019)
  16. Halder, Abhishek; Geng, Xinbo; Fontes, Fernando A. C. C.; Kumar, P. R.; Xie, Le: Optimal power consumption for demand response of thermostatically controlled loads (2019)
  17. Hesaraki, Alireza F.; Dellaert, Nico P.; de Kok, Ton: Generating outpatient chemotherapy appointment templates with balanced flowtime and makespan (2019)
  18. Karimi, Sahar; Ronagh, Pooya: Practical integer-to-binary mapping for quantum annealers (2019)
  19. Karwowski, Jan; Mańdziuk, Jacek: A Monte Carlo tree search approach to finding efficient patrolling schemes on graphs (2019)
  20. Krumke, Sven O.; Schmidt, Eva; Streicher, Manuel: Robust multicovers with budgeted uncertainty (2019)

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