Leetcode 120. How To Find The Minimum Path From Top To Bottom Of A Triangle
Two dynamic programming techniques to solve Leetcode 120. Triangle. One has space complexity O(n^2). The other is O(n).
Problem statement
You're provided with a triangle
array. Your goal is to find the smallest possible sum of a path from the top of the triangle to the bottom.
You can move to an adjacent number in the row below at each step. Specifically, if you're at index i
in the current row, you can move to either index i
or index i + 1
in the next row.
Example 1
Input: triangle = [[2],[3,4],[6,5,7],[4,1,8,3]]
Output: 11
Explanation: The triangle looks like:
2
3 4
6 5 7
4 1 8 3
The minimum path sum from top to bottom is 2 + 3 + 5 + 1 = 11 (underlined above).
Example 2
Input: triangle = [[-10]]
Output: -10
Constraints
1 <= triangle.length <= 200
.triangle[0].length == 1
.triangle[i].length == triangle[i - 1].length + 1
.-10^4 <= triangle[i][j] <= 10^4
.
Follow up
- Could you use only
O(n)
extra space, wheren
is the total number of rows in the triangle?
Solution 1: Store all minimum paths
You can store all minimum paths at every positions (i,j)
to compute the next ones with this relationship.
minPath[i][j] = triangle[i][j] + min(minPath[i - 1][j - 1], minPath[i - 1][j]);
Code
#include <iostream>
#include <vector>
#include <algorithm>
using namespace std;
int minimumTotal(const vector<vector<int>>& triangle) {
const int n = triangle.size(); // triangle's height
vector<vector<int>> minPath(n);
minPath[0] = triangle[0];
for (int i = 1; i < n; i++) {
const int N = triangle[i].size();
minPath[i].resize(N);
// left most number
minPath[i][0] = triangle[i][0] + minPath[i-1][0];
for (int j = 1; j < N - 1; j++) {
minPath[i][j] = triangle[i][j] + min(minPath[i-1][j-1], minPath[i-1][j]);
}
// right most number
minPath[i][N-1] = triangle[i][N-1] + minPath[i-1][N-2];
}
// pick the min path among the ones (begin -> end)
// go to the bottom (n-1)
return *min_element(minPath[n-1].begin(), minPath[n-1].end());
}
int main() {
vector<vector<int>> triangle{{2},{3,4},{6,5,7},{4,1,8,3}};
cout << minimumTotal(triangle) << endl;
triangle = {{-10}};
cout << minimumTotal(triangle) << endl;
}
Output:
11
-10
This solution finds the minimum path sum from the top to the bottom of a triangle, represented as a vector of vectors. It uses dynamic programming to calculate the minimum path sum.
The algorithm initializes a minPath
vector of vectors to store the minimum path sum for each element in the triangle
. It starts by setting the first row of minPath
to be the same as the first row of the triangle
.
Then, it iterates through the rows of the triangle
starting from the second row. For each element in the current row, the minimum path sum is calculated by considering the two paths from the previous row leading to that element. It takes the minimum of the two paths and adds the current element's value. This way, it accumulates the minimum path sum efficiently.
The algorithm continues this process until it reaches the last row of the triangle
. Finally, it returns the minimum element from the last row of minPath
, representing the minimum path sum from top to bottom.
Complexity
- Runtime:
O(n^2)
, wheren
is the number of rows in thetriangle
. - Extra space:
O(n^2)
.
Solution 2: Store only the minimum paths of each row
You do not need to store all paths for all rows. The computation of the next row only depends on its previous one.
Code
#include <iostream>
#include <vector>
#include <algorithm>
using namespace std;
int minimumTotal(const vector<vector<int>>& triangle) {
const int n = triangle.size();
// store only min path for each row
vector<int> minPath(n);
minPath[0] = triangle[0][0];
for (int i = 1; i < n; i++) {
// right most number
minPath[i] = triangle[i][i] + minPath[i - 1];
for (int j = i - 1; j > 0; j--) {
minPath[j] = triangle[i][j] + min(minPath[j - 1], minPath[j]);
}
// left most number
minPath[0] = triangle[i][0] + minPath[0];
}
return *min_element(minPath.begin(), minPath.end());
}
int main() {
vector<vector<int>> triangle{{2},{3,4},{6,5,7},{4,1,8,3}};
cout << minimumTotal(triangle) << endl;
triangle = {{-10}};
cout << minimumTotal(triangle) << endl;
}
Output:
11
-10
Complexity
- Runtime:
O(n^2)
, wheren
is the number of rows in the triangle. - Extra space:
O(n)
.