Dynamic Programming

Dynamic Programming (DP) is a powerful technique for solving optimization problems by breaking them down into simpler subproblems and storing solutions to avoid redundant calculations.

Learning Map

Dynamic programming concepts and problem types organized by complexity.

Prerequisites

• Time & Space Complexity Analysis
• Arrays & Strings
• Hash Tables & Sets
• Trees & Binary Trees

What's in scope

• DP Fundamentals: Core concepts including overlapping subproblems, optimal substructure, and implementation approaches
• Classic DP Problems: Essential problems like Fibonacci, climbing stairs, house robber, and coin change
• Advanced DP Patterns: Complex patterns including knapsack, edit distance, and matrix chain multiplication
• DP on Trees & Graphs: Applying dynamic programming to tree and graph structures

How to use this section

• Start with DP Fundamentals to understand the core concepts
• Practice Classic DP Problems to build intuition
• Master Advanced DP Patterns for complex problems
• Explore DP on Trees & Graphs for specialized applications

📄️ Advanced DP Patterns

Complex dynamic programming patterns that require deeper understanding and more sophisticated state management.

📄️ Classic DP Problems

Master these fundamental dynamic programming problems that form the foundation for more complex DP challenges.

📄️ DP Fundamentals

Understanding the core concepts of dynamic programming is essential for recognizing when and how to apply this powerful technique.

📄️ DP on Trees & Graphs

Applying dynamic programming to tree and graph structures requires understanding how to traverse and compute optimal solutions on these data structures.