Permutations
Given an array `nums` of distinct integers, return all the possible permutations.
Why Interviewers Ask This
Cisco evaluates this question to assess a candidate's mastery of backtracking algorithms and recursive thinking, which are critical for network pathfinding and state management. They specifically look for the ability to handle combinatorial complexity without missing edge cases or creating duplicate results, reflecting their need for engineers who can design robust, error-free protocols.
How to Answer This Question
1. Clarify constraints: Confirm if the array contains distinct integers and define the expected output format immediately. 2. Propose the solution: State that you will use Depth-First Search (DFS) with backtracking to explore all permutations systematically. 3. Walk through the logic: Explain the recursive function where you iterate through available numbers, swap the current number into place, recurse, and then backtrack by swapping it back to restore the state. 4. Analyze complexity: Explicitly calculate time complexity as O(N! * N) due to generating N! permutations of length N, and space complexity as O(N) for the recursion stack. 5. Dry run: Trace the algorithm manually with a small input like [1, 2] to demonstrate correctness before writing code.
Key Points to Cover
- Explicitly stating the use of backtracking to manage state restoration
- Correctly identifying the base case as path length equaling array length
- Demonstrating understanding of why swapping or marking is necessary to avoid duplicates
- Providing accurate time complexity analysis involving factorial growth
- Tracing the execution flow logically to prove correctness
Sample Answer
To solve the Permutations problem efficiently, I would implement a backtracking approach using recursion. First, I'd verify that the input array contains distinct integers, which simplifies the logic since we don't need to handle duplicates. My strategy involves defining a recursive helper function that builds permutations incrementally. Inside this function, I'll maintain a list of used elements to ensure each number appears exactly once per permutation. The base case occurs when the current path length equals the input size, at which point I add a copy of the path to my results list. For the recursive step, I iterate through the input array; if an element hasn't been used, I mark it as used, append it to the current path, and make a recursive call. Crucially, after returning from the recursion, I must remove the last added element and unmark it as used. This 'undo' operation is the core of backtracking, allowing the algorithm to explore alternative branches. For example, with input [1, 2], the first branch picks 1, then 2, resulting in [1, 2]. Backtracking removes 2, then the loop continues to pick 2, followed by 1, yielding [2, 1]. This ensures every unique ordering is generated. Regarding complexity, since there are N! possible permutations and each takes O(N) time to construct, the total time complexity is O(N! * N). The space complexity is O(N) for the recursion stack depth. This approach is optimal because any algorithm must visit every permutation at least once.
Common Mistakes to Avoid
- Failing to backtrack (restore state) after the recursive call, leading to incorrect or incomplete results
- Not handling the base case correctly, causing infinite recursion or premature termination
- Using extra data structures inefficiently instead of swapping in-place, increasing space complexity unnecessarily
- Overlooking the distinction between combinations and permutations regarding order sensitivity
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