Published In Confronting Hardness Using a Hybrid Approach

combinatorial optimization A hybrid algorithm is a collection of heuristics, paired with a polynomial time selector S that runs on the input to decide which heuristic should be executed to solve the problem. Hybrid algorithms are interesting in scenarios where the selector must decide between heuristics that are "good " with respect to different complexity measures. In this paper, we focus on hybrid algorithms with a "hardness-defying " property: for a problem II, there is a set of complexity measures {rrii} whereby II is known or conjectured to be hard (or unsolvable) for each ra^, but for each heuristic hi of the hybrid algorithm, one can give a complexity guarantee for hi on the instances of II that S selects for hi that is strictly better than rrii. For example, we show that for NP-hard problems such as MAX-E/c-LlN-p, LONGEST PATH and MINIMUM BANDWIDTH, a given instance can either be solved exactly in "sub-exponential " (2°^) time, or be approximated in polynomial time with an approximation ratio exceeding that of the known or conjectured inapproximability of the problem, assuming P 7 ^ NP. We also prove some inherent limitations to the design of hybrid algorithms that arise under the assumption that N