Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false)

The edit distance (a.k.a. the Levenshtein distance) between two strings is defined as the minimum number of insertions, deletions, or substitutions of symbols needed to transform one string into another. The problem of computing the edit distance between two strings is a classical computational task, with a well-known algorithm based on dynamic programming. Unfortunately, all known algorithms for this problem run in nearly quadratic time. In this paper we provide evidence that the near-quadratic running time bounds known for the problem of computing edit distance might be tight. Specifically, we show that if the edit distance can be computed in time $O(n^{2-\delta})$ for some constant $\delta>0$, then the satisfiability of conjunctive normal form formulas with $N$ variables and $M$ clauses can be solved in time $M^{O(1)} 2^{(1-\epsilon)N}$ for a constant $\epsilon>0$. The latter result would violate the strong exponential time hypothesis, which postulates that such algorithms do not exist.

[1]  Mike Paterson,et al.  A Faster Algorithm Computing String Edit Distances , 1980, J. Comput. Syst. Sci..

[2]  Russell Impagliazzo,et al.  Which problems have strongly exponential complexity? , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).

[3]  Russell Impagliazzo,et al.  On the Complexity of k-SAT , 2001, J. Comput. Syst. Sci..

[4]  Gonzalo Navarro,et al.  A guided tour to approximate string matching , 2001, CSUR.

[5]  Ryan Williams,et al.  A new algorithm for optimal 2-constraint satisfaction and its implications , 2005, Theor. Comput. Sci..

[6]  Alexandr Andoni,et al.  Polylogarithmic Approximation for Edit Distance and the Asymmetric Query Complexity , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

[7]  Mihai Patrascu,et al.  On the possibility of faster SAT algorithms , 2010, SODA '10.

[8]  Liam Roditty,et al.  Fast approximation algorithms for the diameter and radius of sparse graphs , 2013, STOC '13.

[9]  Karl Bringmann,et al.  Why Walking the Dog Takes Time: Frechet Distance Has No Strongly Subquadratic Algorithms Unless SETH Fails , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.

[10]  Oren Weimann,et al.  Consequences of Faster Alignment of Sequences , 2014, ICALP.

[11]  Amir Abboud,et al.  Popular Conjectures Imply Strong Lower Bounds for Dynamic Problems , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.

[12]  Huacheng Yu,et al.  More Applications of the Polynomial Method to Algorithm Design , 2015, SODA.

[13]  Virginia Vassilevska Williams,et al.  Hardness of Easy Problems: Basing Hardness on Popular Conjectures such as the Strong Exponential Time Hypothesis (Invited Talk) , 2015, IPEC.

[14]  Marvin Künnemann,et al.  Quadratic Conditional Lower Bounds for String Problems and Dynamic Time Warping , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

[15]  Amir Abboud,et al.  Tight Hardness Results for LCS and Other Sequence Similarity Measures , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

[16]  Timothy M. Chan,et al.  Deterministic APSP, Orthogonal Vectors, and More: Quickly Derandomizing Razborov-Smolensky , 2016, SODA.

[17]  Ryan Williams,et al.  Simulating branching programs with edit distance and friends: or: a polylog shaved is a lower bound made , 2015, STOC.