Search⌘ K
AI Features
Home
Courses
Grokking the Coding Interview Patterns in Python

Solution: Minimize Manhattan Distances

Explore how to reduce the maximum Manhattan distance between points on a 2D plane by strategically removing one point. Understand the relationship between coordinate sums and differences, identify extreme values, and apply an O(n) time complexity solution to optimize distance.

We'll cover the following...

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 ...