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How to Identify Recursion Problems in Coding Interviews

Recursion is one of the most fundamental problem-solving techniques in programming. However, not every problem is a recursion problem. In interviews, many candidates struggle to decide whether recursion is the right tool. This blog will help you build intuition for when and why to use recursion.

🔑 Step 1: What is Recursion?

Recursion is when a function calls itself to solve a smaller instance of the same problem until it reaches a base case.

Example:

int factorial(int n) {
    if (n == 0) return 1; // base case
    return n * factorial(n - 1); // recursive step
}
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Key properties of recursion:

  • A base case to stop recursion.
  • A recursive case that reduces the problem size.

🔑 Step 2: Checklist – Is it a Recursion Problem?

Ask yourself:

  1. Can I break the problem into smaller versions of itself?
    Example: Factorial → n! = n * (n-1)!

  2. Does the solution naturally follow a divide-and-conquer approach?
    Example: Binary search, merge sort.

  3. Am I traversing hierarchical structures (trees, graphs)?
    Example: Tree traversals (inorder, preorder, postorder).

  4. Am I exploring multiple choices/paths?
    Example: Subsets, permutations, N-Queens → backtracking problems.

👉 If most of these are true, recursion is likely the right choice.

🔑 Step 3: Common Patterns of Recursion Problems

1. Divide and Conquer

  • Break the problem into two or more independent parts.
  • Examples: Merge Sort, Quick Sort, Binary Search.

2. Tree/Graph Traversal

  • Natural fit since trees/graphs are recursive data structures.
  • Examples: DFS, BFS (can be recursive with DFS).

3. Backtracking

  • Explore all possibilities and backtrack when a path is invalid.
  • Examples: N-Queens, Sudoku solver, Subset/Permutation generation.

4. Mathematical Recurrence Relations

  • Problems that directly map to recurrence.
  • Examples: Factorial, Fibonacci (though inefficient without DP).

🔑 Step 4: Signs a Problem Is Not Recursion

  • The problem is purely iterative (e.g., sum of numbers in an array).
  • No smaller self-similar subproblem exists.
  • Problem doesn’t require branching, hierarchical traversal, or divide-and-conquer.

📝 Quick Decision Points 

Problem → Can I define it in terms of smaller versions of itself?
          ↓ No → Not recursion
          ↓ Yes
    Does it need hierarchical traversal OR multiple choice exploration?
          ↓ Yes → Use recursion
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🎯 Final Words

Recursion shines in problems where self-similarity, hierarchical structures, or branching choices are present. If you can clearly define a base case and a recursive step, then recursion is often the cleanest and most elegant solution.

Start practicing by identifying whether a problem can be broken down into smaller subproblems. Once that intuition is built, spotting recursion problems in interviews becomes second nature.

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