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.
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References in zbMATH (referenced in 126 articles )
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Sorted by year (- Blanco, Víctor; Puerto, Justo; Ponce, Diego: Continuous location under the effect of `refraction’ (2017)
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- Anjos, Miguel F.; Fischer, Anja; Hungerländer, Philipp: Solution approaches for the double-row equidistant facility layout problem (2016)
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- Bertsimas, Dimitris; de Ruiter, Frans J.C.T.: Duality in two-stage adaptive linear optimization: faster computation and stronger bounds (2016)
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- Burdakov, Oleg P.; Kanzow, Christian; Schwartz, Alexandra: Mathematical programs with cardinality constraints: reformulation by complementarity-type conditions and a regularization method (2016)
- Castillo-Salazar, J.Arturo; Landa-Silva, Dario; Qu, Rong: Workforce scheduling and routing problems: literature survey and computational study (2016)
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- de Ruiter, Frans J.C.T.; Brekelmans, Ruud C.M.; den Hertog, Dick: The impact of the existence of multiple adjustable robust solutions (2016)
- Eckstein, Jonathan; Eskandani, Deniz; Fan, Jingnan: Multilevel optimization modeling for risk-averse stochastic programming (2016)
- Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
- Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
- Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)