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Posts published in “Dynamic Programming”

花花酱 LeetCode 718. Maximum Length of Repeated Subarray

iven two integer arrays A and B, return the maximum length of an subarray that appears in both arrays.

Example 1:

Input:
A: [1,2,3,2,1]
B: [3,2,1,4,7]
Output: 3
Explanation: 
The repeated subarray with maximum length is [3, 2, 1].

Note:

  1. 1 <= len(A), len(B) <= 1000
  2. 0 <= A[i], B[i] < 100

Solution: DP

dp[i][j] := max length of (A[0:i], B[0:j])

dp[i][j] = dp[i – 1][j – 1] + 1 if A[i-1] == B[j-1] else 0

Time complexity: O(m*n)
Space complexity: O(m*n) -> O(n)

C++ S:O(mn)

C++ S:O(min(m,n))

花花酱 LeetCode 1043. Partition Array for Maximum Sum

Given an integer array A, you partition the array into (contiguous) subarrays of length at most K.  After partitioning, each subarray has their values changed to become the maximum value of that subarray.

Return the largest sum of the given array after partitioning.

Example 1:

Input: A = [1,15,7,9,2,5,10], K = 3
Output: 84
Explanation: A becomes [15,15,15,9,10,10,10]

Note:

  1. 1 <= K <= A.length <= 500
  2. 0 <= A[i] <= 10^6

Solution: DP

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

dp[i] := max sum of A[1] ~ A[i]
init: dp[0] = 0
transition: dp[i] = max{dp[i – k] + max(A[i-k:i]) * k}, 1 <= k <= min(i, K)
ans: dp[n]

A = | 2 | 1 | 4 | 3 |
K = 3
dp[0] = 0
dp[1] = max(dp[0] + 2 * 1) = 2
dp[2] = max(dp[0] + 2 * 2, dp[1] + 1 * 1) = max(4, 3) = 4
dp[3] = max(dp[0] + 4 * 3, dp[1] + 4 * 2, dp[2] + 4 * 1) = max(12, 10, 8) = 12
dp[4] = max(dp[1] + 4 * 3, dp[2] + 4 * 2, dp[3] + 3 * 1) = max(14, 12, 15) = 15
best = | 4 | 4 | 4 | 3 |

C++

花花酱 LeetCode 279. Perfect Squares

Given a positive integer n, find the least number of perfect square numbers (for example, 1, 4, 9, 16, ...) which sum to n.

Example 1:

Input: n = 12
Output: 3 
Explanation: 12 = 4 + 4 + 4.

Example 2:

Input: n = 13
Output: 2
Explanation: 13 = 4 + 9.

Solution 1: DP

dp[i] := ans
dp[0] = 0
dp[i] = min{dp[i – j * j] + 1} 1 <= j * j <= i

dp[5] = min{
dp[5 – 2 * 2] + 1 = dp[1] + 1 = (dp[1 – 1 * 1] + 1) + 1 = dp[0] + 1 + 1 = 2,
dp[5 – 1 * 1] + 1 = dp[3] + 1 = (dp[3 – 1 * 1] + 1) + 1 = dp[1] + 2 = dp[1 – 1*1] + 1 + 2 = dp[0] + 3 = 3
};

dp[5] = 2

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

C++

花花酱 LeetCode 1000. Minimum Cost to Merge Stones

There are N piles of stones arranged in a row.  The i-th pile has stones[i] stones.

move consists of merging exactly K consecutive piles into one pile, and the cost of this move is equal to the total number of stones in these K piles.

Find the minimum cost to merge all piles of stones into one pile.  If it is impossible, return -1.

Example 1:

Input: stones = [3,2,4,1], K = 2
Output: 20
Explanation: 
We start with [3, 2, 4, 1].
We merge [3, 2] for a cost of 5, and we are left with [5, 4, 1].
We merge [4, 1] for a cost of 5, and we are left with [5, 5].
We merge [5, 5] for a cost of 10, and we are left with [10].
The total cost was 20, and this is the minimum possible.

Example 2:

Input: stones = [3,2,4,1], K = 3
Output: -1
Explanation: After any merge operation, there are 2 piles left, and we can't merge anymore.  So the task is impossible.

Example 3:

Input: stones = [3,5,1,2,6], K = 3
Output: 25
Explanation: 
We start with [3, 5, 1, 2, 6].
We merge [5, 1, 2] for a cost of 8, and we are left with [3, 8, 6].
We merge [3, 8, 6] for a cost of 17, and we are left with [17].
The total cost was 25, and this is the minimum possible.

Note:

  • 1 <= stones.length <= 30
  • 2 <= K <= 30
  • 1 <= stones[i] <= 100

Solution: DP

dp[i][j][k] := min cost to merge subarray i ~ j into k piles
Init: dp[i][j][k] = 0 if i==j and k == 1 else inf
ans: dp[0][n-1][1]
transition:
1. dp[i][j][k] = min{dp[i][m][1] + dp[m+1][j][k-1]} for all i <= m < j
2. dp[i][j][1] = dp[i][j][K] + sum(A[i]~A[j])

Time complexity: O(n^3)
Space complexity: O(n^2*K)

C++

C++/top down

Solution 2: DP

dp[l][i] := min cost to merge [i, i + l) into as less piles as possible. Number of merges will be (l-1) / (K – 1) and
Transition: dp[l][i] = min(dp[m][i] + dp[l – m][i + m]) for 1 <= m < l
if ((l – 1) % (K – 1) == 0) [i, i + l) can be merged into 1 pile, dp[l][i] += sum(A[i:i+l])

Time complexity: O(n^3 / k)
Space complexity: O(n^2)

C++

C++/Top-Down

花花酱LeetCode 983. Minimum Cost For Tickets

In a country popular for train travel, you have planned some train travelling one year in advance.  The days of the year that you will travel is given as an array days.  Each day is an integer from 1 to 365.

Train tickets are sold in 3 different ways:

  • a 1-day pass is sold for costs[0] dollars;
  • a 7-day pass is sold for costs[1] dollars;
  • a 30-day pass is sold for costs[2] dollars.

The passes allow that many days of consecutive travel.  For example, if we get a 7-day pass on day 2, then we can travel for 7 days: day 2, 3, 4, 5, 6, 7, and 8.

Return the minimum number of dollars you need to travel every day in the given list of days.

Example 1:

Input: days = [1,4,6,7,8,20], costs = [2,7,15]
Output: 11
Explanation: 
For example, here is one way to buy passes that lets you travel your travel plan:
On day 1, you bought a 1-day pass for costs[0] = $2, which covered day 1.
On day 3, you bought a 7-day pass for costs[1] = $7, which covered days 3, 4, ..., 9.
On day 20, you bought a 1-day pass for costs[0] = $2, which covered day 20.
In total you spent $11 and covered all the days of your travel.

Example 2:

Input: days = [1,2,3,4,5,6,7,8,9,10,30,31], costs = [2,7,15]
Output: 17
Explanation: 
For example, here is one way to buy passes that lets you travel your travel plan:
On day 1, you bought a 30-day pass for costs[2] = $15 which covered days 1, 2, ..., 30.
On day 31, you bought a 1-day pass for costs[0] = $2 which covered day 31.
In total you spent $17 and covered all the days of your travel.

Note:

  1. 1 <= days.length <= 365
  2. 1 <= days[i] <= 365
  3. days is in strictly increasing order.
  4. costs.length == 3
  5. 1 <= costs[i] <= 1000

Solution: DP

dp[i] := min cost to cover the i-th day
dp[0] = 0
dp[i] = min(dp[i – 1] + costs[0], dp[i – 7] + costs[1], dp[i – 30] + costs[2])

C++

Python