AlphaECP

GAMS/AlphaECP is a MINLP (Mixed-Integer Non-Linear Programming) solver based on the extended cutting plane (ECP) method. The solver can be applied to general MINLP problems and global optimal solutions can be ensured for pseudo-convex MINLP problems. The ECP method is an extension of Kelley’s cutting plane method which was originally given for convex NLP problems (Kelley, 1960). The method requires only the solution of a MIP sub problem in each iteration. The MIP sub problems may be solved to optimality, but can also be solved to feasibility or only to an integer relaxed solution in intermediate iterations. This makes the ECP algorithm efficient and easy to implement. Futher information about the underlying algorithm can be found in Westerlund T. and Pörn R. (2002). Solving Pseudo-Convex Mixed Integer Optimization Problems by Cutting Plane Techniques. Optimization and Engineering, 3. 253-280.


References in zbMATH (referenced in 37 articles )

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  1. Cozad, Alison; Sahinidis, Nikolaos V.: A global MINLP approach to symbolic regression (2018)
  2. Delfino, A.; de Oliveira, W.: Outer-approximation algorithms for nonsmooth convex MINLP problems (2018)
  3. Kronqvist, Jan; Lundell, Andreas; Westerlund, Tapio: Reformulations for utilizing separability when solving convex MINLP problems (2018)
  4. Oliphant, Terry-Leigh; Ali, M. Montaz: A trajectory-based method for mixed integer nonlinear programming problems (2018)
  5. Westerlund, Tapio; Eronen, Ville-Pekka; Mäkelä, Marko M.: On solving generalized convex MINLP problems using supporting hyperplane techniques (2018)
  6. Canca, David; De-Los-Santos, Alicia; Laporte, Gilbert; Mesa, Juan A.: An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem (2017)
  7. Canca, David; Barrena, Eva; Laporte, Gilbert; Ortega, Francisco A.: A short-turning policy for the management of demand disruptions in rapid transit systems (2016)
  8. de Oliveira, Welington: Regularized optimization methods for convex MINLP problems (2016)
  9. Kronqvist, Jan; Lundell, Andreas; Westerlund, Tapio: The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming (2016)
  10. van Ackooij, W.; Frangioni, A.; de Oliveira, W.: Inexact stabilized Benders’ decomposition approaches with application to chance-constrained problems with finite support (2016)
  11. Hiller, Benjamin; Humpola, Jesco; Lehmann, Thomas; Lenz, Ralf; Morsi, Antonio; Pfetsch, Marc E.; Schewe, Lars; Schmidt, Martin; Schwarz, Robert; Schweiger, Jonas; Stangl, Claudia; Willert, Bernhard M.: Computational results for validation of nominations (2015)
  12. Wei, Zhou; Ali, M. Montaz: Generalized Benders decomposition for one class of MINLPs with vector conic constraint (2015)
  13. Wei, Zhou; Ali, M. Montaz: Convex mixed integer nonlinear programming problems and an outer approximation algorithm (2015)
  14. Patil, Bhagyesh V.; Nataraj, P. S. V.: An improved Bernstein global optimization algorithm for MINLP problems with application in process industry (2014)
  15. D’ambrosio, Claudia; Lodi, Andrea: Mixed integer nonlinear programming tools: an updated practical overview (2013)
  16. Li, Min; Vicente, Luís Nunes: Inexact solution of NLP subproblems in MINLP (2013)
  17. Van Dinter, Jennifer; Rebennack, Steffen; Kallrath, Josef; Denholm, Paul; Newman, Alexandra: The unit commitment model with concave emissions costs: a hybrid Benders’ decomposition with nonconvex master problems (2013)
  18. Bonami, Pierre; Kilinç, Mustafa; Linderoth, Jeff: Algorithms and software for convex mixed integer nonlinear programs (2012)
  19. Exler, Oliver; Lehmann, Thomas; Schittkowski, Klaus: A comparative study of SQP-type algorithms for nonlinear and nonconvex mixed-integer optimization (2012)
  20. Grossmann, Ignacio E.; Ruiz, Juan P.: Generalized disjunctive programming: a framework for formulation and alternative algorithms for MINLP optimization (2012)

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