String

String Cheat Sheet

Strings, sequences of characters, share many similarities with arrays, making an understanding of arrays beneficial before exploring strings. They are pivotal in coding interviews, where a deep comprehension of string manipulation and analysis can distinguish a candidate.

Common Data Structures for Strings

• Trie/Prefix Tree: Optimizes word or prefix lookups, facilitating operations like autocomplete.

• Suffix Tree: Supports complex string manipulations and efficient pattern searches, crucial for substring identification and analysis.

Common String Algorithms

• Rabin Karp: Employs a rolling hash for efficient substring searches, ideal for plagiarism detection.

• KMP (Knuth-Morris-Pratt): Eliminates backtracking in substring searches, enhancing efficiency in pattern matching.

Time Complexity of String Operations

• Access: O(1), direct index access in most languages.

• Search: O(n), varies based on the algorithm for specific patterns.

• Insert: O(n), considering potential reallocation and shifting.

• Remove: O(n), due to shifting elements in the string.

Operations Involving Another String

• Find Substring: O(n*m) for the naive approach, with n and m being the lengths of the strings involved.

• Concatenate: O(n + m), combining two strings.

• Slice: O(m), extracting a substring.

• Split: O(n + m), dividing a string based on a delimiter.

• Strip: O(n), removing leading and trailing whitespaces.

Considerations

• Character Set and Sensitivity: Clarify if the problem is case-sensitive and the allowed character set, as this can affect the solution approach.

• Corner Cases: Prepare for scenarios like empty strings, strings with a single character, and strings with repeated or unique characters.

Techniques for String Problems

• Counting Characters: Utilize a hash table for frequency counting, an O(1) space complexity due to the fixed number of characters.

• Unique Characters: Implement a 26-bit bitmask for tracking unique characters, efficient for problems involving lowercase Latin characters.

• Anagrams: Determine anagrams through sorting, prime factor decomposition, or frequency counting, each method providing a different efficiency.

• Palindromes: Identify palindromes by reversing the string or using two pointers,. Char sequence is important here, not count.

Practice Problems

Leetcode	Pattern Names	Difficulty
First Unique Character in a String	Counting Characters, Unique Characters	Easy
Valid Anagram	Anagrams	Easy
Valid Palindrome	Palindromes	Easy
Palindrome Permutation	Palindromes, Anagrams	Easy
Ransom Note	Counting Characters	Easy
Implement strStr()	KMP	Easy
Longest Palindrome	Palindromes, Counting Characters	Easy
Detect Capital	Unique Characters	Easy
Reverse String	Palindromes	Easy
Repeated Substring Pattern	KMP	Easy
Group Anagrams	Anagrams	Medium
Longest Substring Without Repeating Characters	Unique Characters	Medium
Find All Anagrams in a String	Anagrams, Rabin Karp	Medium
Longest Palindromic Substring	Palindromes	Medium
Palindrome Pairs	Palindromes	Medium
Minimum Window Substring	Counting Characters	Medium
Substring with Concatenation of All Words	Counting Characters	Medium
Partition Labels	Unique Characters	Medium
Permutation in String	Anagrams	Medium
Longest Repeating Character Replacement	Counting Characters	Medium

Additional Topics

• String Compression and Decompression: Understanding algorithms for efficiently encoding and decoding strings.

• Regular Expressions: Leveraging regex for pattern matching and string manipulation tasks.

• Dynamic Programming on Strings: Solving problems like edit distance and substring matching through dynamic programming.