Problem

In a Meta location-ranking pipeline, you are given 2D points and need to return the k points closest to the origin (0, 0). Distance is based on Euclidean distance, but you should avoid unnecessary square root operations.

Formal Specification

Write a function that takes:

• points: a list of points, where each point is a list [x, y]
• k: an integer

Return a list of k points that have the smallest distance from the origin. The output may be in any order.

Use squared distance x*x + y*y for comparison, since it preserves ordering.

Examples

Example 1

• Input: points = [[1,3],[-2,2]], k = 1
• Output: [[-2,2]]
• Explanation: Squared distances are 10 and 8, so [-2,2] is closer.

Example 2

• Input: points = [[3,3],[5,-1],[-2,4]], k = 2
• Output: [[3,3],[-2,4]]
• Explanation: Squared distances are 18, 26, and 20. The two smallest are 18 and 20.

Constraints

• 1 <= len(points) <= 10^4
• -10^4 <= x, y <= 10^4
• 1 <= k <= len(points)
• Points are not necessarily unique
• Any valid order of the returned k points is acceptable