Min Manhattan Distance in Matrix - 1point3acres


矩阵中两元素最小曼哈顿距离
http://massivealgorithms.blogspot.com/2015/12/leetcode-317-shortest-distance-from-all.html
还有我看了近三个月的面经,感觉 数字翻转那个题(1981旋转180°之后是1861) 和 矩阵里找两个元素的最小曼哈顿距离的题 有点高频啊,后面面试的同学仔细看看吧

我也印象见过矩阵里找两个元素的最小曼哈顿距离的题几次。

原帖找不到了但是大意是这个:

矩阵里面有0,1,2,3

0表示可以通过
3表示障碍物不能穿行。

现在需要你找离1最短的2.
?
323003
130001  <- 最左边的1是离右下角的2最短距离的。
000333
330002


我觉得最直接的应该就是对每个1进行bfs, 然后返回距离最近的2, 然后比较得出最短的距离。
还有一种想法就是能不能同时对所有2同时进行bsf,首先遇到的1就是最短距离,这个思路和bidirectional bsf差不多,只是同时搜索。


其实就是变图中单源最短路径为多源最短路径
https://www.1point3acres.com/bbs/forum.php?mod=viewthread&tid=511673
   正式的题是一个二维矩阵上面有X, Y,O,问从X到Y的最短距离(曼哈顿距离)
楼主说的应该是有很多的X, Y求其中曼哈顿距离最小的一对pair吧。这个题也看到过很多次了


https://www.jiuzhang.com/qa/885/
初阶:在一个n*m的矩阵中,有k个点,求矩阵中距离这k个点的距离和最近的点。
进阶:如果要求这个点与所给的k个点不重合,该怎么办?

注:这里的距离采用曼哈顿距离——|x0-x1| + |y0-y1|

初阶:因为采用曼哈顿距离,所以可以分开考虑x坐标和y坐标。将k个点的x坐标排序,可以知道要求的x坐标一定在这k个点的x坐标上,扫描一遍并统计到各个点的x坐标距离和,找到使得距离和最小的x坐标。这一步只需要O(k)的时间复杂度,而不是O(k^2),怎么优化这里不多赘述。对y坐标做同样的操作,从而得到答案。时间复杂度O(klogk),排序的复杂度。

进阶:通过初阶的算法得到一个最优位置,如果这个位置与k个点重合,则从这个位置开始进行搜索,将这个点周围的点和对应的距离放入到一个堆里,每次从堆中取出最小距离的点,然后将这个点周围的点放入堆中,直到取出的点不与所给k个点重合。时间复杂度klogk,因为最多从堆中取出k+1个点即可找到一个不与所给k个点重合的点。堆每次操作为logk。

面试官角度:

本题的最优算法较难想到。所以如果公司要求不高,答出O(nm)的方法即可。O(nm)的方法是因为假设我们知道在(x,y)这个位置的距离和为S,那么当(x,y)移动到(x+1,y)和(x,y+1)的时候,我们可以在O(1)的时间更新S。方法是预处理每一行上方/下方有多少个k个点中的点,每一列左侧/右侧有多少个k个点中的点。上面的解答基于nm>>klogk,如果k比较大,则还是O(nm)的方法更好。答题时需要答出对于给定参数不同情况下采用不用算法这一点。


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