AMPL

AMPL is a comprehensive and powerful algebraic modeling language for linear and nonlinear optimization problems, in discrete or continuous variables. Developed at Bell Laboratories, AMPL lets you use common notation and familiar concepts to formulate optimization models and examine solutions, while the computer manages communication with an appropriate solver. AMPL’s flexibility and convenience render it ideal for rapid prototyping and model development, while its speed and control options make it an especially efficient choice for repeated production runs.


References in zbMATH (referenced in 424 articles , 2 standard articles )

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  1. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  2. Burachik, R.S.; Kaya, C.Y.; Rizvi, M.M.: A new scalarization technique and new algorithms to generate Pareto fronts (2017)
  3. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  4. do Rosário de Pinho, Maria; Nunes Nogueira, Filipa: On application of optimal control to SEIR normalized models: pros and cons (2017)
  5. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  6. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  7. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)
  8. Klamka, Jerzy; Maurer, Helmut; Swierniak, Andrzej: Local controllability and optimal control for a model of combined anticancer therapy with control delays (2017)
  9. Rojas Rodríguez, Clara; Belmonte-Beitia, Juan: Optimizing the delivery of combination therapy for tumors: a mathematical model (2017)
  10. Silva, Cristiana J.; Maurer, Helmut; Torres, Delfim F.M.: Optimal control of a tuberculosis model with state and control delays (2017)
  11. Alt, Walter; Kaya, C.Yalçın; Schneider, Christopher: Dualization and discretization of linear-quadratic control problems with bang-bang solutions (2016)
  12. Benko, Matus; Gfrerer, Helmut: An SQP method for mathematical programs with complementarity constraints with strong convergence properties. (2016)
  13. Birgin, E.G.; Lobato, R.D.; Martínez, J.M.: Packing ellipsoids by nonlinear optimization (2016)
  14. Birgin, E.G.; Martínez, J.M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  15. Boland, Natashia; Kalinowski, Thomas; Rigterink, Fabian: New multi-commodity flow formulations for the pooling problem (2016)
  16. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  17. Castro, Jordi: Interior-point solver for convex separable block-angular problems (2016)
  18. Chaudhry, Aizaz U.; Chinneck, John W.; Hafez, Roshdy H.M.: Fast heuristics for the frequency channel assignment problem in multi-hop wireless networks (2016)
  19. Cire, Andre A.; Hooker, John N.; Yunes, Tallys: Modeling with metaconstraints and semantic typing of variables (2016)
  20. Dearing, P.M.; Belotti, Pietro; Smith, Andrea M.: A primal algorithm for the weighted minimum covering ball problem in $\mathbb R^n$ (2016)

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