Recursion and Backtracking
Recursion & Backtracking in DSA
Master problem solving using recursive thinking and systematic exploration techniques.
📌 Topics Covered
🔁 Recursion
- A function calls itself to solve smaller subproblems.
- Breaks problems into identical smaller parts.
- Uses a base case to stop execution.
- Common examples: factorial, Fibonacci, tree traversal.
💡 Key Idea: Think of recursion as “divide → solve → combine”.
🔄 Backtracking
- Try all possibilities and undo (backtrack) when a path fails.
- Used when multiple solutions exist.
- Built on top of recursion.
- Examples: N-Queens, Sudoku, Maze problems.
💡 Key Idea: Choose → Explore → Undo (Backtrack).
📚 Important Questions & Topics
1. Understanding Recursion
- What is recursion?
- How recursion works internally (call stack)
- Factorial, Fibonacci
- Tail vs Non-tail recursion
2. Recursion in Data Structures
- Tree traversal
- DFS in graphs
3. Understanding Backtracking
- Difference from recursion
- Decision tree approach
4. Backtracking Problems
- N-Queens
- Sudoku Solver
- Rat in a Maze
- Subset Sum
- Hamiltonian Cycle
5. Optimization Techniques
- Memoization
- Pruning
- Dynamic Programming
6. Practice & Implementation
- Write recursive solutions
- Implement backtracking from scratch
- Analyze complexity