K Closest Points to Origin

// C — sort by distance

// C++ — max-heap of size k
vector<vector<int>> kClosest(vector<vector<int>>& points, int k) {
    auto dist = [](vector<int>& p){ return p[0]*p[0] + p[1]*p[1]; };
    priority_queue<pair<int,int>> maxH; // (dist, index)
    for (int i = 0; i < (int)points.size(); i++) {
        maxH.push({dist(points[i]), i});
        if ((int)maxH.size() > k) maxH.pop();
    }
    vector<vector<int>> res;
    while (!maxH.empty()) { res.push_back(points[maxH.top().second]); maxH.pop(); }
    return res;
}

// Java
public int[][] kClosest(int[][] points, int k) {
    PriorityQueue<int[]> maxH = new PriorityQueue<>(
        (a, b) -> (b[0]*b[0]+b[1]*b[1]) - (a[0]*a[0]+a[1]*a[1]));
    for (int[] p : points) {
        maxH.offer(p);
        if (maxH.size() > k) maxH.poll();
    }
    return maxH.toArray(new int[k][]);
}

// JavaScript
function kClosest(points, k) {
    const dist = p => p[0]*p[0] + p[1]*p[1];
    points.sort((a, b) => dist(a) - dist(b));
    return points.slice(0, k);
}

# Python
import heapq

def kClosest(points, k):
    # Max-heap (negate distance)
    heap = []
    for x, y in points:
        d = -(x*x + y*y)
        heapq.heappush(heap, (d, x, y))
        if len(heap) > k:
            heapq.heappop(heap)
    return [[x, y] for _, x, y in heap]