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 499 articles , 2 standard articles )

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  1. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  2. Cafieri, Sonia; Cellier, Loïc; Messine, Frédéric; Omheni, Riadh: Combination of optimal control approaches for aircraft conflict avoidance via velocity regulation (2018)
  3. Carrizosa, Emilio; Guerrero, Vanesa; Romero Morales, Dolores: On mathematical optimization for the visualization of frequencies and adjacencies as rectangular maps (2018)
  4. de Pinho, Maria do Rosário; Maurer, Helmut; Zidani, Hasnaa: Optimal control of normalized SIMR models with vaccination and treatment (2018)
  5. Gelashvili, Koba; Khutsishvili, Irina; Gorgadze, Luka; Alkhazishvili, Lela: Speeding up the convergence of the Polyak’s heavy ball algorithm (2018)
  6. Jara-Moroni, Francisco; Pang, Jong-Shi; Wächter, Andreas: A study of the difference-of-convex approach for solving linear programs with complementarity constraints (2018)
  7. Renault, Vincent; Thieullen, Michèle; Trélat, Emmanuel: Minimal time spiking in various ChR2-controlled neuron models (2018)
  8. Rodrigues, Filipe; Silva, Cristiana J.; Torres, Delfim F. M.; Maurer, Helmut: Optimal control of a delayed HIV model (2018)
  9. Thäter, Markus; Chudej, Kurt; Pesch, Hans Josef: Optimal vaccination strategies for an SEIR model of infectious diseases with logistic growth (2018)
  10. Trélat, Emmanuel; Zhang, Can; Zuazua, Enrique: Steady-state and periodic exponential turnpike property for optimal control problems in Hilbert spaces (2018)
  11. Zhang, Weizhong; Wang, Dan; Inanc, Tamer: A multiphase DMOC-based trajectory optimization method (2018)
  12. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  13. Backe, Stian; Haugland, Dag: Strategic optimization of offshore wind farm installation (2017)
  14. Baharev, Ali; Domes, Ferenc; Neumaier, Arnold: A robust approach for finding all well-separated solutions of sparse systems of nonlinear equations (2017)
  15. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  16. Burachik, R. S.; Kaya, C. Y.; Rizvi, M. M.: A new scalarization technique and new algorithms to generate Pareto fronts (2017)
  17. Cacchiani, Valentina; D’Ambrosio, Claudia: A branch-and-bound based heuristic algorithm for convex multi-objective MINLPs (2017)
  18. Cafieri, Sonia; Omheni, Riadh: Mixed-integer nonlinear programming for aircraft conflict avoidance by sequentially applying velocity and heading angle changes (2017)
  19. D’Ambrosio, Claudia; Vu, Ky; Lavor, Carlile; Liberti, Leo; Maculan, Nelson: New error measures and methods for realizing protein graphs from distance data (2017)
  20. Després, Bruno: Polynomials with bounds and numerical approximation (2017)

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