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 761 articles , 2 standard articles )

Showing results 1 to 20 of 761.
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  1. 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)
  2. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  3. 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)
  4. 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)
  5. Consiglio, Andrea; Tumminello, Michele; Zenios, Stavros A.: Pricing sovereign contingent convertible debt (2018)
  6. Fischetti, Matteo; Monaci, Michele; Salvagnin, Domenico: SelfSplit parallelization for mixed-integer linear programming (2018)
  7. Gergel, Victor; Barkalov, Konstantin; Sysoyev, Alexander: Globalizer: a novel supercomputer software system for solving time-consuming global optimization problems (2018)
  8. Kallrath, Josef; Frey, Markus M.: Minimal surface convex hulls of spheres (2018)
  9. Lima, Ricardo M.; Conejo, Antonio J.; Langodan, Sabique; Hoteit, Ibrahim; Knio, Omar M.: Risk-averse formulations and methods for a virtual power plant (2018)
  10. Mazidi, Peyman; Tohidi, Yaser; Ramos, Andres; Sanz-Bobi, Miguel A.: Profit-maximization generation maintenance scheduling through bi-level programming (2018)
  11. Mejia-Argueta, Christopher; Gaytán, Juan; Caballero, Rafael; Molina, Julián; Vitoriano, Begoña: Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods (2018)
  12. Mertens, Nick; Kunde, Christian; Kienle, Achim; Michaels, Dennis: Monotonic reformulation and bound tightening for global optimization of ideal multi-component distillation columns (2018)
  13. Mitsos, Alexander; Najman, Jaromił; Kevrekidis, Ioannis G.: Optimal deterministic algorithm generation (2018)
  14. Montanher, Tiago; Neumaier, Arnold; Domes, Ferenc: A computational study of global optimization solvers on two trust region subproblems (2018)
  15. Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)
  16. Petridis, Konstantinos; Dey, Prasanta Kumar: Measuring incineration plants’ performance using combined data envelopment analysis, goal programming and mixed integer linear programming (2018)
  17. Pineda, S.; Bylling, H.; Morales, J. M.: Efficiently solving linear bilevel programming problems using off-the-shelf optimization software (2018)
  18. 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)
  19. Schweiger, Jonas: Exploiting structure in non-convex quadratic optimization and gas network planning under uncertainty (2018)
  20. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: ParaXpress: an experimental extension of the FICO Xpress-Optimizer to solve hard MIPs on supercomputers (2018)

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