Find directed graph that has the adjacency matrix Find directed graph that has the adjacency matrix
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An adjacency list graph is a data structure that represents connections between vertices in a graph. It is efficient for sparse graphs with fewer edges. Each vertex is stored with a list of its neighboring vertices, making it easy to find adjacent vertices and traverse the graph. This data structure is commonly used in algorithms like depth-first search and breadth-first search.
One efficient way to find all cycles in a directed graph is by using algorithms like Tarjan's algorithm or Johnson's algorithm, which can identify and list all cycles in the graph. These algorithms work by traversing the graph and keeping track of the nodes visited to detect cycles.
You can use a The Depth-First Search algorithm.
DFS, BFS
To Find the number in that matrix and check that number adjacency elements... import java.util.Scanner; public class FindAdjacencyMatrix { public static int[][] array1 = new int[30][30]; public static int i,j,num,m,n; public static void main(String args[]) { Scanner input = new Scanner(System.in); //------------------------------------------------------------------------------------------------- System.out.println("Enter the m ,n matrix"); m = input.nextInt(); n = input.nextInt(); //------------------------------------------------------------------------------------------------- System.out.println("Enter the matrix Element one by one:"); for(i = 0; i < m; i++) { for(j = 0; j < n; j++) { array1[i][j] = input.nextInt(); } } System.out.println("The Given Matrix is :"); for(i = 0; i < m; i++) { for(j = 0; j < n; j++) { System.out.print(" "+array1[i][j]); } System.out.print("\n"); } //------------------------------------------------------------------------------------------------- System.out.println("Find The Adjacency Elements for Given Number : "); System.out.println("Enter The Number : "); num = input.nextInt(); for(i = 0; i < m; i++) { for(j = 0; j < n; j++) { if(num == array1[i][j]) { System.out.println("Element is Found :"+num); findAdjacency(num,i,j); break; } } } //-------------------------------------------------------------------------------------- } private static void findAdjacency(int elem,int row,int col) { try { if( array1[row][col-1]!=-1) { System.out.println("Left Adjacency : "+array1[row][col-1]); } } catch(Exception e){ System.out.println(" Exception Throwing "); } try{ if(array1[row][col+1]!= -1) { System.out.println("Right Adjacency : "+array1[row][col+1]); } }catch(Exception e){ System.out.println(" Exception Throwing "); } try { if(array1[row-1][col]!= -1) { System.out.println("Top Adjacency : "+array1[row-1][col]); } } catch(Exception e){ System.out.println(" Exception Throwing "); } try { if(array1[row+1][col]!= -1) { System.out.println("Botto Adjacency : "+array1[row+1][col]); } } catch(Exception e){ System.out.println(" Exception Throwing "); } } //---------------------------------------------------------------------------------------------- }
One efficient way to find the shortest path in a directed acyclic graph is to use a topological sorting algorithm, such as the topological sort algorithm. This algorithm can help identify the order in which the nodes should be visited to find the shortest path from a starting node to a destination node. By following the topological order and calculating the shortest path for each node, you can determine the overall shortest path in the graph.
To find the original matrix of an inverted matrix, simply invert it again. Consider A^-1^-1 = A^1 = A
You find the equation of a graph by finding an equation with a graph.
To find a unitary matrix, one must first square the matrix and then take the conjugate transpose of the result. If the conjugate transpose of the squared matrix is equal to the identity matrix, then the original matrix is unitary.
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Cayleys formula states that for a complete graph on nvertices, the number of spanning trees is n^(n-2). For a complete bipartite graph we can use the formula p^q-1 q^p-1. for the number of spanning trees. A generalization of this for any graph is Kirchhoff's theorem or Kirchhoff's matrix tree theorem. This theorem looks at the Laplacian matrix of a graph. ( you may need to look up what that is with some examples). For graphs with a small number of edges and vertices, you can find all the spanning trees and this is often quicker. There are also algorithms such as depth-first and breadth-first for finding spanning trees.