What is the time complexity of Subarray With Elements Greater Than Varying Threshold?

The optimal solution for Subarray With Elements Greater Than Varying Threshold runs in O(n) time and uses O(1) space. This complexity is achieved because we traverse the array with a single pass using two pointers, making it linear in terms of the number of elements.

What is the brute force solution for Subarray With Elements Greater Than Varying Threshold?

The brute force approach for Subarray With Elements Greater Than Varying Threshold is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach checks every possible subarray of length k to see if all its elements are greater than the threshold divided by k. This is straightforward but inefficient for large arrays. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Subarray With Elements Greater Than Varying Threshold?

Subarray With Elements Greater Than Varying Threshold uses the following concepts: Array, Stack, Union-Find, Monotonic Stack. The recommended patterns to study are: Sliding Window, Two Pointers.

What are the interview tips for Subarray With Elements Greater Than Varying Threshold?

Always clarify the constraints and edge cases before diving into the solution. Think about how you can optimize a brute force solution and explain your thought process. Practice identifying patterns in problems, such as the sliding window technique.

What are common mistakes when solving Subarray With Elements Greater Than Varying Threshold?

Not checking if the subarray length matches k after adjusting the window. Failing to reset the left pointer correctly when encountering an invalid element.

#2334

Subarray With Elements Greater Than Varying Threshold

Hard

ArrayStackUnion-FindMonotonic StackSliding WindowTwo Pointers

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Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

This approach uses a sliding window technique to efficiently find the longest subarray where all elements are greater than the threshold divided by the length of the subarray. It reduces unnecessary checks by maintaining a window and adjusting its size dynamically.

⚙️

Algorithm

4 steps

1Step 1: Calculate the required value as threshold / k.
2Step 2: Initialize two pointers (left and right) to represent the current window of elements.
3Step 3: Expand the right pointer to include elements in the window until the window size exceeds k or an element is found that is not greater than the required value.
4Step 4: If the window size is exactly k and all elements are valid, return k. If not, move the left pointer to shrink the window and continue checking.

solution.py9 lines

1def findSubarray(nums, threshold, k):
2    required_value = threshold / k
3    left = 0
4    for right in range(len(nums)):
5        if nums[right] <= required_value:
6            left = right + 1
7        if right - left + 1 == k:
8            return k
9    return -1

ℹ

Complexity note: This complexity is achieved because we traverse the array with a single pass using two pointers, making it linear in terms of the number of elements.

1The minimum element in the subarray must be greater than threshold / k for the entire subarray to be valid.
2Using a sliding window approach allows us to efficiently find valid subarrays without redundant checks.

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