2017/03/20 by Jhoirene Clemente, Clemente, Jhoirene B., Henry N. Adorna +1
Computer Science · #68W25 #Algorithms and Data Compression #Complexity and Algorithms in Graphs #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #G.2.1 #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.1703.06644
openalex publication_date 2017/03/20 · openalex created_date 2017/04/07 · openalex updated_date 2026/07/28
This study investigates whether reoptimization can help in solving the closest substring problem. We are dealing with the following reoptimization scenario. Suppose, we have an optimal l-length closest substring of a given set of sequences S. How can this information be beneficial in obtaining an (l+k)-length closest substring for S? In this study, we show that the problem is still computationally hard even with k=1. We present greedy approximation algorithms that make use of the given information and prove that it has an additive error that grows as the parameter k increases. Furthermore, we present hard instances for each algorithm to show that the computed approximation ratio is tight. We also show that we can slightly improve the running-time of the existing polynomial-time approximation scheme (PTAS) for the original problem through reoptimization.