3 accepted papers
Recent work has shown that machine-learned predictions can provably improve the performance of classic algorithms. In this work, we propose the first minimum-cost network flow algorithm augmented with a dual prediction. Our method is based on a classic minimum-cost flow algorithm, namely ε-relaxatio
Algorithm configuration, which involves selecting algorithm parameters based on sampled problem instances, is a crucial step in applying modern algorithms such as SAT solvers. Although prior work has attempted to understand the theoretical foundations of algorithm configuration, we still lack a comp…
Multi-objective optimization problems arise widely in various fields. In practice, multi-objective optimization is generally solved by heuristics with tunable parameters that are highly application-specific. Tuning parameters based on real-world instances (a.k.a. algorithm configuration) are general…