Hard Instances on Demand
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 random instances, which are known to be misleading: they may say more about the random generator than about the algorithmic question at hand. We treat instance generation as an optimization problem: parameterize the instance space, take the solver's running time as the objective, and let a black-box optimizer search for hard instances. We develop a widely applicable workflow built on the Nevergrad optimization platform and demonstrate it on four NP-hard problems: ...
| Authors | Jamie Fravel, Martin Koutecký, Pavlo Tsitsei |
|---|---|
| Primary category | cs.DS |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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