A hardness result and new algorithm for the longest common palindromic subsequence problem

TL;DR

This paper establishes the computational hardness of the 2-LCPS problem by relating it to the 4-LCS problem and introduces a new, more efficient algorithm for solving 2-LCPS based on string matching and character set properties.

Contribution

The paper proves the 2-LCPS problem is as hard as the 4-LCS problem and presents a novel algorithm with improved runtime under certain conditions.

Findings

The 2-LCPS problem is at least as hard as the 4-LCS problem.

The new algorithm runs in O(σ M^2 + n) time, improving over previous methods.

The algorithm is faster when the number of common characters σ is small.

Abstract

The 2-LCPS problem, first introduced by Chowdhury et al. [Fundam. Inform., 129(4):329-340, 2014], asks one to compute (the length of) a longest palindromic common subsequence between two given strings $A$ and $B$. We show that the 2-LCPS problem is at least as hard as the well-studied longest common subsequence problem for four strings (the 4-LCS problem). Then, we present a new algorithm which solves the 2-LCPS problem in $O(\sigma M^2 + n)$ time, where $n$ denotes the length of $A$ and $B$, $M$ denotes the number of matching positions between $A$ and $B$, and $\sigma$ denotes the number of distinct characters occurring in both $A$ and $B$. Our new algorithm is faster than Chowdhury et al.'s sparse algorithm when $\sigma = o(\log^2n \log\log n)$.