You are given an integer array nums and a positive integer k.
Return the number of subarrays where the maximum element of nums appears at least k times in that subarray.
A subarray is a contiguous sequence of elements within an array.
Example 1:
Input: nums = [1,3,2,3,3], k = 2 Output: 6 Explanation: The subarrays that contain the element 3 at least 2 times are: [1,3,2,3], [1,3,2,3,3], [3,2,3], [3,2,3,3], [2,3,3] and [3,3].
Example 2:
Input: nums = [1,4,2,1], k = 3 Output: 0 Explanation: No subarray contains the element 4 at least 3 times.
Constraints:
1 <= nums.length <= 1051 <= nums[i] <= 1061 <= k <= 105This approach utilizes the sliding window technique to efficiently count subarrays that satisfy the given condition. By maintaining a window and a count of the occurrences of the maximum element within that window, we can determine the valid subarrays as we expand and contract the window.
The code initializes a sliding window by setting pointers i and j. As we iterate through the array, we update the current maximum element and its count within the window. When the count of the maximum element reaches k or more, we add subarrays from the start of the window to the end of the array that satisfy the condition.
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Time Complexity: O(n), where n is the number of elements in nums.
Space Complexity: O(1), only constant space is used for variables.
This strategy involves iterating through the array with two pointers, resetting conditions when new maximum elements are encountered and calculating valid subarrays based on maximum occurrence counts.
The C code uses two pointers (left and right) to validate subarrays. When a maximum condition changes, the calculation restarts, which ensures only subarrays with the required maximum occurrence are considered.
C++
Java
Python
C#
JavaScript
Time Complexity: O(n), as we process each element in nums.
Space Complexity: O(1), no extra space besides pointer variables.
| Approach | Complexity |
|---|---|
| Sliding Window Technique | Time Complexity: O(n), where n is the number of elements in nums. |
| Two-Pointer Approach with Reset | Time Complexity: O(n), as we process each element in nums. |
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