296. Best Meeting Point (Hard)
A group of two or more people wants to meet and minimize the total travel distance. You are given a 2D grid of values 0 or 1, where each 1 marks the home of someone in the group. The distance is calculated using Manhattan Distance, where distance(p1, p2) = |p2.x - p1.x| + |p2.y - p1.y|
.
For example, given three people living at (0,0)
, (0,4)
, and (2,2)
:
1 - 0 - 0 - 0 - 1
| | | | |
0 - 0 - 0 - 0 - 0
| | | | |
0 - 0 - 1 - 0 - 0
The point (0,2)
is an ideal meeting point, as the total travel distance of 2+2+2=6 is minimal. So return 6.
Hint:
- Try to solve it in one dimension first. How can this solution apply to the two dimension case?
Solution: Math, Sort
这道题让我们求最佳的开会地点,该地点需要到每个为1的点的曼哈顿距离之和最小,题目中给了我们提示,让我们先从一维的情况来分析,那么我们先看一维时有两个点A和B的情况,
______A_____P_______B_______
那么我们可以发现,只要开会为位置P在[A, B]区间内,不管在哪,距离之和都是A和B之间的距离,如果P不在[A, B]之间,那么距离之和就会大于A和B之间的距离,那么我们现在再加两个点C和D:
______C_____A_____P_______B______D______
我们通过分析可以得出,P点的最佳位置就是在[A, B]区间内,这样和四个点的距离之和为AB距离加上CD距离,在其他任意一点的距离都会大于这个距离,那么分析出来了上述规律,这题就变得很容易了,我们只要给位置排好序,然后用最后一个坐标减去第一个坐标,即CD距离,倒数第二个坐标减去第二个坐标,即AB距离,以此类推,直到最中间停止,那么一维的情况分析出来了,二维的情况就是两个一维相加即可,参见代码如下:
version 1: 6ms
class Solution {
int minTotalDistance(vector<int>& v) {
int res = 0;
sort(v.begin(), v.end());
int i = 0, j = v.size()-1;
while (i < j) res += v[j--]-v[i++];
return res;
}
public:
int minTotalDistance(vector<vector<int>>& grid) {
vector<int> rows, cols;
for (int i = 0; i < grid.size(); ++i) {
for (int j = 0; j < grid[0].size(); ++j) {
if (grid[i][j] == 1) {
rows.push_back(i);
cols.push_back(j);
}
}
}
return minTotalDistance(rows)+minTotalDistance(cols);
}
};
version 2: 6ms
我们也可以不用多写一个函数,直接对rows和cols同时处理,稍稍能简化些代码:
class Solution {
public:
int minTotalDistance(vector<vector<int>>& grid) {
vector<int> rows, cols;
for (int i = 0; i < grid.size(); ++i) {
for (int j = 0; j < grid[0].size(); ++j) {
if (grid[i][j] == 1) {
rows.push_back(i);
cols.push_back(j);
}
}
}
sort(rows.begin(), rows.end());
sort(cols.begin(), cols.end());
int res = 0, i = 0, j = rows.size()-1;
while (i < j) res += rows[j]-rows[i]+cols[j--]-cols[i++];
return res;
}
};