The General Algebraic Modeling System (GAMS) is specifically designed for modeling linear, nonlinear and mixed integer optimization problems. The system is especially useful with large, complex problems. GAMS is available for use on personal computers, workstations, mainframes and supercomputers. GAMS allows the user to concentrate on the modeling problem by making the setup simple. The system takes care of the time-consuming details of the specific machine and system software implementation. GAMS is especially useful for handling large, complex, one-of-a-kind problems which may require many revisions to establish an accurate model. The system models problems in a highly compact and natural way. The user can change the formulation quickly and easily, can change from one solver to another, and can even convert from linear to nonlinear with little trouble.

References in zbMATH (referenced in 806 articles , 2 standard articles )

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  1. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  2. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  3. 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)
  4. Hooshmand, F.; Amerehi, F.; MirHassani, S. A.: Logic-based Benders decomposition algorithm for contamination detection problem in water networks (2020)
  5. Marendet, Antoine; Goldsztejn, Alexandre; Chabert, Gilles; Jermann, Christophe: A standard branch-and-bound approach for nonlinear semi-infinite problems (2020)
  6. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  7. Brikaa, M. G.; Zheng, Zhoushun; Ammar, El-Saeed: Fuzzy multi-objective programming approach for constrained matrix games with payoffs of fuzzy rough numbers (2019)
  8. Burlacu, Robert; Egger, Herbert; Groß, Martin; Martin, Alexander; Pfetsch, Marc E.; Schewe, Lars; Sirvent, Mathias; Skutella, Martin: Maximizing the storage capacity of gas networks: a global MINLP approach (2019)
  9. Djelassi, Hatim; Glass, Moll; Mitsos, Alexander: Discretization-based algorithms for generalized semi-infinite and bilevel programs with coupling equality constraints (2019)
  10. Kallrath, Josef; Frey, Markus M.: Packing circles into perimeter-minimizing convex hulls (2019)
  11. Kampas, Frank J.; Castillo, Ignacio; Pintér, János D.: Optimized ellipse packings in regular polygons (2019)
  12. Kim, Youngdae; Ferris, Michael C.: Solving equilibrium problems using extended mathematical programming (2019)
  13. Koster, Arie M. C. A.; Kuhnke, Sascha: An adaptive discretization algorithm for the design of water usage and treatment networks (2019)
  14. Ogbe, Emmanuel; Li, Xiang: A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs (2019)
  15. Poudel, Sushil R.; Quddus, Md Abdul; Marufuzzaman, Mohammad; Bian, Linkan; Burch, Reuben F. V: Managing congestion in a multi-modal transportation network under biomass supply uncertainty (2019)
  16. Schewe, Lars; Schmidt, Martin: Computing feasible points for binary MINLPs with MPECs (2019)
  17. Singh, Bismark; Watson, Jean-Paul: Approximating two-stage chance-constrained programs with classical probability bounds (2019)
  18. Wang, Tong; Lima, Ricardo M.; Giraldi, Loïc; Knio, Omar M.: Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming (2019)
  19. Yang, Yunyun; Jia, Wenjing; Shu, Xiu; Wu, Boying: Level set formulation based on edge and region information with application to accurate lesion segmentation of brain magnetic resonance images (2019)
  20. Yue, Dajun; Gao, Jiyao; Zeng, Bo; You, Fengqi: A projection-based reformulation and decomposition algorithm for global optimization of a class of mixed integer bilevel linear programs (2019)

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