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New method generates hard instances for NP-hard problems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for generating challenging instances for optimization problems, treating instance creation as an optimization task itself. This approach uses a black-box optimizer to search for instances that maximize solver running time, requiring only the solver, an instance encoder, and a compute budget. The method was successfully applied to four NP-hard problems, including satisfiability and the traveling salesperson problem, yielding instances significantly harder than random draws and competitive with curated benchmark sets. AI

IMPACT This method could improve the reliability of AI model evaluations by ensuring they are tested on genuinely challenging problem instances.

RANK_REASON The item is an academic paper detailing a new methodology for generating hard instances for optimization problems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New method generates hard instances for NP-hard problems

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The item is an academic paper detailing a new methodology for generating hard instances for optimization problems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jamie Fravel, Martin Kouteck\'y, Pavlo Tsitsei ·

    Hard Instances on Demand

    arXiv:2610.22637v1 Announce Type: cross Abstract: The conclusions of an experimental study of algorithms are only as good as the instances they are run on. Benchmark libraries for well-studied problems took decades to curate, while evaluations of new problems often fall back on r…

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