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Clone Graph

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.graphs;

import java.util.*;

/* * ============================================================================================ * PROBLEM: Clone Graph * ============================================================================================ * * --------------------- * 1. Problem Description * --------------------- * Given a reference to a node in a connected undirected graph, return a deep copy (clone) * of the graph. Each node contains a value (int) and a list of its neighbors. * * --------------------- * 2. Logical Breakdown & Mathematical Formulation * --------------------- * A "Deep Copy" means we must create entirely new instances of every node and replicate * all adjacency connections. Simply copying references would result in a shallow copy. * * Algorithm (BFS with Hash Map): * 1. Mapping Strategy: * Use a Hash Map map<OriginalNode, ClonedNode> to keep track of already created * clones. This prevents infinite loops in graphs with cycles. * * 2. Traversal: * - Initialize a Queue for BFS and add the starting node. * - Create a clone of the starting node and store it in the map. * * 3. Processing: * While the Queue is not empty: * - Pop the original node u. * - For each neighbor v of u: * a. If v has not been cloned yet (not in map): * - Create a clone of v. * - Store the mapping {v: clone_v} in the map. * - Enqueue v to process its neighbors later. * b. Add the clone of v to the neighbors list of the clone of u. * * --------------------- * 3. Constraints & Complexity Analysis * --------------------- * Constraints: * - The number of nodes is between 1 and 100. * - Node values are unique. * * Time Complexity: * O(V + E) - We visit every node and every edge exactly once. * * Space Complexity: * O(V) - To store the mapping of original nodes to cloned nodes and the BFS queue. * * ============================================================================================ / class UndirectedGraphNode { int label; List neighbors; UndirectedGraphNode(int x) { label = x; neighbors = new ArrayList(); } }; public class CloneGraph { public int solve(int A) { // TODO: Implement solution return 0; }

public UndirectedGraphNode cloneGraph(UndirectedGraphNode node1) {
    return new UndirectedGraphNode(1);
}

}