Backtracking Pattern: LeetCode Guide & Problems
Explore all possibilities recursively, pruning invalid branches early.
Problems (14)
| Problem | Solution | Link | |
|---|---|---|---|
| Permutations
#Array
#Backtracking | Solution | ||
| Subsets
#Array
#Backtracking
#Bit Manipulation | Solution | ||
| Combination Sum
#Array
#Backtracking | Solution | ||
| Word Search
#Array
#Matrix
#Backtracking | Solution | ||
| Letter Combinations of a Phone Number
#Hash Table
#String
#Backtracking | Solution | ||
| Generate Parentheses
#String
#Dynamic Programming
#Backtracking | Solution | ||
| N-Queens
#Array
#Backtracking | Solution | ||
| Palindrome Partitioning
#String
#Dynamic Programming
#Backtracking | Solution | ||
| Combination Sum II
#Array
#Backtracking | Solution | ||
| Subsets II
#Array
#Backtracking
#Bit Manipulation | Solution | ||
| Next Permutation
#Array | Solution | ||
| Find all permutations of a string
#String | Solution | ||
| Find all permutations of a given string without duplicates
#String | Solution | ||
| Longest Valid Parentheses
#String | Solution |
Mastering the Backtracking Pattern for Coding Interviews
1. Mental Model & Visual Intuition
The Backtracking pattern is designed to eliminate redundant work and reduce search spaces. Rather than evaluating every possible state using a naive brute-force approach, this pattern relies on an invariant that allows you to safely discard subproblems that cannot lead to an optimal answer. When encountering an interview question, look for core triggers such as monotonicity, contiguous constraints, optimal substructure, or overlapping subproblems.
2. Algorithmic Invariants & Step-by-Step Execution
To implement this pattern flawlessly under interview conditions, structure your thought process into three discrete steps:
- State Initialization: Define your boundaries, pointers, or memoization tables clearly before entering loops.
- State Transition: Update tracking variables incrementally as new elements enter or leave your active evaluation window.
- Feasibility Check: When constraints are violated, systematically shrink or adjust your state until the valid invariant is restored.
3. Time and Space Complexity Trade-Offs
In top-tier technical interviews at Google, Meta, and Amazon, arriving at a working solution is only half the battle. You must explicitly justify why your approach achieves optimal Big-O bounds. For Backtracking, the target time complexity is typically Exponential — O(n!) or O(2ⁿ) depending on problem while maintaining an auxiliary space complexity of O(n) call stack depth. Always discuss whether an in-place modification or two-pass traversal can save memory.
4. Common Interview Edge Cases to Test
Before informing your interviewer that you have completed coding, trace your logic against these standard high-failure test cases:
- Empty inputs, single-element collections, or all-identical values.
- Inputs with negative numbers, duplicates, or extreme values that could cause integer overflow.
- Odd versus even length inputs when dealing with pointer convergence or tree midpoints.
Related Guide
Backtracking Algorithms — Complete Guide With LeetCode Problems [2026] →Backtracking is just DFS with pruning. Once you see that, permutations, combinations, subsets, and N…