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674. Longest Continuous Increasing Subsequence
Description
Given an unsorted array of integers nums
, return the length of the longest continuous increasing subsequence (i.e. subarray). The subsequence must be strictly increasing.
A continuous increasing subsequence is defined by two indices l
and r
(l < r
) such that it is [nums[l], nums[l + 1], ..., nums[r - 1], nums[r]]
and for each l <= i < r
, nums[i] < nums[i + 1]
.
Example 1:
Input: nums = [1,3,5,4,7] Output: 3 Explanation: The longest continuous increasing subsequence is [1,3,5] with length 3. Even though [1,3,5,7] is an increasing subsequence, it is not continuous as elements 5 and 7 are separated by element 4.
Example 2:
Input: nums = [2,2,2,2,2] Output: 1 Explanation: The longest continuous increasing subsequence is [2] with length 1. Note that it must be strictly increasing.
Constraints:
1 <= nums.length <= 104
-109 <= nums[i] <= 109
Solutions
Solution 1: One-pass Scan
We can traverse the array $nums$, using a variable $cnt$ to record the length of the current consecutive increasing sequence. Initially, $cnt = 1$.
Then, we start from index $i = 1$ and traverse the array $nums$ to the right. Each time we traverse, if $nums[i - 1] < nums[i]$, it means that the current element can be added to the consecutive increasing sequence, so we set $cnt = cnt + 1$, and then update the answer to $ans = \max(ans, cnt)$. Otherwise, it means that the current element cannot be added to the consecutive increasing sequence, so we set $cnt = 1$.
After the traversal ends, we return the answer $ans$.
The time complexity is $O(n)$, where $n$ is the length of the array $nums$. The space complexity is $O(1)$.
Solution 2: Two Pointers
We can also use two pointers $i$ and $j$ to find each consecutive increasing sequence, and find the length of the longest consecutive increasing sequence as the answer.
The time complexity is $O(n)$, where $n$ is the length of the array $nums$. The space complexity is $O(1)$.
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class Solution { public int findLengthOfLCIS(int[] nums) { int ans = 1; for (int i = 1, cnt = 1; i < nums.length; ++i) { if (nums[i - 1] < nums[i]) { ans = Math.max(ans, ++cnt); } else { cnt = 1; } } return ans; } }
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class Solution { public: int findLengthOfLCIS(vector<int>& nums) { int ans = 1; for (int i = 1, cnt = 1; i < nums.size(); ++i) { if (nums[i - 1] < nums[i]) { ans = max(ans, ++cnt); } else { cnt = 1; } } return ans; } };
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class Solution: def findLengthOfLCIS(self, nums: List[int]) -> int: ans = cnt = 1 for i, x in enumerate(nums[1:]): if nums[i] < x: cnt += 1 ans = max(ans, cnt) else: cnt = 1 return ans
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func findLengthOfLCIS(nums []int) int { ans, cnt := 1, 1 for i, x := range nums[1:] { if nums[i] < x { cnt++ ans = max(ans, cnt) } else { cnt = 1 } } return ans }
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function findLengthOfLCIS(nums: number[]): number { let [ans, cnt] = [1, 1]; for (let i = 1; i < nums.length; ++i) { if (nums[i - 1] < nums[i]) { ans = Math.max(ans, ++cnt); } else { cnt = 1; } } return ans; }
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class Solution { /** * @param Integer[] $nums * @return Integer */ function findLengthOfLCIS($nums) { $ans = 1; $cnt = 1; for ($i = 1; $i < count($nums); ++$i) { if ($nums[$i - 1] < $nums[$i]) { $ans = max($ans, ++$cnt); } else { $cnt = 1; } } return $ans; } }
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impl Solution { pub fn find_length_of_lcis(nums: Vec<i32>) -> i32 { let mut ans = 1; let mut cnt = 1; for i in 1..nums.len() { if nums[i - 1] < nums[i] { ans = ans.max(cnt + 1); cnt += 1; } else { cnt = 1; } } ans } }