Given an n x n array of integers matrix, return the minimum sum of any falling path through matrix.
A falling path starts at any element in the first row and chooses the element in the next row that is either directly below or diagonally left/right. Specifically, the next element from position (row, col) will be (row + 1, col - 1), (row + 1, col), or (row + 1, col + 1).
Input: matrix = [[2,1,3],[6,5,4],[7,8,9]]
Output: 13
Explanation: There are two falling paths with a minimum sum as shown.
Input: matrix = [[-19,57],[-40,-5]]
Output: -59
Explanation: The falling path with a minimum sum is shown.
Constraints:
n == matrix.length == matrix[i].length
1 <= n <= 100
-100 <= matrix[i][j] <= 100
Simple dp. The transformation equation is:
d p [ i ] [ j ] = min ( d p [ i + 1 ] [ j − 1 ] , d p [ i + 1 ] [ j ] , d p [ i + 1 ] [ j + 1 ] ) dp[i][j] = \min(dp[i+1][j-1], dp[i+1][j],dp[i+1][j+1]) dp[i][j]=min(dp[i+1][j−1],dp[i+1][j],dp[i+1][j+1])
Time complexity: o ( n 2 ) o(n^2) o(n2)
Space complexity: o ( n 2 ) o(n^2) o(n2), could be reduced to o ( n ) o(n) o(n)
class Solution:
def minFallingPathSum(self, matrix: List[List[int]]) -> int:
n = len(matrix)
dp = [[100] * n for _ in range(n)]
dp[-1] = matrix[-1].copy()
max_val = 1000001
for i in range(n - 2, -1, -1):
for j in range(n):
if j - 1 >= 0:
left_val = dp[i + 1][j - 1]
else:
left_val = max_val
if j + 1 < n:
right_val = dp[i + 1][j + 1]
else:
right_val = max_val
dp[i][j] = matrix[i][j] + min([dp[i + 1][j], left_val, right_val])
return min(dp[0])