Is Find the Most Competitive Subsequence easy or hard?

Find the Most Competitive Subsequence is classified as a Medium problem. The challenge comes from recognizing the greedy rule and implementing the monotonic stack correctly. Once the pattern is identified, the algorithm becomes a straightforward linear scan.

Find the Most Competitive Subsequence Python/Java solution

Python and Java solutions typically implement the monotonic stack pattern. Iterate through nums, pop from the stack while the top element is larger than the current value and enough elements remain, then push the current value if the stack size is below k. This produces an O(n) solution in both languages.

How to solve Find the Most Competitive Subsequence in O(n)?

Scan the array once while maintaining a monotonic increasing stack. For each number, remove larger elements from the stack if enough remaining elements exist to still reach length k. Then push the current number if the stack has fewer than k elements. This greedy process ensures the smallest possible subsequence while keeping the complexity O(n).

What is the best approach for Find the Most Competitive Subsequence?

The best approach uses a greedy monotonic stack. Iterate through the array while maintaining an increasing stack of elements in the subsequence. If the current number is smaller than the stack top and enough elements remain to still build length k, pop the larger value. This guarantees the lexicographically smallest subsequence in O(n) time and O(k) space.

Is Find the Most Competitive Subsequence asked at Google/Amazon/Meta?

Variants of this problem appear in interviews at companies like Amazon, Google, and Meta because it tests greedy reasoning and monotonic stack patterns. Interviewers often expect candidates to identify that earlier larger elements should be removed when a smaller element appears and enough elements remain.

What data structure is used in Find the Most Competitive Subsequence?

A stack is the primary data structure used in the optimal solution. Specifically, a monotonic increasing stack helps maintain the smallest possible sequence while processing the array. The stack stores elements of the current subsequence and allows efficient removal of larger previous values.

What is the time complexity of Find the Most Competitive Subsequence?

The optimal solution runs in O(n) time where n is the length of the array. Each element is pushed to the stack once and popped at most once, so the total number of operations is linear. The space complexity is O(k) because the resulting subsequence contains exactly k elements.

Description Hints 1 Solution Video 34:18

1673. Find the Most Competitive Subsequence

Medium53.4% acceptance2 companies

Given an integer array nums and a positive integer k, return the most competitive subsequence of nums of size k.

An array's subsequence is a resulting sequence obtained by erasing some (possibly zero) elements from the array.

We define that a subsequence a is more competitive than a subsequence b (of the same length) if in the first position where a and b differ, subsequence a has a number less than the corresponding number in b. For example, [1,3,4] is more competitive than [1,3,5] because the first position they differ is at the final number, and 4 is less than 5.

Example 1:

Input: nums = [3,5,2,6], k = 2
Output: [2,6]
Explanation: Among the set of every possible subsequence: {[3,5], [3,2], [3,6], [5,2], [5,6], [2,6]}, [2,6] is the most competitive.

Example 2:

Input: nums = [2,4,3,3,5,4,9,6], k = 4
Output: [2,3,3,4]

Constraints:

1 <= nums.length <= 10⁵
0 <= nums[i] <= 10⁹
1 <= k <= nums.length

Asked at 2 companies

Google Uber

Topics

Hints open one at a time

Hint 1

In lexicographical order, the elements to the left have higher priority than those that come after. Can you think of a strategy that incrementally builds the answer from left to right?

34:18

Still stuck?Read 3 approaches, or watch a walkthrough.

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