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Posts published in “Dynamic Programming”

花花酱 LeetCode 1463. Cherry Pickup II

Given a rows x cols matrix grid representing a field of cherries. Each cell in grid represents the number of cherries that you can collect.

You have two robots that can collect cherries for you, Robot #1 is located at the top-left corner (0,0) , and Robot #2 is located at the top-right corner (0, cols-1) of the grid.

Return the maximum number of cherries collection using both robots  by following the rules below:

  • From a cell (i,j), robots can move to cell (i+1, j-1) , (i+1, j) or (i+1, j+1).
  • When any robot is passing through a cell, It picks it up all cherries, and the cell becomes an empty cell (0).
  • When both robots stay on the same cell, only one of them takes the cherries.
  • Both robots cannot move outside of the grid at any moment.
  • Both robots should reach the bottom row in the grid.

Example 1:

Input: grid = [[3,1,1],[2,5,1],[1,5,5],[2,1,1]]
Output: 24
Explanation: Path of robot #1 and #2 are described in color green and blue respectively.
Cherries taken by Robot #1, (3 + 2 + 5 + 2) = 12.
Cherries taken by Robot #2, (1 + 5 + 5 + 1) = 12.
Total of cherries: 12 + 12 = 24.

Example 2:

Input: grid = [[1,0,0,0,0,0,1],[2,0,0,0,0,3,0],[2,0,9,0,0,0,0],[0,3,0,5,4,0,0],[1,0,2,3,0,0,6]]
Output: 28
Explanation: Path of robot #1 and #2 are described in color green and blue respectively.
Cherries taken by Robot #1, (1 + 9 + 5 + 2) = 17.
Cherries taken by Robot #2, (1 + 3 + 4 + 3) = 11.
Total of cherries: 17 + 11 = 28.

Example 3:

Input: grid = [[1,0,0,3],[0,0,0,3],[0,0,3,3],[9,0,3,3]]
Output: 22

Example 4:

Input: grid = [[1,1],[1,1]]
Output: 4

Constraints:

  • rows == grid.length
  • cols == grid[i].length
  • 2 <= rows, cols <= 70
  • 0 <= grid[i][j] <= 100 

Solution: DP

dp[y][x1][x2] := max cherry when ro1 at (x1, y) and ro2 at (x2, y)
dp[y][x1][x2] = max(dp[y+1][x1 + dx1][x2 + dx2]) -1 <= dx1, dx2 <= 1

Time complexity: O(c^2*r)
Space complexity: O(c^2*r)

C++

Bottom-up

C++

O(c^2) Space

C++

花花酱 LeetCode 1458. Max Dot Product of Two Subsequences

Given two arrays nums1 and nums2.

Return the maximum dot product between non-empty subsequences of nums1 and nums2 with the same length.

A subsequence of a array is a new array which is formed from the original array by deleting some (can be none) of the characters without disturbing the relative positions of the remaining characters. (ie, [2,3,5] is a subsequence of [1,2,3,4,5] while [1,5,3] is not).

Example 1:

Input: nums1 = [2,1,-2,5], nums2 = [3,0,-6]
Output: 18
Explanation: Take subsequence [2,-2] from nums1 and subsequence [3,-6] from nums2.
Their dot product is (2*3 + (-2)*(-6)) = 18.

Example 2:

Input: nums1 = [3,-2], nums2 = [2,-6,7]
Output: 21
Explanation: Take subsequence [3] from nums1 and subsequence [7] from nums2.
Their dot product is (3*7) = 21.

Example 3:

Input: nums1 = [-1,-1], nums2 = [1,1]
Output: -1
Explanation: Take subsequence [-1] from nums1 and subsequence [1] from nums2.
Their dot product is -1.

Constraints:

  • 1 <= nums1.length, nums2.length <= 500
  • -1000 <= nums1[i], nums2[i] <= 1000

Solution: DP

dp[i][j] := max product of nums1[0~i], nums2[0~j].

dp[i][j] = max(dp[i-1][j], dp[i][j -1], max(0, dp[i-1][j-1]) + nums1[i]*nums2[j])

Time complexity: O(n1*n2)
Space complexity: O(n1*n2)

C++

C++

花花酱 LeetCode 1449. Form Largest Integer With Digits That Add up to Target

Given an array of integers cost and an integer target. Return the maximum integer you can paint under the following rules:

  • The cost of painting a digit (i+1) is given by cost[i] (0 indexed).
  • The total cost used must be equal to target.
  • Integer does not have digits 0.

Since the answer may be too large, return it as string.

If there is no way to paint any integer given the condition, return “0”.

Example 1:

Input: cost = [4,3,2,5,6,7,2,5,5], target = 9
Output: "7772"
Explanation:  The cost to paint the digit '7' is 2, and the digit '2' is 3. Then cost("7772") = 2*3+ 3*1 = 9. You could also paint "997", but "7772" is the largest number.
Digit    cost
  1  ->   4
  2  ->   3
  3  ->   2
  4  ->   5
  5  ->   6
  6  ->   7
  7  ->   2
  8  ->   5
  9  ->   5

Example 2:

Input: cost = [7,6,5,5,5,6,8,7,8], target = 12
Output: "85"
Explanation: The cost to paint the digit '8' is 7, and the digit '5' is 5. Then cost("85") = 7 + 5 = 12.

Example 3:

Input: cost = [2,4,6,2,4,6,4,4,4], target = 5
Output: "0"
Explanation: It's not possible to paint any integer with total cost equal to target.

Example 4:

Input: cost = [6,10,15,40,40,40,40,40,40], target = 47
Output: "32211"

Constraints:

  • cost.length == 9
  • 1 <= cost[i] <= 5000
  • 1 <= target <= 5000

Solution: DP

dp(target) := largest number to print with cost == target.
dp(target) = max(dp(target – d) + cost[d])

Time complexity: O(target^2)
Space complexity: O(target^2)

C++ / Top Down

C++ / Bottom Up

To avoid string copying, we can store digit added (in order to back track the parent) and length of the optimal string.

Time complexity: O(target)
Space complexity: O(target)

C++ / O(target)

C++ / O(target)

花花酱 LeetCode 1444. Number of Ways of Cutting a Pizza

Given a rectangular pizza represented as a rows x cols matrix containing the following characters: 'A' (an apple) and '.' (empty cell) and given the integer k. You have to cut the pizza into k pieces using k-1 cuts. 

For each cut you choose the direction: vertical or horizontal, then you choose a cut position at the cell boundary and cut the pizza into two pieces. If you cut the pizza vertically, give the left part of the pizza to a person. If you cut the pizza horizontally, give the upper part of the pizza to a person. Give the last piece of pizza to the last person.

Return the number of ways of cutting the pizza such that each piece contains at least one apple. Since the answer can be a huge number, return this modulo 10^9 + 7.

Example 1:

Input: pizza = ["A..","AAA","..."], k = 3
Output: 3 
Explanation: The figure above shows the three ways to cut the pizza. Note that pieces must contain at least one apple.

Example 2:

Input: pizza = ["A..","AA.","..."], k = 3
Output: 1

Example 3:

Input: pizza = ["A..","A..","..."], k = 1
Output: 1

Constraints:

  • 1 <= rows, cols <= 50
  • rows == pizza.length
  • cols == pizza[i].length
  • 1 <= k <= 10
  • pizza consists of characters 'A' and '.' only.

Solution: DP

dp(n, m, k) := # of ways to cut pizza[n:N][m:M] with k cuts.

dp(n, m, k) = sum(hasApples(n, m, N – 1, y) * dp(y + 1, n, k – 1) for y in range(n, M)) + sum(hasApples(n, m, x, M – 1) * dp(m, x + 1, k – 1) for x in range(n, M))

Time complexity: O(M*N*(M+N)*K) = O(N^3 * K)
Space complexity: O(M*N*K)

C++

花花酱 LeetCode 1434. Number of Ways to Wear Different Hats to Each Other

There are n people and 40 types of hats labeled from 1 to 40.

Given a list of list of integers hats, where hats[i] is a list of all hats preferred by the i-th person.

Return the number of ways that the n people wear different hats to each other.

Since the answer may be too large, return it modulo 10^9 + 7.

Example 1:

Input: hats = [[3,4],[4,5],[5]]
Output: 1
Explanation: There is only one way to choose hats given the conditions. 
First person choose hat 3, Second person choose hat 4 and last one hat 5.

Example 2:

Input: hats = [[3,5,1],[3,5]]
Output: 4
Explanation: There are 4 ways to choose hats
(3,5), (5,3), (1,3) and (1,5)

Example 3:

Input: hats = [[1,2,3,4],[1,2,3,4],[1,2,3,4],[1,2,3,4]]
Output: 24
Explanation: Each person can choose hats labeled from 1 to 4.
Number of Permutations of (1,2,3,4) = 24.

Example 4:

Input: hats = [[1,2,3],[2,3,5,6],[1,3,7,9],[1,8,9],[2,5,7]]
Output: 111

Constraints:

  • n == hats.length
  • 1 <= n <= 10
  • 1 <= hats[i].length <= 40
  • 1 <= hats[i][j] <= 40
  • hats[i] contains a list of unique integers.

Solution: DP

dp[i][j] := # of ways using first i hats, j is the bit mask of people wearing hats.

e.g. dp[3][101] == # of ways using first 3 hats that people 1 and 3 are wearing hats.

init dp[0][0] = 1

dp[i][mask | (1 << p)] = dp[i-1][mask | (1 << p)] + dp[i-1][mask], where people p prefers hats i.

ans: dp[nHat][1…1]

Time complexity: O(2^n * h * n)
Space complexity: O(2^n * h) -> O(2^n)

C++

O(2^n) memory

C++