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B Closest Points To Origin

Logical Breakdown

  • [ ] Subproblem Identification:
  • [ ] Optimal Substructure:
  • [ ] Constraint Handling: (e.g., Modulo \(10^9 + 7\))
  • [ ] Optimization: (Matrix Exponentiation if 'A' is huge)

Mathematical Rigor

State Definition

Let \(dp[i]\) be the state for the \(i\)-th subproblem.

Recurrence Relation

\[ dp[i] = ... \]

Visualization

graph TD A["Current State"] --> B["Previous State 1"] A --> C["Previous State 2"]

Complexity Analysis

Approach Time Complexity Space Complexity
Iterative DP \(O(N)\) \(O(1)\)

Code Reference

package com.dsa.heaps;

/* * ============================================================================================ * PROBLEM: B Closest Points to Origin * ============================================================================================ * * --------------------- * 1. Problem Description * --------------------- * Given a set of points (x, y) in A and an integer B. * Find the B points that are closest to the origin (0, 0). * Distance is calculated using the Euclidean formula: sqrt(x^2 + y^2). * * --------------------- * 2. Logical Breakdown & Mathematical Formulation * --------------------- * To find the "top-k smallest" distances, we use a Max-Heap of size B. * * Step 1: Distance Metric * For the purpose of comparison, we can use the squared Euclidean distance: * $\(d^2 = x^2 + y^2\)$ * We avoid sqrt to maintain precision and improve performance. * * Step 2: Max-Heap Strategy * 1. Initialize a Max-Heap to store point indices or objects, ordered by their \(d^2\). * 2. Iterate through each point in A: * a. Calculate its squared distance. * b. Add the point to the Max-Heap. * c. If the size of the heap exceeds B, remove the point with the maximum distance. * 3. After processing all points, the heap contains the B closest points. * * --------------------- * 3. Constraints & Complexity Analysis * --------------------- * Time Complexity: * O(N log B) * - We iterate through N points once. * - Each heap operation (insertion/deletion) takes O(log B). * * Space Complexity: * O(B) - To store the B closest points in the Priority Queue. * * ============================================================================================ / public class BClosestPointsToOrigin { public int[][] solve(int[][] a1, int A) { // TODO: Implement solution return new int[0][0]; } }