The feasibility pump

By Matteo Fischetti, Fred W. Glover, Andrea Lodi. Mathematical programming, 2005

455 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/5863baef912f54cd81179c1468c0beb46209a084

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Citations in the corpus

Mixed-integer nonlinear optimization*† (2013, methodology): “This version requires random changes in x′ whenever cycling occurs, similar to the work of (Fischetti et al., 2005).”
MIPLIB 2010 - Mixed Integer Programming Library version 5 (2011, unknown): “Table 3 continued Name Originator and description biella1, dc1c, dc1l, dolom1, nsr8k, siena1 Double-Click SAS [54,55] Crew scheduling instances bienst2, binkar10_1 H.”
Primal Heuristics for Mixed Integer Programs (2006, methodology): “One was proposed by Fischetti, Glover, and Lodi [24], who suggested to add an objective cutoff and iterate the whole solving process.”
Tight Mixed Integer Linear Programming Formulations for the Unit Commitment Problem (2012, methodology): “A major advancement in CPLEX between these versions has been the implementation of the feasibility pump [17], a heuristic used to find feasible solutions.”
Variable neighborhood search: basics and variants (2017, background): “branching (VNB) [29] and Feasibility pump heuristics [17].”
Integer-Programming Software Systems (2005, background): “Fortunately, several researchers have produced promising computational results using novel ideas, such as local branching (Fischetti and Lodi, 2003) and its variants (Danna, Rothberg, and Pape, 2003; Fischetti, Glover, and Lodi, 2005 ).”
Active-constraint variable ordering for faster feasibility of mixed integer linear programs (2007, methodology): “Gauthier and Ribiere [1977] developed “automatic ordered branching” in which the integer variables are sorted in decreasing order of estimated degradation in the objective function value, based on calculations using the pseudo-costs of the variables.”
Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning (2016, unknown): “Heuristics can be used to find initial feasible solutions prior to starting the B&B search [21,22] and to improve existing incumbent solutions [23–25].”
Algorithms and Software for Convex Mixed Integer Nonlinear Programs (2012, methodology): “[84] A. Lodi, MIP computation and beyond, in 50 Years of Integer Programming 1958—2008, M. Jünger, T. Liebling, D. Naddef, W. Pulleyblank, G. Reinelt, G. Rinaldi, and L. Wolsey, eds., Springer, 2009.”