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 856 articles , 2 standard articles )

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  1. Alpaslan Takan, Melis; Kasimbeyli, Refail: Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems (2021)
  2. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  3. Eichfelder, Gabriele; Klamroth, Kathrin; Niebling, Julia: Nonconvex constrained optimization by a filtering branch and bound (2021)
  4. Francesco Ceccon, Ruth Misener: Solving the pooling problem at scale with extensible solver GALINI (2021) arXiv
  5. Khodayifar, Salman: Minimum cost multicommodity network flow problem in time-varying networks: by decomposition principle (2021)
  6. Lohmann, Timo; Bussieck, Michael R.; Westermann, Lutz; Rebennack, Steffen: High-performance prototyping of decomposition methods in GAMS (2021)
  7. Mavrotas, George; Makryvelios, Evangelos: Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in research and development project portfolio selection: a case study from Greece (2021)
  8. Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee, Timo Kuosmanen: pyStoNED: A Python Package for Convex Regression and Frontier Estimation (2021) arXiv
  9. Singh, Bismark; Knueven, Bernard: Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system (2021)
  10. Wolsey, Laurence A.: Integer programming (2021)
  11. Ashimov, Abdykappar A.; Borovskiy, Yuriy V.; Novikov, Dmitry A.; Sultanov, Bahyt T.; Onalbekov, Mukhit A.: Macroeconomic analysis and parametric control of a regional economic union (2020)
  12. Berberler, Murat Erşen; Uğurlu, Onur; Berberler, Zeynep Nihan: Independent strong weak domination: a mathematical programming approach (2020)
  13. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  14. Cervantes-Gaxiola, Maritza E.; Sosa-Niebla, Erik F.; Hernández-Calderón, Oscar M.; Ponce-Ortega, José M.; Ortiz-del-Castillo, Jesús R.; Rubio-Castro, Eusiel: Optimal crop allocation including market trends and water availability (2020)
  15. Charitopoulos, Vassilis M.; Dua, Vivek; Pinto, Jose M.; Papageorgiou, Lazaros G.: A game-theoretic optimisation approach to fair customer allocation in oligopolies (2020)
  16. Diwekar, Urmila M.: Introduction to applied optimization (2020)
  17. Duarte, Belmiro P. M.; Granjo, José F. O.; Wong, Weng Kee: Optimal exact designs of experiments via mixed integer nonlinear programming (2020)
  18. Duarte, Belmiro P. M.; Sagnol, Guillaume: Approximate and exact optimal designs for (2^k) factorial experiments for generalized linear models via second order cone programming (2020)
  19. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  20. 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)

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