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 842 articles , 2 standard articles )

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  1. Ding Ma, Dominique Orban, Michael A. Saunders: A Julia implementation of Algorithm NCL for constrained optimization (2021) arXiv
  2. Wolsey, Laurence A.: Integer programming (2021)
  3. 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)
  4. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  5. 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)
  6. Charitopoulos, Vassilis M.; Dua, Vivek; Pinto, Jose M.; Papageorgiou, Lazaros G.: A game-theoretic optimisation approach to fair customer allocation in oligopolies (2020)
  7. Diwekar, Urmila M.: Introduction to applied optimization (2020)
  8. Duarte, Belmiro P. M.; Granjo, José F. O.; Wong, Weng Kee: Optimal exact designs of experiments via mixed integer nonlinear programming (2020)
  9. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  10. 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)
  11. Hooshmand, F.; Amerehi, F.; MirHassani, S. A.: Logic-based Benders decomposition algorithm for contamination detection problem in water networks (2020)
  12. Lad, Frank; Sanfilippo, Giuseppe: Predictive distributions that mimic frequencies over a restricted subdomain (2020)
  13. Marendet, Antoine; Goldsztejn, Alexandre; Chabert, Gilles; Jermann, Christophe: A standard branch-and-bound approach for nonlinear semi-infinite problems (2020)
  14. Marufuzzaman, Mohammad; Nur, Farjana; Bednar, Amy E.; Cowan, Mark: Enhancing Benders decomposition algorithm to solve a combat logistics problem (2020)
  15. Miralinaghi, Mohammad; Seilabi, Sania E.; Chen, Sikai; Hsu, Yu-Ting; Labi, Samuel: Optimizing the selection and scheduling of multi-class projects using a Stackelberg framework (2020)
  16. Nazemi, Alireza; Mortezaee, Marziyeh: Stabilization of a class of nonlinear control systems via a neural network scheme with convergence analysis (2020)
  17. Paulavičius, R.; Adjiman, C. S.: New bounding schemes and algorithmic options for the Branch-and-Sandwich algorithm (2020)
  18. Prasad, T. V. S. R. K.; Srinivas, Kolla; Srinivas, C.: Investigations into control strategies of supply chain planning models: a case study (2020)
  19. Vrahatis, Michael N.; Kontogiorgos, Panagiotis; Papavassilopoulos, George P.: Particle swarm optimization for computing Nash and Stackelberg equilibria in energy markets (2020)
  20. Yu, Guan; Yin, Liang; Lu, Shu; Liu, Yufeng: Confidence intervals for sparse penalized regression with random designs (2020)

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