Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
This work addresses the need for efficient and adaptive algorithm configuration methods in optimization, offering a solution that improves upon prior approaches by combining key properties without significant trade-offs.
The paper tackles the problem of algorithm configuration by introducing a new method, 'Structured Procrastination with Confidence', that achieves near-optimal performance with high probability and minimal runtime in worst-case scenarios, while adding adaptivity to algorithm characteristics and preserving an anytime property for tightening guarantees over time.
Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure ("Structured Procrastination") that provably achieves near optimal performance with high probability and with nearly minimal runtime in the worst case. It also offers an $\textit{anytime}$ property: it keeps tightening its optimality guarantees the longer it is run. Unfortunately, Structured Procrastination is not $\textit{adaptive}$ to characteristics of the parameterized algorithm: it treats every input like the worst case. Follow-up work ("LeapsAndBounds") achieves adaptivity but trades away the anytime property. This paper introduces a new algorithm, "Structured Procrastination with Confidence", that preserves the near-optimality and anytime properties of Structured Procrastination while adding adaptivity. In particular, the new algorithm will perform dramatically faster in settings where many algorithm configurations perform poorly. We show empirically both that such settings arise frequently in practice and that the anytime property is useful for finding good configurations quickly.