Dynamic Programming (DP) is one of the most challenging yet rewarding topics in technical interviews. Master the art of breaking problems into overlapping subproblems and building optimal solutions from the bottom up.
Core Concepts:
Memoization: Top-down recursive approach with caching
Tabulation: Bottom-up iterative approach
State definition: Identifying what to store in DP table
Recurrence relation: Mathematical formula for subproblems
Common Patterns:
0/1 Knapsack and variations
Longest Common Subsequence (LCS)
Edit distance and string matching
Matrix chain multiplication
Subset sum and partition problems
When to Use DP:
Problem has optimal substructure
Overlapping subproblems exist
Need to find optimal value (min/max)
Counting possibilities or combinations
Difficulty: Medium to Hard. Practice 20-30 problems to build pattern recognition.
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3 min read Longest Common Subsequence (LCS) is a fundamental string DP problem that appears in interviews at Google, Microsoft, and Amazon — Read article
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