2012/10/30 by Lane A. Hemaspaandra, Ryan Williams, Hemaspaandra, Lane A. +1
Computer Science · #Artificial Intelligence (cs.AI) #Computational Complexity (cs.CC) #Data Structures and Algorithms (cs.DS) #F.1.3 #F.2.2 #FOS: Computer and information sciences #I.2.8 #cs.AI #cs.CC #cs.DS
paper · pdf · doi:10.48550/arxiv.1210.8099
This article is currently scheduled to appear in the December 2012 issue of SIGACT News
arxiv created 2012/10/30 · arxiv updated 2012/10/31
Heuristic approaches often do so well that they seem to pretty much always give the right answer. How close can heuristic algorithms get to always giving the right answer, without inducing seismic complexity-theoretic consequences? This article first discusses how a series of results by Berman, Buhrman, Hartmanis, Homer, Longpré, Ogiwara, Schöening, and Watanabe, from the early 1970s through the early 1990s, explicitly or implicitly limited how well heuristic algorithms can do on NP-hard problems. In particular, many desirable levels of heuristic success cannot be obtained unless severe, highly unlikely complexity class collapses occur. Second, we survey work initiated by Goldreich and Wigderson, who showed how under plausible assumptions deterministic heuristics for randomized computation can achieve a very high frequency of correctness. Finally, we consider formal ways in which theory can help explain the effectiveness of heuristics that solve NP-hard problems in practice.