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.


References in zbMATH (referenced in 405 articles )

Showing results 1 to 20 of 405.
Sorted by year (citations)

1 2 3 ... 19 20 21 next

  1. 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)
  2. Burgelman, Jeroen; Vanhoucke, Mario: Project schedule performance under general mode implementation disruptions (2020)
  3. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  4. 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)
  5. 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)
  6. Kim, Jongeun; Veremyev, Alexander; Boginski, Vladimir; Prokopyev, Oleg A.: On the maximum small-world subgraph problem (2020)
  7. Lesage-Landry, Antoine; Taylor, Joshua A.: A second-order cone model of transmission planning with alternating and direct current lines (2020)
  8. Li, Xingmei; Huang, Yao-Huei; Fang, Shu-Cherng; Zhang, Youzhong: An alternative efficient representation for the project portfolio selection problem (2020)
  9. Altner, Douglas S.; Mason, Erica K.; Servi, Les D.: Two-stage stochastic days-off scheduling of multi-skilled analysts with training options (2019)
  10. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  11. Andrade, Tiago; Oliveira, Fabricio; Hamacher, Silvio; Eberhard, Andrew: Enhancing the normalized multiparametric disaggregation technique for mixed-integer quadratic programming (2019)
  12. Aswani, Anil; Kaminsky, Philip; Mintz, Yonatan; Flowers, Elena; Fukuoka, Yoshimi: Behavioral modeling in weight loss interventions (2019)
  13. Bagger, Niels-Christian F.; Sørensen, Matias; Stidsen, Thomas R.: Dantzig-Wolfe decomposition of the daily course pattern formulation for curriculum-based course timetabling (2019)
  14. Bahmani, Sohail: Estimation from nonlinear observations via convex programming with application to bilinear regression (2019)
  15. Bean, Christian; Gudmundsson, Bjarki; Ulfarsson, Henning: Automatic discovery of structural rules of permutation classes (2019)
  16. Becker, Henrique; Buriol, Luciana S.: An empirical analysis of exact algorithms for the unbounded knapsack problem (2019)
  17. Benítez-Peña, Sandra; Blanquero, Rafael; Carrizosa, Emilio; Ramírez-Cobo, Pepa: On support vector machines under a multiple-cost scenario (2019)
  18. Benítez-Peña, S.; Blanquero, R.; Carrizosa, E.; Ramírez-Cobo, P.: Cost-sensitive feature selection for support vector machines (2019)
  19. Bereg, Sergey; Miller, Zevi; Mojica, Luis Gerardo; Morales, Linda; Sudborough, I. H.: New lower bounds for permutation arrays using contraction (2019)
  20. Beresnev, Vladimir; Melnikov, Andrey: Approximation of the competitive facility location problem with MIPs (2019)

1 2 3 ... 19 20 21 next