Given a list of points on the 2-D plane and an integer K. The task is to find K closest points to the origin and print them.**Note**: The distance between two points on a plane is the Euclidean distance.

**Examples:**

Input :point = [[3, 3], [5, -1], [-2, 4]], K = 2Output :[[3, 3], [-2, 4]] Square of Distance of origin from this point is (3, 3) = 18 (5, -1) = 26 (-2, 4) = 20 So rhe closest two points are [3, 3], [-2, 4].Input :point = [[1, 3], [-2, 2]], K = 1Output :[[-2, 2]] Square of Distance of origin from this point is (1, 3) = 10 (-2, 2) = 8 So the closest point to origin is (-2, 2)

**Approach :** The idea is to calculate the euclidean distance from the origin for every given point and sort the array according to the euclidean distance found. Print the first k closest points from the list.

**Algorithm :**

Consider two points with coordinates as (x1, y1) and (x2, y2) respectively. The **euclidean distance **between these two points will be:

√{(x2-x1)^{2}+ (y2-y1)^{2}}

- Sort the points by distance using Euclidean distance formula.
- Select first K points form the list
- Print the points obtained in any order.

Below is the implementation of the above approach:

## C++

`// C++ program for implementation of ` `// above approach ` `#include<bits/stdc++.h>` `using` `namespace` `std;` `// Function to print required answer` `void` `pClosest(vector<vector<` `int` `>> pts, ` `int` `k)` `{` ` ` ` ` `// In multimap values gets ` ` ` `// automatically sorted based on ` ` ` `// their keys which is distance here` ` ` `multimap<` `int` `, ` `int` `> mp;` ` ` `for` `(` `int` `i = 0; i < pts.size(); i++)` ` ` `{` ` ` `int` `x = pts[i][0], y = pts[i][1];` ` ` `mp.insert({(x * x) + (y * y) , i});` ` ` `}` ` ` ` ` `for` `(` `auto` `it = mp.begin(); ` ` ` `it != mp.end() && k > 0;` ` ` `it++, k--) ` ` ` `cout << ` `"["` `<< pts[it->second][0] << ` `", "` ` ` `<< pts[it->second][1] << ` `"]"` `<< ` `"\n"` `;` `}` `// Driver code` `int` `main() ` `{` ` ` `vector<vector<` `int` `>> points = { { 3, 3 },` ` ` `{ 5, -1 },` ` ` `{ -2, 4 } };` ` ` ` ` `int` `K = 2;` ` ` ` ` `pClosest(points, K);` ` ` `return` `0;` `}` `// This code is contributed by sarthak_eddy.` |

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## Java

`// Java program for implementation of ` `// above approach ` `import` `java.util.*;` `class` `GFG{` ` ` `// Function to print required answer` `static` `void` `pClosest(` `int` `[][]pts, ` `int` `k) ` `{` ` ` `int` `n = pts.length;` ` ` `int` `[] distance = ` `new` `int` `[n];` ` ` `for` `(` `int` `i = ` `0` `; i < n; i++)` ` ` `{` ` ` `int` `x = pts[i][` `0` `], y = pts[i][` `1` `];` ` ` `distance[i] = (x * x) + (y * y);` ` ` `}` ` ` `Arrays.sort(distance);` ` ` ` ` `// Find the k-th distance` ` ` `int` `distk = distance[k - ` `1` `];` ` ` `// Print all distances which are ` ` ` `// smaller than k-th distance` ` ` `for` `(` `int` `i = ` `0` `; i < n; i++)` ` ` `{` ` ` `int` `x = pts[i][` `0` `], y = pts[i][` `1` `];` ` ` `int` `dist = (x * x) + (y * y);` ` ` ` ` `if` `(dist <= distk)` ` ` `System.out.println(` `"["` `+ x + ` `", "` `+ y + ` `"]"` `);` ` ` `}` `}` `// Driver code` `public` `static` `void` `main (String[] args)` `{` ` ` `int` `points[][] = { { ` `3` `, ` `3` `}, ` ` ` `{ ` `5` `, -` `1` `},` ` ` `{ -` `2` `, ` `4` `} };` ` ` `int` `K = ` `2` `;` ` ` ` ` `pClosest(points, K);` `}` `}` `// This code is contributed by sarthak_eddy.` |

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## Python3

`# Python3 program for implementation of ` `# above approach` `# Function to return required answer` `def` `pClosest(points, K):` ` ` `points.sort(key ` `=` `lambda` `K: K[` `0` `]` `*` `*` `2` `+` `K[` `1` `]` `*` `*` `2` `)` ` ` `return` `points[:K]` `# Driver program` `points ` `=` `[[` `3` `, ` `3` `], [` `5` `, ` `-` `1` `], [` `-` `2` `, ` `4` `]]` `K ` `=` `2` `print` `(pClosest(points, K))` |

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**Output:**

[[3, 3], [-2, 4]]

**Complexity Analysis:**

**Time Complexity:**O(n log n).

Time complexity to find the distance from origin for every point is O(n) and to sort the array is O(n log n)**Space Complexity:**O(1).

As no extra space is required.

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