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This approach involves iterating through the array while maintaining a variable to track the length of the current continuous increasing subsequence. As we encounter each element, we check if it is larger than the previous one to decide if we should continue the current subsequence or start a new one. We also maintain a global maximum to store the maximum length found during our iteration.
Time Complexity: O(n), where n is the number of elements in the array.
Space Complexity: O(1), as we use a constant amount of space.
1function findLengthOfLCIS(nums) {
2 if (nums.length === 0) return 0;
3 let maxLen = 1, currLen = 1;
4
5 for (let i = 1; i < nums.length; i++) {
6 if (nums[i] > nums[i - 1]) {
7 currLen++;
8 maxLen = Math.max(maxLen, currLen);
9 } else {
10 currLen = 1;
11 }
12 }
13
14 return maxLen;
15}
16
17const nums = [1, 3, 5, 4, 7];
18console.log(findLengthOfLCIS(nums));The JavaScript method follows the same logic of iterating through the array and using conditionals to determine and set the current and maximum sequence lengths.
The greedy approach leverages a single pass through the array to determine the maximum length of an increasing subsequence. This method is optimal because it immediately processes each element only once, updating the sequence lengths on the fly, and computing the maximum without revisiting any part of the array.
Time Complexity: O(n), where n is the number of elements in the array.
Space Complexity: O(1), as no additional space other than variables is utilized.
This Python code implements a greedy check to only keep adjusts on a basic condition, replicating rolling continuity steps that keep sync throughout iterations in a lazy kind of sequence.