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 474 articles , 2 standard articles )

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  1. de Pinho, Maria do Rosário; Maurer, Helmut; Zidani, Hasnaa: Optimal control of normalized SIMR models with vaccination and treatment (2018)
  2. Rodrigues, Filipe; Silva, Cristiana J.; Torres, Delfim F.M.; Maurer, Helmut: Optimal control of a delayed HIV model (2018)
  3. Thäter, Markus; Chudej, Kurt; Pesch, Hans Josef: Optimal vaccination strategies for an SEIR model of infectious diseases with logistic growth (2018)
  4. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  5. Backe, Stian; Haugland, Dag: Strategic optimization of offshore wind farm installation (2017)
  6. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  7. Burachik, R.S.; Kaya, C.Y.; Rizvi, M.M.: A new scalarization technique and new algorithms to generate Pareto fronts (2017)
  8. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  9. Després, Bruno: Polynomials with bounds and numerical approximation (2017)
  10. do Rosário de Pinho, Maria; Nunes Nogueira, Filipa: On application of optimal control to SEIR normalized models: pros and cons (2017)
  11. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  12. Hanks, Robert W.; Weir, Jeffery D.; Lunday, Brian J.: Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets (2017)
  13. 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)
  14. Hijazi, Hassan; Coffrin, Carleton; Van Hentenryck, Pascal: Convex quadratic relaxations for mixed-integer nonlinear programs in power systems (2017)
  15. Johnson, Benjamin; Newman, Alexandra; King, Jeffrey: Optimizing high-level nuclear waste disposal within a deep geologic repository (2017)
  16. Kaya, C.Yalçın: Markov-Dubins path via optimal control theory (2017)
  17. Kersting, Kristian; Mladenov, Martin; Tokmakov, Pavel: Relational linear programming (2017)
  18. Klamka, Jerzy; Maurer, Helmut; Swierniak, Andrzej: Local controllability and optimal control for a model of combined anticancer therapy with control delays (2017)
  19. Rojas Rodríguez, Clara; Belmonte-Beitia, Juan: Optimizing the delivery of combination therapy for tumors: a mathematical model (2017)
  20. Silva, Cristiana J.; Maurer, Helmut; Torres, Delfim F.M.: Optimal control of a tuberculosis model with state and control delays (2017)

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