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Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization

Abstract : Stable matching problems have several practical applications. If preference lists are truncated and contain ties, finding a stable matching with maximal size is computationally difficult. We address this problem using a local search technique, based on Adaptive Search and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods. Moreover, parallel versions (particularly versions with communication) improve performance so much that very large and hard instances can be solved quickly.
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Contributor : Danny Munera Connect in order to contact the contributor
Submitted on : Tuesday, April 21, 2015 - 11:25:59 AM
Last modification on : Friday, April 29, 2022 - 10:12:49 AM
Long-term archiving on: : Wednesday, April 19, 2017 - 1:12:18 AM


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  • HAL Id : hal-01144214, version 1


Danny Munera, Daniel Diaz, Salvador Abreu, Francesca Rossi, Vijay Saraswat, et al.. Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization. 29th AAAI Conference on Artificial Intelligence, Jan 2015, Austin, TX, United States. ⟨hal-01144214⟩



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