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Combining heuristics and Exact Algorithms: A Review

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arxiv 2202.02799 v1 pith:26OIMLAE submitted 2022-02-06 math.OC

classification math.OC
keywords differentmathematicalmethodsproblemsprogrammingseveraladvantagesalgorithms
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Several different ways exist for approaching hard optimization problems. Mathematical programming techniques, including (integer) linear programming-based methods and metaheuristic approaches, are two highly successful streams for combinatorial problems. These two have been established by different communities more or less in isolation from each other. Only over several years ago, a larger number of researchers recognized the advantages and huge potentials of building hybrids of mathematical programming methods and metaheuristics.

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  1. Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling

    cs.AI 2026-08 conditional novelty 6.0 of 10

    OptiDSL maps optimization problem descriptions to domain-specific language templates and reports large gains over MILP-based LLM formulation pipelines, with the caveat that its headline benchmark is self-generated.

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