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On a campus represented as a 2D grid, there are `N`

workers and `M`

bikes, with `N <= M`

. Each worker and bike is a 2D coordinate on this grid.

We assign one unique bike to each worker so that the sum of the Manhattan distances between each worker and their assigned bike is minimized.

The Manhattan distance between two points `p1`

and `p2`

is ```
Manhattan(p1,
p2) = |p1.x - p2.x| + |p1.y - p2.y|
```

.

Return the minimum possible sum of Manhattan distances between each worker and their assigned bike.

**Example 1:**

Input:workers = [[0,0],[2,1]], bikes = [[1,2],[3,3]]Output:6Explanation:We assign bike 0 to worker 0, bike 1 to worker 1. The Manhattan distance of both assignments is 3, so the output is 6.

**Example 2:**

Input:workers = [[0,0],[1,1],[2,0]], bikes = [[1,0],[2,2],[2,1]]Output:4Explanation:We first assign bike 0 to worker 0, then assign bike 1 to worker 1 or worker 2, bike 2 to worker 2 or worker 1. Both assignments lead to sum of the Manhattan distances as 4.

**Note:**

`0 <= workers[i][0], workers[i][1], bikes[i][0], bikes[i][1] < 1000`

- All worker and bike locations are distinct.
`1 <= workers.length <= bikes.length <= 10`

All contents and pictures on this website come from the Internet and are updated regularly every week. They are for personal study and research only, and should not be used for commercial purposes. Thank you for your cooperation.