Two Sum
Given an array of integers `nums` and an integer `target`, return indices of the two numbers such that they add up to `target`.
Why Interviewers Ask This
Google asks the Two Sum problem to evaluate a candidate's ability to optimize time complexity beyond brute force. Interviewers specifically look for proficiency in using hash maps to trade space for speed, demonstrating logical thinking and the capacity to identify edge cases like duplicate elements or negative numbers efficiently.
How to Answer This Question
1. Clarify Requirements: Immediately confirm if input arrays are sorted, if duplicates exist, or if multiple solutions are possible. Google values precise communication before coding.
2. Discuss Brute Force: Briefly mention the O(n^2) nested loop approach to establish a baseline, then explain why it fails on large datasets typical at scale.
3. Propose Hash Map Strategy: Articulate the plan to use a hash map (dictionary) to store visited numbers and their indices. Explain that this allows constant-time lookups for the complement value.
4. Walk Through Logic: Describe iterating once through the array, calculating the target minus current number, checking the map, and updating the map if not found.
5. Analyze Complexity: Conclude by explicitly stating the Time Complexity is O(n) and Space Complexity is O(n), justifying why this is optimal for this specific constraint.
Key Points to Cover
- Demonstrating immediate recognition of the O(n) hash map optimization over O(n^2) brute force
- Clearly explaining the logic of storing complements versus current values during iteration
- Handling edge cases such as finding the solution on the very first or last element
- Explicitly articulating both Time and Space complexity analysis at the conclusion
- Communicating thought process clearly while writing code, reflecting Google's collaborative culture
Sample Answer
To solve the Two Sum problem efficiently, I first clarify constraints. Assuming an unsorted array with potential duplicates, the brute-force method of comparing every pair would take O(n^2) time, which is inefficient for Google-scale data. Instead, I propose a single-pass solution using a hash map.
My strategy involves initializing an empty dictionary to store numbers we have seen so far as keys and their indices as values. As I iterate through the array with index i, I calculate the complement needed to reach the target: complement = target - nums[i].
Before adding the current number to our map, I check if this complement already exists within it. If it does, I have found my pair immediately and can return the stored index of the complement alongside the current index i. This ensures we find the solution in one pass.
If the complement isn't found, I add the current number and its index to the map for future iterations. For example, with nums = [2, 7, 11, 15] and target = 9, when I encounter 7, I see that 2 is already in the map. I return [0, 1].
This approach reduces time complexity to O(n) since we traverse the list once, with O(1) average lookup time. The space complexity is O(n) to store the map entries. This balance of speed and memory usage aligns well with performance requirements for high-throughput systems.
Common Mistakes to Avoid
- Failing to handle the case where the same element cannot be used twice, leading to incorrect index returns
- Ignoring the requirement to return indices instead of the actual values, causing logic errors
- Implementing a two-pass solution that unnecessarily iterates the array twice instead of combining steps
- Not discussing the space-time tradeoff, missing an opportunity to demonstrate system design awareness
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