For the 1d case, the best meeting point is just the median point.
Since the distance is computed using the Manhattan Distance, we can decompose this 2-d problem into two 1-d problems and combine (add) their solutions. In fact, the best meeting point is just the point that comprised by the two best meeting points in each dimension.
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|.
This post shares a nice Python code. However, translating it into C++ makes it so ugly...
For example, given three people living at
(0,2) is an ideal meeting point, as the total travel distance of 2+2+2=6 is minimal. So return 6.