The problem Given an integer array nums, find the subarray with the largest sum, and return its sum. A subarray is a contiguous non-empty sequence of elements within an array. Examples Input: nums = [-2,1,-3,4,-1,2,1,-5,4] Output: 6 Explanation: The subarray [4,-1,2,1] has the largest sum 6. Input: nums = [1] Output: 1 Explanation: The subarray [1] has the largest sum 1. Input: nums = [5,4,-1,7,8] Output: 23 Explanation: The subarray [5,4,-1,7,8] has the largest sum 23. Constraints 1 <= nums.length <= 10⁵ -10⁴ <= nums[i] <= 10⁴ Follow up: If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle. ...

The problem Given two strings text1 and text2, return the length of their longest common subsequence. If there is no common subsequence, return 0. A subsequence of a string is a new string generated from the original string with some characters (can be none) deleted without changing the relative order of the remaining characters. For example, ”ace” is a subsequence of ”abcde”. A common subsequence of two strings is a subsequence that is common to both strings. ...

The problem There is a robot on an m x n grid. The robot is initially located at the top-left corner (i.e., grid[0][0]). The robot tries to move to the bottom-right corner (i.e., grid[m - 1][n - 1]). The robot can only move either down or right at any point in time. Given the two integers m and n, return the number of possible unique paths that the robot can take to reach the bottom-right corner. ...

The problem Given an integer array nums, return the length of the longest strictly increasing subsequence. A subsequence is an array that can be derived from another array by deleting some or no elements without changing the order of the remaining elements. Examples Input: nums = [10,9,2,5,3,7,101,18] Output: 4 Explanation: The longest increasing subsequence is [2,3,7,101], therefore the length is 4. Input: nums = [0,1,0,3,2,3] Output: 4 Input: nums = [7,7,7,7,7,7,7] Output: 1 Constraints 1 <= nums.length <= 2500 -10⁴ <= nums[i] <= 10⁴ Follow-up: Can you come up with an algorithm that runs in O(n log(n)) time complexity? ...

The problem Given a string s and a dictionary of strings wordDict, return true if s can be segmented into a space-separated sequence of one or more dictionary words. Note that the same word in the dictionary may be reused multiple times in the segmentation. Examples Input: s = "leetcode", wordDict = ["leet","code"] Output: true Explanation: Return true because "leetcode" can be segmented as "leet code". Input: s = "applepenapple", wordDict = ["apple","pen"] Output: true Explanation: Return true because "applepenapple" can be segmented as "apple pen apple". Note that you are allowed to reuse a dictionary word. Input: s = "catsandog", wordDict = ["cats","dog","sand","and","cat"] Output: false Constraints 1 <= s.length <= 300 1 <= wordDict.length <= 1000 1 <= wordDict[i].length <= 20 s and wordDict[i] consist of only lowercase English letters. All the strings of wordDict are unique. Recursion solution func wordBreak(_ s: String, _ wordDict: [String]) -> Bool { let sCount = s.count let sArray = Array(s) func dfs(_ i: Int) -> Bool { if i == sCount { return true } for w in wordDict { let wCount = w.count if ((i + wCount) <= sCount && String(sArray[i ..< i + wCount]) == w ) { if dfs(i + wCount) { return true } } } return false } return dfs(0) } Explanation There are multiple ways to solve this problem. For example, we can check every character in input s and compare it with characters in wordDict, but this approach is inefficient from a time complexity perspective. Instead: ...