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Machine learning advances computation of complex link invariants

By PulseAugur Editorial · [1 sources] ·

Researchers have employed machine learning techniques, specifically reinforcement learning and Bayesian optimization, to establish new upper bounds for challenging link invariants like the slice genus and unknotting number. By combining these with existing lower bounds, the study successfully computed exact values for these invariants in numerous instances. The developed unknotting agents also demonstrated the ability to replicate the non-additivity of the unknotting number for specific counterexamples, even discovering novel unknotting trajectories. AI

IMPACT Introduces novel applications of machine learning in abstract mathematics, potentially inspiring new research directions.

RANK_REASON The item is an academic paper detailing novel research methods and findings. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning advances computation of complex link invariants

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The item is an academic paper detailing novel research methods and findings. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yutong Dai, Oliver Hayman, Andr\'as Juh\'asz, Ludovico Morellato ·

    Computations of the slice genus and the unknotting number of links via machine learning

    arXiv:2610.10206v1 Announce Type: cross Abstract: Links are disjoint unions of circles smoothly embedded in $S^3$. We use reinforcement learning and Bayesian optimisation to obtain new upper bounds on several link invariants that are not known to be algorithmically computable: th…

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