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.
Keywords for this software
References in zbMATH (referenced in 439 articles )
Showing results 1 to 20 of 439.
Sorted by year (citations)
- Almeida Guimarães, Dilson; Salles da Cunha, Alexandre; Pereira, Dilson Lucas: Semidefinite programming lower bounds and branch-and-bound algorithms for the quadratic minimum spanning tree problem (2020)
- Arana-Jiménez, Manuel; Blanco, Víctor; Fernández, Elena: On the fuzzy maximal covering location problem (2020)
- Araujo, Janniele A. S.; Santos, Haroldo G.; Gendron, Bernard; Jena, Sanjay Dominik; Brito, Samuel S.; Souza, Danilo S.: Strong bounds for resource constrained project scheduling: preprocessing and cutting planes (2020)
- Bayless, Sam; Kodirov, Nodir; Iqbal, Syed M.; Beschastnikh, Ivan; Hoos, Holger H.; Hu, Alan J.: Scalable constraint-based virtual data center allocation (2020)
- Bereg, Sergey; Mojica, Luis Gerardo; Morales, Linda; Sudborough, Hal: Constructing permutation arrays using partition and extension (2020)
- Beresnev, Vladimir; Melnikov, Andrey: (\varepsilon)-Constraint method for bi-objective competitive facility location problem with uncertain demand scenario (2020)
- Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
- Costalonga, João Paulo: Toroidal boards and code covering (2020)
- De Santis, Marianna; Grani, Giorgio; Palagi, Laura: Branching with hyperplanes in the criterion space: the frontier partitioner algorithm for biobjective integer programming (2020)
- Gerault, David; Lafourcade, Pascal; Minier, Marine; Solnon, Christine: Computing AES related-key differential characteristics with constraint programming (2020)
- Grass, Emilia; Fischer, Kathrin; Rams, Antonia: An accelerated L-shaped method for solving two-stage stochastic programs in disaster management (2020)
- 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)
- Habibian, Mahbubeh; Downward, Anthony; Zakeri, Golbon: Multistage stochastic demand-side management for price-making major consumers of electricity in a co-optimized energy and reserve market (2020)
- Jélvez, Enrique; Morales, Nelson; Askari-Nasab, Hooman: A new model for automated pushback selection (2020)
- Khatami, Mostafa; Salehipour, Amir; Cheng, T. C. E.: Coupled task scheduling with exact delays: literature review and models (2020)
- Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
- Koeln, Justin; Raghuraman, Vignesh; Hencey, Brandon: Vertical hierarchical MPC for constrained linear systems (2020)
- Kronqvist, Jan; Bernal, David E.; Grossmann, Ignacio E.: Using regularization and second order information in outer approximation for convex MINLP (2020)
- Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
- Li, Xingmei; Huang, Yao-Huei; Fang, Shu-Cherng; Zhang, Youzhong: An alternative efficient representation for the project portfolio selection problem (2020)