Coding Practice: Recursion and Backtracking

TL;DR

• Recursion is a shape-matching tool: when the data is recursive, the solution often is too.
• Backtracking matters when I need to explore many valid combinations with pruning.
• I keep state, base cases, and rollback behavior explicit.

Context

Recursive patterns are useful for nested comments, menu trees, folder explorers, rule builders, and schema-driven UIs. Even when I do not ship recursive code directly, practicing it sharpens my decomposition skills.

The Approach

Practice these 10 medium-level questions:

1. Flatten Nested Array — recursive traversal baseline.
2. Generate All Subsets — classic inclusion/exclusion tree.
3. Generate Unique Permutations — backtracking with duplicate control.
4. Combination Sum — reuse candidates while pruning branches.
5. Restore IP Addresses — constrained partitioning with validation.
6. Word Search in Grid — DFS with visited-state rollback.
7. Build File Paths From Tree — recursive path accumulation.
8. Redact Nested JSON Keys — recursive object transform.
9. Palindrome Partitioning — branch on valid prefix decisions.
10. N-Queens — board constraint tracking and backtracking discipline.

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Trade-offs

• Recursion is expressive, but deep input can hit stack limits.
• Backtracking can explode combinatorially, so pruning strategy matters as much as correctness.

References

• Related: Coding Practice: Trees
• Related: Coding Practice: JSON & Object Transformations