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README.md

🔗 Dynamic Programming

This directory contains solutions to the 19 dynamic programming problems from the CSES Problem Set. These problems teach fundamental DP concepts and techniques.

🎯 Problems Overview

# Problem Name Difficulty Topic Solution
1 Dice Combinations ★★☆☆☆ Basic DP View Solution
2 Minimizing Coins ★★☆☆☆ Coin Change View Solution
3 Coin Combinations I ★★☆☆☆ Unbounded Knapsack View Solution
4 Coin Combinations II ★★★☆☆ Bounded Knapsack View Solution
5 Removing Digits ★★★☆☆ Digit DP View Solution
6 Grid Paths ★★★☆☆ 2D DP View Solution
7 Book Shop ★★★☆☆ Knapsack View Solution
8 Money Sums ★★★☆☆ Subset Sum View Solution
9 Removal Game ★★★☆☆ Game Theory + DP View Solution
10 Two Sets II ★★★☆☆ Subset Sum View Solution
11 Beautiful Permutation ★★★☆☆ Permutation DP View Solution
12 Go Go Turtle ★★★★☆ Advanced DP View Solution
13 Decreasing String ★★★★☆ String DP View Solution
14 School Excursion ★★★★☆ Knapsack with Groups View Solution
15 Counting Numbers ★★★★☆ Digit DP View Solution
16 Counting Numbers II ★★★★★ Advanced Digit DP View Solution
17 Counting Numbers III ★★★★★ Very Advanced Digit DP View Solution
18 Counting Numbers IV ★★★★★ Extremely Advanced Digit DP View Solution
19 Counting Numbers V ★★★★★ Ultimate Digit DP View Solution

🚀 Learning Objectives

After solving these problems, you should be comfortable with:

  • Basic DP concepts and memoization
  • State transitions and recurrence relations
  • 1D and 2D DP problems
  • Knapsack variations (bounded, unbounded, with groups)
  • Digit DP techniques
  • Game theory with DP
  • String DP problems
  • Advanced optimization techniques

💡 Key DP Concepts

1. State Definition

  • Clearly define what information you need to remember
  • Keep states as simple as possible
  • Avoid redundant information

2. Recurrence Relation

  • Express current state in terms of smaller subproblems
  • Ensure all base cases are covered
  • Verify the relation is correct

3. Memoization vs Tabulation

  • Memoization: Top-down approach, recursive with memory
  • Tabulation: Bottom-up approach, iterative
  • Choose based on problem constraints

4. Space Optimization

  • Often only need previous states
  • Use rolling arrays for 1D DP
  • Consider if we can solve in-place

🔗 Related Resources

📚 Problem Categories

Basic DP (★★☆☆☆)

  • Dice Combinations: Introduction to DP with simple recurrence
  • Minimizing Coins: Classic coin change problem
  • Coin Combinations I: Unbounded knapsack variant

Intermediate DP (★★★☆☆)

  • Coin Combinations II: Bounded knapsack with ordering
  • Removing Digits: Digit DP introduction
  • Grid Paths: 2D DP with constraints
  • Book Shop: Standard knapsack problem
  • Money Sums: Subset sum problem
  • Removal Game: Game theory + DP
  • Two Sets II: Subset sum with constraints

Advanced DP (★★★★☆)

  • Beautiful Permutation: Permutation counting with DP
  • Go Go Turtle: Complex state transitions
  • Decreasing String: String manipulation with DP
  • School Excursion: Knapsack with group constraints
  • Counting Numbers: Basic digit DP

Expert DP (★★★★★)

  • Counting Numbers II-V: Progressively harder digit DP problems
  • Advanced optimization techniques
  • Complex state management

💻 Implementation Tips

  1. Start Simple: Begin with basic recurrence, optimize later
  2. Test Cases: Always test with edge cases and small examples
  3. State Compression: Use bit manipulation when possible
  4. Memory Management: Be careful with large DP tables
  5. Modulo Operations: Handle large numbers properly

🏆 Practice Strategy

  1. Solve in Order: Start with basic problems, progress to advanced
  2. Understand Patterns: Look for similarities between problems
  3. Implement Multiple Solutions: Try both memoization and tabulation
  4. Optimize: Practice space and time optimizations
  5. Review: Revisit problems to reinforce concepts

Master Dynamic Programming to become a competitive programming champion! 🚀