Approximating binary longest common subsequence in almost-linear time

TL;DR

This paper presents a near-linear time algorithm that improves the approximation ratio for binary longest common subsequence, surpassing the simple 1/2-approximation, and extends the result to strings over larger alphabets.

Contribution

It introduces a new approximation algorithm for binary LCS with ratio better than 1/2 in almost-linear time, generalizing previous results to unequal length strings.

Findings

Achieves a (rac{1}{2}+ ext{delta})-approximation in n^{1+epsilon} time for binary LCS.

Extends approximation results to q-ary strings in near-linear time.

Develops a new combinatorial structure lemma for strings based on oscillation patterns.

Abstract

The Longest Common Subsequence (LCS) is a fundamental string similarity measure, and computing the LCS of two strings is a classic algorithms question. A textbook dynamic programming algorithm gives an exact algorithm in quadratic time, and this is essentially best possible under plausible fine-grained complexity assumptions, so a natural problem is to find faster approximation algorithms. When the inputs are two binary strings, there is a simple $\frac{1}{2}$-approximation in linear time: compute the longest common all-0s or all-1s subsequence. It has been open whether a better approximation is possible even in truly subquadratic time. Rubinstein and Song showed that the answer is yes under the assumption that the two input strings have equal lengths. We settle the question, generalizing their result to unequal length strings, proving that, for any $\varepsilon>0$, there exists $\delta>0$ and a $(\frac{1}{2}+\delta)$-approximation algorithm for binary LCS that runs in $n^{1+\varepsilon}$ time. As a consequence of our result and a result of Akmal and Vassilevska-Williams, for any $\varepsilon>0$, there exists a $(\frac{1}{q}+\delta)$-approximation for LCS over $q$-ary strings in $n^{1+\varepsilon}$ time. Our techniques build on the recent work of Guruswami, He, and Li who proved new bounds for error-correcting codes tolerating deletion errors. They prove a combinatorial "structure lemma" for strings which classifies them according to their oscillation patterns. We prove and use an algorithmic generalization of this structure lemma, which may be of independent interest.