Approximation Algorithms for the Longest Run Subsequence Problem

Abstract

We study the approximability of the Longest Run Subsequence problem (LRS for short). For a string S = s_1 ? s_n over an alphabet ?, a run of a symbol ? ? ? in S is a maximal substring of consecutive occurrences of ?. A run subsequence S\u27 of S is a sequence in which every symbol ? ? ? occurs in at most one run. Given a string S, the goal of LRS is to find a longest run subsequence S^* of S such that the length |S^*| is maximized over all the run subsequences of S. It is known that LRS is APX-hard even if each symbol has at most two occurrences in the input string, and that LRS admits a polynomial-time k-approximation algorithm if the number of occurrences of every symbol in the input string is bounded by k. In this paper, we design a polynomial-time (k+1)/2-approximation algorithm for LRS under the k-occurrence constraint on input strings. For the case k = 2, we further improve the approximation ratio from 3/2 to 4/3