LeetCode

The Problem Given an m x n grid of characters board and a string word, return true if word exists in the grid. The word can be constructed from letters of sequentially adjacent cells, where adjacent cells are horizontally or vertically neighboring. The same letter cell may not be used more than once. Examples Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCCED" Output: true Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "SEE" Output: true ...

The Problem Given an array of distinct integers candidates and a target integer target, return a list of all unique combinations of candidates where the chosen numbers sum to target. You may return the combinations in any order. The same number may be chosen from candidates an unlimited number of times. Two combinations are unique if the frequency of at least one of the chosen numbers is different. The frequency of an element x is the number of times it occurs in the array. ...

The Problem The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, and the median is the mean of the two middle values. For example, for arr = [2,3,4], the median is 3. For example, for arr = [2,3], the median is (2 + 3) / 2 = 2.5. Implement the MedianFinder class: MedianFinder() initializes the MedianFinder object. void addNum(int num) adds the integer num from the data stream to the data structure. double findMedian() returns the median of all elements so far. Answers within 10⁻⁵ of the actual answer will be accepted. Examples Input ["MedianFinder", "addNum", "addNum", "findMedian", "addNum", "findMedian"] [[], [1], [2], [], [3], []] Output [null, null, null, 1.5, null, 2.0] Explanation MedianFinder medianFinder = new MedianFinder(); medianFinder.addNum(1); // arr = [1] medianFinder.addNum(2); // arr = [1, 2] medianFinder.findMedian(); // return 1.5 (i.e., (1 + 2) / 2) medianFinder.addNum(3); // arr = [1, 2, 3] medianFinder.findMedian(); // return 2.0 Constraints -10⁵ <= num <= 10⁵ There will be at least one element in the data structure before calling findMedian(). At most 5 * 10⁴ calls will be made to addNum and findMedian(). Follow-up: If all integer numbers from the stream are in the range [0, 100], how would you optimize your solution? If 99% of all integer numbers from the stream are in the range [0, 100], how would you optimize your solution? Sorting Solution class MedianFinder { private var data: [Int] init() { self.data = [] } func addNum(_ num: Int) { data.append(num) } func findMedian() -> Double { data.sort() let n = data.count if (n & 1) != 0 { return Double(data[n / 2]) } else { return (Double(data[n / 2]) + Double(data[n / 2 - 1])) / 2 } } } Explanation One approach to solving this problem is to apply a built-in sorting algorithm. To find the result, we use an array to collect all numbers added using the addNum method. ...

The Problem Serialization is the process of converting a data structure or object into a sequence of bits so that it can be stored in a file or memory buffer or transmitted across a network connection link to be reconstructed later in the same or another computer environment. Design an algorithm to serialize and deserialize a binary tree. There is no restriction on how your serialization/deserialization algorithm should work. You just need to ensure that a binary tree can be serialized to a string and this string can be deserialized to the original tree structure. ...

The Problem A path in a binary tree is a sequence of nodes where each pair of adjacent nodes in the sequence has an edge connecting them. A node can only appear in the sequence at most once. Note that the path does not need to pass through the root. The path sum of a path is the sum of the node’s values in the path. Given the root of a binary tree, return the maximum path sum of any non-empty path. ...