Longest Continuous Increasing Subsequence

Intuition

Because the run must be contiguous, you never need to look back further than the immediately previous element. As long as each new value is bigger than its predecessor, the current window simply grows by one. The first time that ordering fails, the window can no longer include the earlier elements, so it restarts at the current position. Keep a running maximum of the window length as you go.

Algorithm walkthrough

1. 1Handle the empty array by returning 0, then set both the best length and the current length to 1.
2. 2Walk from the second element to the end of the array.
3. 3If the current element is strictly greater than the previous one, extend the current length and refresh the best length.
4. 4Otherwise the streak is broken, so reset the current length back to 1.
5. 5After the sweep, the best length holds the answer.

Implementation

class Solution:
    def findLengthOfLCIS(self, nums: List[int]) -> int:
        if not nums:
            return 0
        best = 1
        current = 1
        for i in range(1, len(nums)):
            if nums[i] > nums[i - 1]:
                current += 1
                best = max(best, current)
            else:
                current = 1
        return best