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 389 articles , 2 standard articles )

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  1. do Rosário de Pinho, Maria; Nunes Nogueira, Filipa: On application of optimal control to SEIR normalized models: pros and cons (2017)
  2. Klamka, Jerzy; Maurer, Helmut; Swierniak, Andrzej: Local controllability and optimal control for a model of combined anticancer therapy with control delays (2017)
  3. Silva, Cristiana J.; Maurer, Helmut; Torres, Delfim F.M.: Optimal control of a tuberculosis model with state and control delays (2017)
  4. Alt, Walter; Kaya, C.Yalçın; Schneider, Christopher: Dualization and discretization of linear-quadratic control problems with bang-bang solutions (2016)
  5. Benko, Matus; Gfrerer, Helmut: An SQP method for mathematical programs with complementarity constraints with strong convergence properties. (2016)
  6. Birgin, E.G.; Lobato, R.D.; Martínez, J.M.: Packing ellipsoids by nonlinear optimization (2016)
  7. Birgin, E.G.; Martínez, J.M.: On the application of an augmented Lagrangian algorithm to some portfolio problems (2016)
  8. Castro, Jordi: Interior-point solver for convex separable block-angular problems (2016)
  9. Cire, Andre A.; Hooker, John N.; Yunes, Tallys: Modeling with metaconstraints and semantic typing of variables (2016)
  10. Dearing, P.M.; Belotti, Pietro; Smith, Andrea M.: A primal algorithm for the weighted minimum covering ball problem in $\mathbb R^n$ (2016)
  11. Diamond, Steven; Boyd, Stephen: CVXPY: a python-embedded modeling language for convex optimization (2016)
  12. Fliege, Jörg; Vaz, A.Ismael F.: A method for constrained multiobjective optimization based on SQP techniques (2016)
  13. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  14. Iqbal, Tanveer; Neumaier, Arnold: Worst case error bounds for the solution of uncertain Poisson equations with mixed boundary conditions (2016)
  15. Lin, Fu; Leyffer, Sven; Munson, Todd: A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings (2016)
  16. Ninin, Jordan: Global optimization based on contractor programming: an overview of the IBEX library (2016)
  17. Rojas, Clara; Belmonte-Beitia, Juan; Pérez-García, Víctor M.; Maurer, Helmut: Dynamics and optimal control of chemotherapy for low grade gliomas: insights from a mathematical model (2016)
  18. Salvagnin, Domenico: Detecting semantic groups in MIP models (2016)
  19. Shiina, Takayuki; Yurugi, Takahiro; Morito, Susumu; Imaizumi, Jun: Unit commitment by column generation (2016)
  20. Xu, Shubin; Bean, James C.: Scheduling parallel-machine batch operations to maximize on-time delivery performance (2016)

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