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 522 articles , 2 standard articles )

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  1. Gambella, Claudio; Maggioni, Francesca; Vigo, Daniele: A stochastic programming model for a tactical solid waste management problem (2019)
  2. Amaya Moreno, Liana; Fügenschuh, Armin; Kaier, Anton; Schlobach, Swen: A nonlinear model for vertical free-flight trajectory planning (2018)
  3. Arreckx, Sylvain; Orban, Dominique: A regularized factorization-free method for equality-constrained optimization (2018)
  4. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  5. Bruglieri, Maurizio; Pezzella, Ferdinando; Pisacane, Ornella: A two-phase optimization method for a multiobjective vehicle relocation problem in electric carsharing systems (2018)
  6. Cafieri, Sonia; Cellier, Loïc; Messine, Frédéric; Omheni, Riadh: Combination of optimal control approaches for aircraft conflict avoidance via velocity regulation (2018)
  7. Cafieri, Sonia; D’Ambrosio, Claudia: Feasibility pump for aircraft deconfliction with speed regulation (2018)
  8. Camponogara, Eduardo; Guardini, Luiz Alberto; de Assis, Leonardo Salsano: Scheduling pumpoff operations in onshore oilfields with electric-power constraints and variable cycle time (2018)
  9. Carrizosa, Emilio; Guerrero, Vanesa; Romero Morales, Dolores: On mathematical optimization for the visualization of frequencies and adjacencies as rectangular maps (2018)
  10. de Pinho, Maria do Rosário; Maurer, Helmut; Zidani, Hasnaa: Optimal control of normalized SIMR models with vaccination and treatment (2018)
  11. Gay, David M.: Revisiting expression representations for nonlinear AMPL models (2018)
  12. Gelashvili, Koba; Khutsishvili, Irina; Gorgadze, Luka; Alkhazishvili, Lela: Speeding up the convergence of the Polyak’s heavy ball algorithm (2018)
  13. Göllmann, Laurenz; Maurer, Helmut: Optimal control problems with time delays: two case studies in biomedicine (2018)
  14. Hossain, Shahadat; Hakim Mithila, Nasrin: Pattern graph for sparse Hessian matrix (2018)
  15. Jara-Moroni, Francisco; Pang, Jong-Shi; Wächter, Andreas: A study of the difference-of-convex approach for solving linear programs with complementarity constraints (2018)
  16. Jung, Michael N.; Kirches, Christian; Sager, Sebastian; Sass, Susanne: Computational approaches for mixed integer optimal control problems with indicator constraints (2018)
  17. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: Integrality gap minimization heuristics for binary mixed integer nonlinear programming (2018)
  18. Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; Zavala, Victor M.; Biegler, Lorenz T.: pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations (2018)
  19. Petra, C. G.; Qiang, F.; Lubin, M.; Huchette, J.: On efficient Hessian computation using the edge pushing algorithm in Julia (2018)
  20. Renault, Vincent; Thieullen, Michèle; Trélat, Emmanuel: Minimal time spiking in various ChR2-controlled neuron models (2018)

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