added-bron-kerbosch

Backtracking Algorithms/Bron-Kerbosch Algorithm/Program.c

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+#include <stdio.h>
+#include <stdlib.h>
+
+#define MAX_VERTICES 100
+
+// Function to print a maximal clique
+void printClique(int *clique, int size) {
+    printf("Maximal Clique: ");
+    for (int i = 0; i < size; i++) {
+        printf("%d ", clique[i]);
+    }
+    printf("\n");
+}
+
+// Bron-Kerbosch algorithm
+void bronKerbosch(int *R, int r_size, int *P, int p_size, int *X, int x_size, int graph[MAX_VERTICES][MAX_VERTICES]) {
+    if (p_size == 0 && x_size == 0) {
+        printClique(R, r_size);
+        return;
+    }
+
+    for (int i = 0; i < p_size; i++) {
+        int v = P[i];
+
+        // Create new sets for R, P, and X
+        int newR[MAX_VERTICES];
+        int newP[MAX_VERTICES];
+        int newX[MAX_VERTICES];
+        int new_r_size = r_size + 1;
+        int new_p_size = 0, new_x_size = 0;
+
+        // Add v to R
+        newR[0] = v;
+
+        // Add vertices adjacent to v to newP and newX
+        for (int j = 0; j < MAX_VERTICES; j++) {
+            if (graph[v][j]) {
+                if (j < p_size) {
+                    newP[new_p_size++] = j; // Add to newP
+                }
+                if (j < x_size) {
+                    newX[new_x_size++] = j; // Add to newX
+                }
+            }
+        }
+
+        // Combine R and P and recursively call the algorithm
+        for (int j = 0; j < r_size; j++) {
+            newR[j + 1] = R[j]; // Combine R
+        }
+
+        bronKerbosch(newR, new_r_size, newP, new_p_size, newX, new_x_size, graph);
+
+        // Update P and X
+        for (int j = 0; j < p_size; j++) {
+            if (P[j] == v) {
+                // Remove v from P
+                for (int k = j; k < p_size - 1; k++) {
+                    P[k] = P[k + 1];
+                }
+                p_size--;
+                break;
+            }
+        }
+        // Add v to X
+        X[x_size++] = v;
+    }
+}
+
+// Main function
+int main() {
+    int graph[MAX_VERTICES][MAX_VERTICES] = {0}; // Adjacency matrix
+    int n; // Number of vertices
+
+    // Define the graph
+    printf("Enter number of vertices: ");
+    scanf("%d", &n);
+
+    printf("Enter the adjacency matrix:\n");
+    for (int i = 0; i < n; i++) {
+        for (int j = 0; j < n; j++) {
+            scanf("%d", &graph[i][j]);
+        }
+    }
+
+    // Initialize R, P, and X
+    int R[MAX_VERTICES] = {0}; // Current clique
+    int P[MAX_VERTICES]; // Potential candidates
+    int X[MAX_VERTICES]; // Excluded vertices
+    int p_size = 0;
+
+    // Initialize P with all vertices
+    for (int i = 0; i < n; i++) {
+        P[p_size++] = i;
+    }
+
+    // Call the Bron-Kerbosch algorithm
+    bronKerbosch(R, 0, P, p_size, X, 0, graph);
+
+    return 0;
+}

Backtracking Algorithms/Bron-Kerbosch Algorithm/README.md

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+# Bron-Kerbosch Algorithm
+
+The **Bron-Kerbosch algorithm** is a backtracking algorithm used to find all maximal cliques in an undirected graph. A clique is a subset of vertices, all of which are adjacent to each other, and a maximal clique is a clique that cannot be extended by including an adjacent vertex. This algorithm is known for its recursive nature and efficiency in finding cliques in graphs without needing to search through all vertex subsets.
+
+## Definition
+
+The Bron-Kerbosch algorithm is a **recursive backtracking algorithm** that enumerates all maximal cliques in an undirected graph. By maintaining three sets during recursion, the algorithm explores all potential cliques and ensures that each maximal clique is discovered only once.
+
+## Characteristics
+
+- **Exactness**: The algorithm is exact and produces all maximal cliques without duplication.
+- **Recursiveness**: It uses recursive backtracking with sets of vertices to manage the state at each step.
+- **Efficiency**: The algorithm is highly efficient, especially for sparse graphs.
+- **Pivoting**: The pivoting technique can be used to reduce the number of recursive calls, optimizing performance by reducing the search space.
+
+## Time Complexity
+
+The time complexity of the Bron-Kerbosch algorithm varies depending on the implementation:
+
+- **Without pivoting**: , where \(n\) is the number of vertices.
+- **With pivoting**: The worst-case complexity remains `O(3^(n/3))`, but pivoting reduces the number of recursive calls in practice, making the algorithm faster on average.
+
+## Space Complexity
+
+The space complexity is **O(n + E)**, where \(n\) is the number of vertices and \(E\) is the number of edges. This is primarily due to storing the adjacency list or matrix and maintaining three sets (R, P, and X) in each recursive call.
+
+## Approach
+
+The Bron-Kerbosch algorithm follows these steps:
+
+1. **Initialize three sets**:
+   - **R**: The growing clique being constructed.
+   - **P**: The set of vertices that can still be added to the current clique.
+   - **X**: The set of vertices that have already been considered for the current clique.
+
+2. **Recursive Backtracking**:
+   - At each step, choose a vertex from \(P\) and add it to \(R\), forming a new potential clique.
+   - Recur with updated sets:
+     - **R** includes the chosen vertex.
+     - **P** is restricted to vertices adjacent to the new vertex.
+     - **X** is updated to avoid revisiting vertices.
+
+3. **Base Case**:
+   - If both \(P\) and \(X\) are empty, \(R\) is a maximal clique.
+
+4. **Pivot Optimization (Optional)**:
+   - Choose a pivot vertex to minimize the number of recursive calls by excluding vertices that are unlikely to form new cliques.
+
+## Summary
+
+The Bron-Kerbosch algorithm efficiently finds all maximal cliques in a graph, making it useful in various fields such as social network analysis and bioinformatics. With optional pivoting, it is adaptable to dense and sparse graphs alike, offering high-performance clique detection for applications requiring maximal clique identification.