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Grokking the Coding Interview Patterns in Python

Solution: Minimize Manhattan Distances

Explore an efficient solution to minimize the maximum Manhattan distance between 2D points by removing exactly one point. Understand how analyzing coordinate sums and differences helps identify key points affecting the distances, allowing you to optimize the solution with linear time complexity.

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Statement

You are given an array, points, where each element in points[i] =[xj,yi]= [x_j, y_i] represents the integer coordinates of a point in a 2D plane. The distance between any two points is defined as the Manhattan distanceThe Manhattan distance between two cells (x1, y1) and (x2, y2) is |x_1 - x_2| + |y_1 - y_2|..

Your task is to determine and return the smallest possible value for the maximum distance between any two points after removing exactly one point from the array.

Constraints:

  • 3≤3 \leq points.length ≤103\leq 10^3 ...