GAMS

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 780 articles , 2 standard articles )

Showing results 1 to 20 of 780.
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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. Brikaa, M. G.; Zheng, Zhoushun; Ammar, El-Saeed: Fuzzy multi-objective programming approach for constrained matrix games with payoffs of fuzzy rough numbers (2019)
  4. Kallrath, Josef; Frey, Markus M.: Packing circles into perimeter-minimizing convex hulls (2019)
  5. 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)
  6. 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)
  7. 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)
  8. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  9. Breuer, Thomas; Bussieck, Michael; Cao, Karl-Kiên; Cebulla, Felix; Fiand, Frederik; Gils, Hans Christian; Gleixner, Ambros; Khabi, Dmitry; Koch, Thorsten; Rehfeldt, Daniel; Wetzel, Manuel: Optimizing large-scale linear energy system problems with block diagonal structure by using parallel interior-point methods (2018)
  10. Cherri, Luiz H.; Cherri, Adriana C.; Soler, Edilaine M.: Mixed integer quadratically-constrained programming model to solve the irregular strip packing problem with continuous rotations (2018)
  11. Consiglio, Andrea; Tumminello, Michele; Zenios, Stavros A.: Pricing sovereign contingent convertible debt (2018)
  12. Fischetti, Matteo; Monaci, Michele; Salvagnin, Domenico: SelfSplit parallelization for mixed-integer linear programming (2018)
  13. Gergel, Victor; Barkalov, Konstantin; Sysoyev, Alexander: Globalizer: a novel supercomputer software system for solving time-consuming global optimization problems (2018)
  14. Jordan Jalving, Yankai Cao, Victor M. Zavala: Graph-Based Modeling and Simulation of Complex Systems (2018) arXiv
  15. Kallrath, Josef; Frey, Markus M.: Minimal surface convex hulls of spheres (2018)
  16. Lima, Ricardo M.; Conejo, Antonio J.; Langodan, Sabique; Hoteit, Ibrahim; Knio, Omar M.: Risk-averse formulations and methods for a virtual power plant (2018)
  17. Mazidi, Peyman; Tohidi, Yaser; Ramos, Andres; Sanz-Bobi, Miguel A.: Profit-maximization generation maintenance scheduling through bi-level programming (2018)
  18. Mejia-Argueta, Christopher; Gaytán, Juan; Caballero, Rafael; Molina, Julián; Vitoriano, Begoña: Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods (2018)
  19. Mertens, Nick; Kunde, Christian; Kienle, Achim; Michaels, Dennis: Monotonic reformulation and bound tightening for global optimization of ideal multi-component distillation columns (2018)
  20. Mitsos, Alexander; Najman, Jaromił; Kevrekidis, Ioannis G.: Optimal deterministic algorithm generation (2018)

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Further publications can be found at: http://www.gams.com/presentations/index.htm