Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem
This paper introduces a hybrid search framework that combines Thompson sampling with parallel self-avoiding walks to efficiently allocate computational resources in the Low Autocorrelation Binary Sequences (LABS) problem. The method, modeled as a multi-armed bandit, dynamically prioritizes promising search space partitions, achieving new best-known results for 35 sequence lengths and a longest sequence with merit factor exceeding 8.0.
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[Submitted on 17 Jun 2026]
Title:Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem
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Abstract:Low autocorrelation binary sequences problem (LABS) is a hard combinatorial optimization challenge with important applications in communications, signal processing, and satellite navigation. This paper proposes a hybrid search framework that combines Thompson sampling with parallel self-avoiding walks to adaptively allocate computational effort across restriction classes of the LABS search space. By modeling partitions as arms in a multi-armed bandit setting, the proposed method dynamically shifts search resources toward partitions that empirically produce higher merit factors while maintaining exploration of less-sampled regions. The approach is further accelerated through GPU-parallel execution, shared posterior updates, efficient neighborhood evaluation, and a Bloom filter for cycle prevention. In addition, we use a two-stage optimization strategy that first searches constrained partitioned skew-symmetric spaces and then refines the best candidates in the unrestricted space. Experiments on long binary sequences show that the proposed method improves the previously best-known results for 35 sequence lengths in the range $450 \le L \le 527$ and for $L=573$. In particular, we report a new longest sequence with merit factor exceeding $8.0$, obtained for $L=451$. The results also show that Thompson sampling effectively prioritizes partitions with better observed performance, confirming the value of online, data-driven resource allocation in LABS optimization. Overall, the proposed framework provides a scalable and effective strategy for high-performance merit factor maximization.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.09688 [cs.LG]
(or arXiv:2607.09688v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.09688
arXiv-issued DOI via DataCite
Submission history
From: Blaž Pšeničnik [view email] [v1] Wed, 17 Jun 2026 09:17:04 UTC (752 KB)
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