Lesson 277
Longest Common Substring
Dynamic Programming Grid
1:00How dynamic programming finds the longest contiguous block of characters shared by two strings in O(m·n) time using a reset-to-zero recurrence.
By the end, you can
- Explain the difference between a substring and a subsequence, and why that difference changes the DP recurrence.
- State what dp[i][j] represents and derive the match and mismatch cases from first principles.
- Fill a DP grid by hand for small strings and identify the maximum cell.
- Explain why the reset to zero (not carry-forward) enforces contiguity.
- Recover the longest common substring from the dp grid using the tracked ending index.
- Reduce space usage from O(m·n) to O(min(m,n)) using two rolling rows.
- State the time and space complexity of the DP solution and compare it to the suffix tree approach.
- Name at least two real-world domains where longest common substring appears (e.g., diff tools, bioinformatics).
Up next in String Algorithms, Advanced DP & Competitive Techniques




