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 829 articles , 2 standard articles )

Showing results 1 to 20 of 829.
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  1. 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)
  2. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  3. 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)
  4. Duarte, Belmiro P. M.; Granjo, José F. O.; Wong, Weng Kee: Optimal exact designs of experiments via mixed integer nonlinear programming (2020)
  5. Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir: Solving oligopolistic equilibrium problems with convex optimization (2020)
  6. 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)
  7. Hooshmand, F.; Amerehi, F.; MirHassani, S. A.: Logic-based Benders decomposition algorithm for contamination detection problem in water networks (2020)
  8. Lad, Frank; Sanfilippo, Giuseppe: Predictive distributions that mimic frequencies over a restricted subdomain (2020)
  9. Marendet, Antoine; Goldsztejn, Alexandre; Chabert, Gilles; Jermann, Christophe: A standard branch-and-bound approach for nonlinear semi-infinite problems (2020)
  10. Marufuzzaman, Mohammad; Nur, Farjana; Bednar, Amy E.; Cowan, Mark: Enhancing Benders decomposition algorithm to solve a combat logistics problem (2020)
  11. 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)
  12. Nazemi, Alireza; Mortezaee, Marziyeh: Stabilization of a class of nonlinear control systems via a neural network scheme with convergence analysis (2020)
  13. Paulavičius, R.; Adjiman, C. S.: New bounding schemes and algorithmic options for the Branch-and-Sandwich algorithm (2020)
  14. Agwa, M. A.: Critical elastic parameters motivating divergence instability of frictional composite infinitely long media (2019)
  15. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  16. Brikaa, M. G.; Zheng, Zhoushun; Ammar, El-Saeed: Fuzzy multi-objective programming approach for constrained matrix games with payoffs of fuzzy rough numbers (2019)
  17. Burlacu, Robert; Egger, Herbert; Groß, Martin; Martin, Alexander; Pfetsch, Marc E.; Schewe, Lars; Sirvent, Mathias; Skutella, Martin: Maximizing the storage capacity of gas networks: a global MINLP approach (2019)
  18. Djelassi, Hatim; Glass, Moll; Mitsos, Alexander: Discretization-based algorithms for generalized semi-infinite and bilevel programs with coupling equality constraints (2019)
  19. Duarte, Belmiro P. M.; Granjo, José F. O.: Optimal exact design of double acceptance sampling plans by attributes (2019)
  20. Howitt, Richard E.; Msangi, Siwa: Using moment constraints in GME estimation (2019)

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