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Improved Branch-and-Bound for Low Autocorrelation Binary Sequences

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arxiv 1305.6187 v2 pith:ANMJCU7S submitted 2013-05-27 cs.AI

S. D. Prestwich This is my paper
classification cs.AI
keywords branch-and-boundautocorrelationbinaryproblemprogressedapplicationsbettercomplete
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The Low Autocorrelation Binary Sequence problem has applications in telecommunications, is of theoretical interest to physicists, and has inspired many optimisation researchers. Metaheuristics for the problem have progressed greatly in recent years but complete search has not progressed since a branch-and-bound method of 1996. In this paper we find four ways of improving branch-and-bound, leading to a tighter relaxation, faster convergence to optimality, and better empirical scalability.

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  1. Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem

    cs.LG 2026-06 conditional novelty 5.0 of 10

    Thompson sampling over LABS restriction classes plus GPU self-avoiding walks yields new best merit factors for 35 lengths in 450–527 and L=573, including F=8.0555 at L=451.

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