String Algorithms and Pattern Matching Questions

Covers algorithmic techniques and practical skills for solving string problems and pattern matching tasks. Core algorithm knowledge includes substring search and pattern matching algorithms such as Knuth Morris Pratt, Rabin Karp, Boyer Moore, Z algorithm, Aho Corasick for multiple pattern matching, and rolling hash methods. Data structures and suffix structures are important, including tries, suffix arrays, suffix trees, and suffix automata, together with longest common prefix arrays and related construction techniques. Also includes dynamic programming approaches for string problems such as edit distance and longest common subsequence, palindrome and anagram detection methods, and regular expression concepts and engine behavior. Emphasizes algorithmic complexity analysis, time and space trade offs, memory and streaming constraints, and optimization strategies for very long inputs and high throughput text processing. Practical considerations include parsing and string manipulation idioms in common languages, Unicode and character encoding issues, edge case handling, test case design for strings, and real world applications such as log analysis, text search, and data transformation.

MediumTechnical

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Given a string s, implement longest_repeated_substring(s: str) -> str using suffix array and LCP array. For the interview, it's acceptable to build suffixes and sort them (O(n log n)), compute adjacent LCPs, and return the longest repeated substring. Discuss complexity and how to improve to near-linear construction for large inputs.

EasyTechnical

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Explain the difference between NFA-based and DFA-based regular expression engines. Discuss how features like backreferences and capture groups impact implementation and performance. Provide an example regex that can cause exponential backtracking in an NFA engine and explain why.

EasyTechnical

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Write a Python function find_substring_occurrences(text: str, pattern: str) -> List[int] that returns all starting indices where pattern appears in text. Assume ASCII for simplicity. Return overlapping matches (e.g., text='ababa', pattern='aba' -> [0,2]). Discuss time and space complexity and compare your naive approach to using Python's built-in str.find/str.index. Describe edge cases you would test (empty pattern, pattern longer than text, repeated characters).

HardTechnical

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Given a precomputed suffix array SA and LCP array for string s, describe how to find all occurrences of a pattern p using binary search on SA and using LCP values to accelerate comparisons. Provide complexity analysis and detail how to obtain occurrence positions sorted by original index.

MediumTechnical

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Implement KMP in Python: write find_all_kmp(text: str, pattern: str) -> List[int] that returns all starting indices of overlapping matches. Your implementation should compute the prefix-function in O(m) and perform the search in O(n+m). Handle edge cases: empty pattern, pattern longer than text, and Unicode characters.

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