Press "Enter" to skip to content

Huahua's Tech Road

花花酱 LeetCode 1441. Build an Array With Stack Operations

Given an array target and an integer n. In each iteration, you will read a number from  list = {1,2,3..., n}.

Build the target array using the following operations:

  • Push: Read a new element from the beginning list, and push it in the array.
  • Pop: delete the last element of the array.
  • If the target array is already built, stop reading more elements.

You are guaranteed that the target array is strictly increasing, only containing numbers between 1 to n inclusive.

Return the operations to build the target array.

You are guaranteed that the answer is unique.

Example 1:

Input: target = [1,3], n = 3
Output: ["Push","Push","Pop","Push"]
Explanation: 
Read number 1 and automatically push in the array -> [1]
Read number 2 and automatically push in the array then Pop it -> [1]
Read number 3 and automatically push in the array -> [1,3]

Example 2:

Input: target = [1,2,3], n = 3
Output: ["Push","Push","Push"]

Example 3:

Input: target = [1,2], n = 4
Output: ["Push","Push"]
Explanation: You only need to read the first 2 numbers and stop.

Example 4:

Input: target = [2,3,4], n = 4
Output: ["Push","Pop","Push","Push","Push"]

Constraints:

  • 1 <= target.length <= 100
  • 1 <= target[i] <= 100
  • 1 <= n <= 100
  • target is strictly increasing.

Solution: Simulation

For each number in target, keep discarding i if i != num by “Push” + “Pop”, until i == num. One more “Push”.

Time complexity: O(n)
Space complexity: O(n) or O(1) w/o output.

C++

花花酱 LeetCode 1443. Minimum Time to Collect All Apples in a Tree

Given an undirected tree consisting of n vertices numbered from 0 to n-1, which has some apples in their vertices. You spend 1 second to walk over one edge of the tree. Return the minimum time in seconds you have to spend in order to collect all apples in the tree starting at vertex 0 and coming back to this vertex.

The edges of the undirected tree are given in the array edges, where edges[i] = [fromi, toi] means that exists an edge connecting the vertices fromi and toi. Additionally, there is a boolean array hasApple, where hasApple[i] = true means that vertex i has an apple, otherwise, it does not have any apple.

Example 1:

Input: n = 7, edges = [[0,1],[0,2],[1,4],[1,5],[2,3],[2,6]], hasApple = [false,false,true,false,true,true,false]
Output: 8 
Explanation: The figure above represents the given tree where red vertices have an apple. One optimal path to collect all apples is shown by the green arrows.  

Example 2:

Input: n = 7, edges = [[0,1],[0,2],[1,4],[1,5],[2,3],[2,6]], hasApple = [false,false,true,false,false,true,false]
Output: 6
Explanation: The figure above represents the given tree where red vertices have an apple. One optimal path to collect all apples is shown by the green arrows.  

Example 3:

Input: n = 7, edges = [[0,1],[0,2],[1,4],[1,5],[2,3],[2,6]], hasApple = [false,false,false,false,false,false,false]
Output: 0

Constraints:

  • 1 <= n <= 10^5
  • edges.length == n-1
  • edges[i].length == 2
  • 0 <= fromi, toi <= n-1
  • fromi < toi
  • hasApple.length == n

Solution: DFS

Build the graph (tree) and DFS.

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

C++

if edge is not ordered

C++

玩转Linux命令行 – 事件处理 – EP3

Download Data

download.sh

V1

V2

V3

花花酱 LeetCode 1439. Find the Kth Smallest Sum of a Matrix With Sorted Rows

You are given an m * n matrix, mat, and an integer k, which has its rows sorted in non-decreasing order.

You are allowed to choose exactly 1 element from each row to form an array. Return the Kth smallest array sum among all possible arrays.

Example 1:

Input: mat = [[1,3,11],[2,4,6]], k = 5
Output: 7
Explanation: Choosing one element from each row, the first k smallest sum are:
[1,2], [1,4], [3,2], [3,4], [1,6]. Where the 5th sum is 7.  

Example 2:

Input: mat = [[1,3,11],[2,4,6]], k = 9
Output: 17

Example 3:

Input: mat = [[1,10,10],[1,4,5],[2,3,6]], k = 7
Output: 9
Explanation: Choosing one element from each row, the first k smallest sum are:
[1,1,2], [1,1,3], [1,4,2], [1,4,3], [1,1,6], [1,5,2], [1,5,3]. Where the 7th sum is 9.  

Example 4:

Input: mat = [[1,1,10],[2,2,9]], k = 7
Output: 12

Constraints:

  • m == mat.length
  • n == mat.length[i]
  • 1 <= m, n <= 40
  • 1 <= k <= min(200, n ^ m)
  • 1 <= mat[i][j] <= 5000
  • mat[i] is a non decreasing array.

Solution 1: Priority Queue

Generate the arrays in order.

Each node is {sum, idx_0, idx_1, …, idx_m},

Start with {sum_0, 0, 0, …, 0}.

For expansion, pick one row and increase its index

Time complexity: O(k * m ^ 2* log k)
Space complexity: O(k)

C++

花花酱 LeetCode 1438. Longest Continuous Subarray With Absolute Diff Less Than or Equal to Limit

Given an array of integers nums and an integer limit, return the size of the longest continuous subarray such that the absolute difference between any two elements is less than or equal to limit.

In case there is no subarray satisfying the given condition return 0.

Example 1:

Input: nums = [8,2,4,7], limit = 4
Output: 2 
Explanation: All subarrays are: 
[8] with maximum absolute diff |8-8| = 0 <= 4.
[8,2] with maximum absolute diff |8-2| = 6 > 4. 
[8,2,4] with maximum absolute diff |8-2| = 6 > 4.
[8,2,4,7] with maximum absolute diff |8-2| = 6 > 4.
[2] with maximum absolute diff |2-2| = 0 <= 4.
[2,4] with maximum absolute diff |2-4| = 2 <= 4.
[2,4,7] with maximum absolute diff |2-7| = 5 > 4.
[4] with maximum absolute diff |4-4| = 0 <= 4.
[4,7] with maximum absolute diff |4-7| = 3 <= 4.
[7] with maximum absolute diff |7-7| = 0 <= 4. 
Therefore, the size of the longest subarray is 2.

Example 2:

Input: nums = [10,1,2,4,7,2], limit = 5
Output: 4 
Explanation: The subarray [2,4,7,2] is the longest since the maximum absolute diff is |2-7| = 5 <= 5.

Example 3:

Input: nums = [4,2,2,2,4,4,2,2], limit = 0
Output: 3

Constraints:

  • 1 <= nums.length <= 10^5
  • 1 <= nums[i] <= 10^9
  • 0 <= limit <= 10^9

Solution 1: Sliding Window + TreeSet

Use a treeset to maintain a range of [l, r] such that max(nums[l~r]) – min(nums[l~r]) <= limit.
Every time, we add nums[r] into the tree, and move l towards r to keep the max diff under limit.

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

C++

Solution 2: Dual Monotonic Queue

Similar to https://zxi.mytechroad.com/blog/dynamic-programming/leetcode-1425-constrained-subset-sum/

We want to maintain a range [l, r] that max(nums[l~r]) – min(nums[l~r]) <= limit, to track the max/min of a range efficiently we could use monotonic queue. One for max and one for min.

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

C++