string manipulation
repetitive sequence
algorithm
pattern detection
computational problem

Find longest repetitive sequence in a string

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Introduction

Finding the longest repetitive sequence within a string is a common problem in computer science, particularly in fields such as bioinformatics, data compression, and text processing. The challenge is to efficiently identify the longest contiguous sequence of characters that appear more than once in a given string.

Problem Definition

Given a string `S`, the objective is to determine the longest substring that is repeated two or more times. For example, in the string `"banana"`, the longest substring that appears more than once is `"ana"`.

Technical Explanation

Approaches to Solve the Problem

Several algorithms can address this problem, each with distinct time and space complexities.

1. Naive Approach

The simplest approach is to use two nested loops to create all possible substrings and then check for repetitions. While straightforward, it is highly inefficient with a time complexity of O(n3)O(n^3), where nn is the length of the string.

  • Suffix Array: A sorted array of all suffixes of a string.
  • LCP Array: An array where each value represents the longest common prefix length between consecutive suffixes in the suffix array.
  • Suffixes: `["banana", "anana", "nana", "ana", "na", "a"]`
  • Sorted Suffixes: `["a", "ana", "anana", "banana", "na", "nana"]`
  • Suffix Array: `[5, 3, 1, 0, 4, 2]`
  • LCP Array: `[1, 3, 0, 0, 2]`
  • Longest Repeated Substring: `"ana"`
  • Bioinformatics: Identifying repeated sequences can assist in DNA sequence analysis.
  • Data Compression: Detecting redundancy can optimize storage.
  • Text Processing: Useful in plagiarism detection and data deduplication.

Related reading
Course
Intermediate
27 lessons
15 hours
DSA Fundamentals

Master algorithmic patterns and data structures through hands-on LeetCode-style problems - from arrays and hashing to dynamic programming and advanced graphs.

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Data Structures & Algorithms practice on Codemia

Step through 300 algorithm problems with animated visualisers that show the data structure changing as the code runs.

Practice algorithms

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