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 747 articles , 2 standard articles )

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  1. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  2. Breuer, Thomas; Bussieck, Michael; Cao, Karl-Ki^en; 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)
  3. Fischetti, Matteo; Monaci, Michele; Salvagnin, Domenico: SelfSplit parallelization for mixed-integer linear programming (2018)
  4. Lima, Ricardo M.; Conejo, Antonio J.; Langodan, Sabique; Hoteit, Ibrahim; Knio, Omar M.: Risk-averse formulations and methods for a virtual power plant (2018)
  5. Mazidi, Peyman; Tohidi, Yaser; Ramos, Andres; Sanz-Bobi, Miguel A.: Profit-maximization generation maintenance scheduling through bi-level programming (2018)
  6. Mejia-Argueta, Christopher; Gaytán, Juan; Caballero, Rafael; Molina, Julián; Vitoriano, Begoña: Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods (2018)
  7. Mertens, Nick; Kunde, Christian; Kienle, Achim; Michaels, Dennis: Monotonic reformulation and bound tightening for global optimization of ideal multi-component distillation columns (2018)
  8. Mitsos, Alexander; Najman, Jaromił; Kevrekidis, Ioannis G.: Optimal deterministic algorithm generation (2018)
  9. Montanher, Tiago; Neumaier, Arnold; Domes, Ferenc: A computational study of global optimization solvers on two trust region subproblems (2018)
  10. Pineda, S.; Bylling, H.; Morales, J. M.: Efficiently solving linear bilevel programming problems using off-the-shelf optimization software (2018)
  11. Schwarz, Hannes; Bertsch, Valentin; Fichtner, Wolf: Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter (2018)
  12. Schweiger, Jonas: Exploiting structure in non-convex quadratic optimization and gas network planning under uncertainty (2018)
  13. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: ParaXpress: an experimental extension of the FICO Xpress-Optimizer to solve hard MIPs on supercomputers (2018)
  14. Adamo, Tommaso; Ghiani, Gianpaolo; Grieco, Antonio; Guerriero, Emanuela; Manni, Emanuele: MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration (2017)
  15. Andrei, Neculai: Continuous nonlinear optimization for engineering applications in GAMS technology (2017)
  16. Asimakopoulou, Georgia E.; Vlachos, Andreas G.; Hatziargyriou, Nikos D.: Bilevel model for retail electricity pricing (2017)
  17. Atabaki, Mohammad Saeid; Mohammadi, Mohammad: A genetic algorithm for integrated lot sizing and supplier selection with defective items and storage and supplier capacity constraints (2017)
  18. Atabaki, Mohammad Saeid; Mohammadi, Mohammad; Naderi, Bahman: Hybrid genetic algorithm and invasive weed optimization via priority based encoding for location-allocation decisions in a three-stage supply chain (2017)
  19. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  20. Bongartz, Dominik; Mitsos, Alexander: Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations (2017)

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