This approach uses dynamic programming to solve the problem in O(n^2) time complexity. We maintain a dp array where dp[i] represents the length of the longest increasing subsequence that ends with nums[i]. For each element, we iterate over all previous elements to see if they can be included in the subsequence ending at the current element, and update dp[i] accordingly.
Time Complexity: O(n^2) where n is the length of the input array. Space Complexity: O(n) for storing the dp array.
1using System;
2
3public class Solution {
4 public int LengthOfLIS(int[] nums) {
5 if (nums == null || nums.Length == 0) return 0;
6 int n = nums.Length;
7 int[] dp = new int[n];
8 Array.Fill(dp, 1);
9 int maxLen = 1;
10 for (int i = 1; i < n; i++) {
11 for (int j = 0; j < i; j++) {
12 if (nums[i] > nums[j]) {
13 dp[i] = Math.Max(dp[i], dp[j] + 1);
14 }
15 }
16 maxLen = Math.Max(maxLen, dp[i]);
17 }
18 return maxLen;
19 }
20 public static void Main() {
21 int[] nums = {10,9,2,5,3,7,101,18};
22 Solution sol = new Solution();
23 Console.WriteLine(sol.LengthOfLIS(nums));
24 }
25}The C# solution mimics other language solutions with dynamic programming, utilizing nested loops to progressively build up the dp array values.
In this approach, we use a combination of dynamic programming and binary search to solve the problem in O(n log n) time. This is often called the 'patience sorting' technique where we maintain a list, 'ends', to store the smallest ending value of any increasing subsequence with length i+1 in 'ends[i]'. We use binary search to find the position where each element in nums can be placed in 'ends'.
Time Complexity: O(n log n). Space Complexity: O(n).
1from bisect import bisect_left
2
3class Solution:
4 def lengthOfLIS(self, nums):
5 ends = []
6 for num in nums:
7 pos = bisect_left(ends, num)
8 if pos < len(ends):
9 ends[pos] = num
10 else:
11 ends.append(num)
12 return len(ends)
13
14# Example usage:
15nums = [10,9,2,5,3,7,101,18]
16sol = Solution()
17print(sol.lengthOfLIS(nums))Python solution makes use of the bisect module to apply binary search on the list 'ends'. This approach ensures finding the right position for each element in logarithmic time.