Beyond the Longest Letter-duplicated Subsequence Problem

TL;DR

This paper investigates variants of the longest letter-duplicated subsequence problem, proving NP-hardness for certain cases, providing approximation algorithms for others, and developing an $O(n^2)$ dynamic programming solution for the weighted version.

Contribution

It introduces new complexity results and algorithms for multiple variants of the letter-duplicated subsequence problem, including NP-hardness proofs and approximation strategies.

Findings

NP-hardness for the constrained version with unbounded alphabet and certain letter frequency conditions

A 1.5-approximation algorithm when each letter appears at most 3 times

An $O(n^2)$ dynamic programming algorithm for the weighted version

Abstract

Given a sequence $S$ of length $n$, a letter-duplicated subsequence is a subsequence of $S$ in the form of $x_1^{d_1}x_2^{d_2}\cdots x_k^{d_k}$ with $x_i\in\Sigma$, $x_j\neq x_{j+1}$ and $d_i\geq 2$ for all $i$ in $[k]$ and $j$ in $[k-1]$. A linear time algorithm for computing the longest letter-duplicated subsequence (LLDS) of $S$ can be easily obtained. In this paper, we focus on two variants of this problem. We first consider the constrained version when $\Sigma$ is unbounded, each letter appears in $S$ at least 6 times and all the letters in $\Sigma$ must appear in the solution. We show that the problem is NP-hard (a further twist indicates that the problem does not admit any polynomial time approximation). The reduction is from possibly the simplest version of SAT that is NP-complete, $(\leq 2,1,\leq 3)$-SAT, where each variable appears at most twice positively and exact once negatively, and each clause contains at most three literals and some clauses must contain exactly two literals. (We hope that this technique will serve as a general tool to help us proving the NP-hardness for some more tricky sequence problems involving only one sequence -- much harder than with at least two input sequences, which we apply successfully at the end of the paper on some extra variations of the LLDS problem.) We then show that when each letter appears in $S$ at most 3 times, then the problem admits a factor $1.5-O(\frac{1}{n})$ approximation. Finally, we consider the weighted version, where the weight of a block $x_i^{d_i} (d_i\geq 2)$ could be any positive function which might not grow with $d_i$. We give a non-trivial $O(n^2)$ time dynamic programming algorithm for this version, i.e., computing an LD-subsequence of $S$ whose weight is maximized.