Mean-Field Approximations to the Longest Common Subsequence Problem

TL;DR

This paper refines mean-field approximations for the Longest Common Subsequence problem by systematically including correlations, improving accuracy through perturbative and simulation methods, and revealing non-perturbative effects in large alphabet limits.

Contribution

It introduces a systematic way to incorporate correlations into mean-field approximations for the LCS problem, enhancing the accuracy of the model.

Findings

Refined approximation series for LCS with correlations

Monte-Carlo simulations validate the improved models

Large S expansion shows non-perturbative correlation effects

Abstract

The Longest Common Subsequence (LCS) problem is a fundamental problem of sequence comparison. A natural approximation to this problem is a model in which every pairs of letters of two ``sequences'' are matched independently of the other pairs with probability 1/S, $S$ representing the size of the alphabet. This model is analogous to a mean field version of the LCS problem, which can be solved with a cavity approach (Eur. Phys. J. B 7-2(1999),pp. 293-308). We refine here this approximation by incorporating in a systematic way correlations among the matches in the cavity calculation. We obtain a series of closer and closer approximations to the LCS problem, which we quantify in the large $S$ limit, both with a perturbative approach and by Monte-Carlo simulations. We find that, as it happens in the expansion around mean-field for other disordered systems, the corrections to our approximations depend upon long-ranged correlation effects which render the large $S$ expansion non-perturbative.