Local branching

Fischetti, Matteo; Lodi, Andrea

doi:10.1007/s10107-003-0395-5

Cited by 641 publications

(407 citation statements)

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“…Fischetti and Lodi [15] introduced local branching, an exact approach combining the spirit of local search metaheuristics with a generic MIP-solver (CPLEX). They consider general MIPs with 0-1 variables.…”

Section: Metaheuristics For Strategic Guidance Of Exact Search Frencmentioning

confidence: 99%

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Puchinger

Raidl

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

211

117

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Abstract. In this survey we discuss different state-of-the-art approaches of combining exact algorithms and metaheuristics to solve combinatorial optimization problems. Some of these hybrids mainly aim at providing optimal solutions in shorter time, while others primarily focus on getting better heuristic solutions. The two main categories in which we divide the approaches are collaborative versus integrative combinations. We further classify the different techniques in a hierarchical way. Altogether, the surveyed work on combinations of exact algorithms and metaheuristics documents the usefulness and strong potential of this research direction.

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Section: Metaheuristics For Strategic Guidance Of Exact Search Frencmentioning

confidence: 99%

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Puchinger

Raidl

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

211

117

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“…The leafs visited for discrepancy 1 (in Figure 1.b correspond to all leafs that are reached when exactly one right branch is chosen, and so on. Note that the 'Local Branching' search strategy in the context of integer programming resembles limited discrepancy search [Fischetti and Lodi, 2003]. Namely, given a current solution x * and an integer k, Local Branching creates a subproblem in which k variables are allowed to deviate from the value taken in x * .…”

Section: Search Strategiesmentioning

confidence: 99%

Semidefinite Programming and Constraint Programming

Hoeve

2011

International Series in Operations Research &Amp; Management Science

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“…Both rely on certain subproblems of (2) obtained by adjoining appropriate constraints: VNS requires Local Branching type constraint [22], whilst FPMINLP requires a specific outer approximation. The reformulations listed in 2.-4. above can be implemented using the basic reformulations encoded in ROSE.…”

Section: Expression Tree Librarymentioning

confidence: 99%

The Reformulation-Optimization Software Engine

Liberti¹,

Cafieri²,

Savourey³

2010

Mathematical Software – ICMS 2010

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Abstract. Most optimization software performs numerical computation, in the sense that the main interest is to find numerical values to assign to the decision variables, e.g. a solution to an optimization problem. In mathematical programming, however, a considerable amount of symbolic transformation is essential to solving difficult optimization problems, e.g. relaxation or decomposition techniques. This step is usually carried out by hand, involves human ingenuity, and often constitutes the "theoretical contribution" of some research papers. We describe a Reformulation-Optimization Software Engine (ROSE) for performing (automatic) symbolic computation on mathematical programming formulations.

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Local branching

Cited by 641 publications

References 0 publications

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification

Semidefinite Programming and Constraint Programming

The Reformulation-Optimization Software Engine

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