FEASPUMP

Feasibility pump 2.0. Finding a feasible solution of a given mixed-integer programming (MIP) model is a very important 𝒩𝒫-complete problem that can be extremely hard in practice. Feasibility Pump (FP) is a heuristic scheme for finding a feasible solution to general MIPs that can be viewed as a clever way to round a sequence of fractional solutions of the LP relaxation, until a feasible one is eventually found. In this paper we study the effect of replacing the original rounding function (which is fast and simple, but somehow blind) with more clever rounding heuristics. In particular, we investigate the use of a diving-like procedure based on rounding and constraint propagation-a basic tool in Constraint Programming. Extensive computational results on binary and general integer MIPs from the literature show that the new approach produces a substantial improvement of the FP success rate, without slowing-down the method and with a significantly better quality of the feasible solutions found.


References in zbMATH (referenced in 92 articles )

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  1. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  2. Borwein, Jonathan M.; Giladi, Ohad: Convex analysis in groups and semigroups: a sampler (2018)
  3. Dey, Santanu S.; Iroume, Andres; Molinaro, Marco; Salvagnin, Domenico: Improving the randomization step in feasibility pump (2018)
  4. Hill, Alessandro; Voß, Stefan: Generalized local branching heuristics and the capacitated ring tree problem (2018)
  5. Kılınç, Mustafa R.; Sahinidis, Nikolaos V.: Exploiting integrality in the global optimization of mixed-integer nonlinear programming problems with BARON (2018)
  6. Munguía, Lluís-Miquel; Ahmed, Shabbir; Bader, David A.; Nemhauser, George L.; Shao, Yufen: Alternating criteria search: a parallel large neighborhood search algorithm for mixed integer programs (2018)
  7. Soylu, Banu: The search-and-remove algorithm for biobjective mixed-integer linear programming problems (2018)
  8. Andrade, Carlos E.; Ahmed, Shabbir; Nemhauser, George L.; Shao, Yufen: A hybrid primal heuristic for finding feasible solutions to mixed integer programs (2017)
  9. Arts, Joachim: A multi-item approach to repairable stocking and expediting in a fluctuating demand environment (2017)
  10. Belotti, Pietro; Berthold, Timo: Three ideas for a feasibility pump for nonconvex MINLP (2017)
  11. Dupin, Nicolas: Tighter MIP formulations for the discretised unit commitment problem with MIN-stop ramping constraints (2017)
  12. Eronen, Ville-Pekka; Kronqvist, Jan; Westerlund, Tapio; Mäkelä, Marko M.; Karmitsa, Napsu: Method for solving generalized convex nonsmooth mixed-integer nonlinear programming problems (2017)
  13. Geißler, Björn; Morsi, Antonio; Schewe, Lars; Schmidt, Martin: Penalty alternating direction methods for mixed-integer optimization: a new view on feasibility pumps (2017)
  14. Guastaroba, G.; Savelsbergh, M.; Speranza, M. G.: Adaptive kernel search: a heuristic for solving mixed integer linear programs (2017)
  15. Hanafi, Saïd; Todosijević, Raca: Mathematical programming based heuristics for the 0--1 MIP: a survey (2017)
  16. Hansen, Pierre; Mladenović, Nenad; Todosijević, Raca; Hanafi, Saïd: Variable neighborhood search: basics and variants (2017)
  17. Li, M.; Liu, Q.: Inexact feasibility pump for mixed integer nonlinear programming (2017)
  18. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  19. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  20. Sharma, Shaurya; Knudsen, Brage Rugstad; Grimstad, Bjarne: Towards an objective feasibility pump for convex minlps (2016)

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