# Posts published in “Uncategorized”

There is an n x n grid, with the top-left cell at (0, 0) and the bottom-right cell at (n - 1, n - 1). You are given the integer n and an integer array startPos where startPos = [startrow, startcol] indicates that a robot is initially at cell (startrow, startcol).

You are also given a 0-indexed string s of length m where s[i] is the ith instruction for the robot: 'L' (move left), 'R' (move right), 'U' (move up), and 'D' (move down).

The robot can begin executing from any ith instruction in s. It executes the instructions one by one towards the end of s but it stops if either of these conditions is met:

• The next instruction will move the robot off the grid.
• There are no more instructions left to execute.

Return an array answer of length m where answer[i] is the number of instructions the robot can execute if the robot begins executing from the ith instruction in s.

Example 1:

Input: n = 3, startPos = [0,1], s = "RRDDLU"
Output: [1,5,4,3,1,0]
Explanation: Starting from startPos and beginning execution from the ith instruction:
- 0th: "RRDDLU". Only one instruction "R" can be executed before it moves off the grid.
- 1st:  "RDDLU". All five instructions can be executed while it stays in the grid and ends at (1, 1).
- 2nd:   "DDLU". All four instructions can be executed while it stays in the grid and ends at (1, 0).
- 3rd:    "DLU". All three instructions can be executed while it stays in the grid and ends at (0, 0).
- 4th:     "LU". Only one instruction "L" can be executed before it moves off the grid.
- 5th:      "U". If moving up, it would move off the grid.


Example 2:

Input: n = 2, startPos = [1,1], s = "LURD"
Output: [4,1,0,0]
Explanation:
- 0th: "LURD".
- 1st:  "URD".
- 2nd:   "RD".
- 3rd:    "D".


Example 3:

Input: n = 1, startPos = [0,0], s = "LRUD"
Output: [0,0,0,0]
Explanation: No matter which instruction the robot begins execution from, it would move off the grid.


Constraints:

• m == s.length
• 1 <= n, m <= 500
• startPos.length == 2
• 0 <= startrow, startcol < n
• s consists of 'L''R''U', and 'D'.

## Solution: Simulation

Time complexity: O(m2)
Space complexity: O(1)

## C++

You are given two strings word1 and word2. Merge the strings by adding letters in alternating order, starting with word1. If a string is longer than the other, append the additional letters onto the end of the merged string.

Return the merged string.

Example 1:

Input: word1 = "abc", word2 = "pqr"
Output: "apbqcr"
Explanation: The merged string will be merged as so:
word1:  a   b   c
word2:    p   q   r
merged: a p b q c r


Example 2:

Input: word1 = "ab", word2 = "pqrs"
Output: "apbqrs"
Explanation: Notice that as word2 is longer, "rs" is appended to the end.
word1:  a   b
word2:    p   q   r   s
merged: a p b q   r   s


Example 3:

Input: word1 = "abcd", word2 = "pq"
Output: "apbqcd"
Explanation: Notice that as word1 is longer, "cd" is appended to the end.
word1:  a   b   c   d
word2:    p   q
merged: a p b q c   d


Constraints:

• 1 <= word1.length, word2.length <= 100
• word1 and word2 consist of lowercase English letters.

## Solution: Find the shorter one

Time complexity: O(m+n)
Space complexity: O(1)

## C++

You are given two integer arrays, source and target, both of length n. You are also given an array allowedSwaps where each allowedSwaps[i] = [ai, bi] indicates that you are allowed to swap the elements at index ai and index bi (0-indexed) of array source. Note that you can swap elements at a specific pair of indices multiple times and in any order.

The Hamming distance of two arrays of the same length, source and target, is the number of positions where the elements are different. Formally, it is the number of indices i for 0 <= i <= n-1 where source[i] != target[i] (0-indexed).

Return the minimum Hamming distance of source and target after performing any amount of swap operations on array source.

Example 1:

Input: source = [1,2,3,4], target = [2,1,4,5], allowedSwaps = [[0,1],[2,3]]
Output: 1
Explanation: source can be transformed the following way:
- Swap indices 0 and 1: source = [2,1,3,4]
- Swap indices 2 and 3: source = [2,1,4,3]
The Hamming distance of source and target is 1 as they differ in 1 position: index 3.


Example 2:

Input: source = [1,2,3,4], target = [1,3,2,4], allowedSwaps = []
Output: 2
Explanation: There are no allowed swaps.
The Hamming distance of source and target is 2 as they differ in 2 positions: index 1 and index 2.


Example 3:

Input: source = [5,1,2,4,3], target = [1,5,4,2,3], allowedSwaps = [[0,4],[4,2],[1,3],[1,4]]
Output: 0


Constraints:

• n == source.length == target.length
• 1 <= n <= 105
• 1 <= source[i], target[i] <= 105
• 0 <= allowedSwaps.length <= 105
• allowedSwaps[i].length == 2
• 0 <= ai, bi <= n - 1
• ai != bi

## Solution: Union Find

Think each pair as an edge in a graph. Since we can swap as many time as we want, which means we can arrange the elements whose indices are in a connected component (CC) in any order.

For each index i, we increase the counter of CC(i) for key source[i] and decrease the counter of the same CC for key target[i]. If two keys are the same (can from different indices), one from source and one from target, it will cancel out, no distance. Otherwise, the counter will be off by two. Finally we sum up the counter for all the keys and divide it by two to get the hamming distance.

Time complexity: O(V+E)
Space complexity: O(V)

## C++

Given an integer array nums and a positive integer k, return the most competitive subsequence of nums of size k.

An array’s subsequence is a resulting sequence obtained by erasing some (possibly zero) elements from the array.

We define that a subsequence a is more competitive than a subsequence b (of the same length) if in the first position where a and b differ, subsequence a has a number less than the corresponding number in b. For example, [1,3,4] is more competitive than [1,3,5] because the first position they differ is at the final number, and 4 is less than 5.

Example 1:

Input: nums = [3,5,2,6], k = 2
Output: [2,6]
Explanation: Among the set of every possible subsequence: {[3,5], [3,2], [3,6], [5,2], [5,6], [2,6]}, [2,6] is the most competitive.


Example 2:

Input: nums = [2,4,3,3,5,4,9,6], k = 4
Output: [2,3,3,4]


Constraints:

• 1 <= nums.length <= 105
• 0 <= nums[i] <= 109
• 1 <= k <= nums.length

## Solution: Stack

Use a stack to track the best solution so far, pop if the current number is less than the top of the stack and there are sufficient numbers left. Then push the current number to the stack if not full.

Time complexity: O(n)
Space complexity: O(k)

## Python3

A binary tree is named Even-Odd if it meets the following conditions:

• The root of the binary tree is at level index 0, its children are at level index 1, their children are at level index 2, etc.
• For every even-indexed level, all nodes at the level have odd integer values in strictly increasing order (from left to right).
• For every odd-indexed level, all nodes at the level have even integer values in strictly decreasing order (from left to right).

Given the root of a binary tree, return true if the binary tree is Even-Odd, otherwise return false.

Example 1:

Input: root = [1,10,4,3,null,7,9,12,8,6,null,null,2]
Output: true
Explanation: The node values on each level are:
Level 0: [1]
Level 1: [10,4]
Level 2: [3,7,9]
Level 3: [12,8,6,2]
Since levels 0 and 2 are all odd and increasing, and levels 1 and 3 are all even and decreasing, the tree is Even-Odd.


Example 2:

Input: root = [5,4,2,3,3,7]
Output: false
Explanation: The node values on each level are:
Level 0: [5]
Level 1: [4,2]
Level 2: [3,3,7]
Node values in the level 2 must be in strictly increasing order, so the tree is not Even-Odd.


Example 3:

Input: root = [5,9,1,3,5,7]
Output: false
Explanation: Node values in the level 1 should be even integers.


Example 4:

Input: root = [1]
Output: true


Example 5:

Input: root = [11,8,6,1,3,9,11,30,20,18,16,12,10,4,2,17]
Output: true


Constraints:

• The number of nodes in the tree is in the range [1, 105].
• 1 <= Node.val <= 106

## Solution 1: DFS

Time complexity: O(n)
Space complexity: O(n)

## Solution 2: BFS

Time complexity: O(n)
Space complexity: O(n)