Depth-First SearchDepth-first search algorithm searches deeper in graph whenever possible. In this, edges are explored out of the most recently visited vertex that still has unexplored edges leaving it. When all the vertices of that vertex’s edges have been explored, the search goes backtracks to explore edges leaving the vertex from which a vertex was recently discovered. This process continues until we have discovered all the vertices that are reachable from the original source vertex. It works on both directed and undirected graphs. Click here, or on the image below to check out Java Applet Visualizations of Depth and Breadth First Search AnalysisThe DFS function is called exactly once for every vertex that is reachable from the start vertex. Each call looks at the successors of the current vertex, so total time is O(# reachable nodes + total # of outgoing edges from those nodes). The running time of DFS is therefore O(V + E). Complete Tutorial with Examples : Depth First Search - C Program Source code#include<stdio.h> Quick Notes about the Algorithm and the code:Input Format: Graph is directed and unweighted. First two integers must be number of vertices and edges which must be followed by pairs of vertices which has an edge between them. maxVertices represents maximum number of vertices that can be present in the graph. vertices represent number of vertices and edges represent number of edges in the graph. graph[i][j] represent the weight of edge joining i and j. size[maxVertices] is initialed to{0}, represents the size of every vertex i.e. the number of edges corresponding to the vertex. visited[maxVertices]={0} represents the vertex that have been visited. Initialize the graph. For presentVertex = 0 to vertices if visited[presentVertex] is 0, i.e. if the vertex has not been visited then call Dfs function. presentVertex represents the vertex that is being tackled. Dfs function is called to get the shortest path. Dfs functionThis function takes the graph obtained (graph[ ][ maxVertices]), pointer to the array size and visited, and the presentValue as arguments. Print the vertex that is being visited now, which is presentVertex visited[presentVertex] = 1 as the vertex has now been visited. Iterate through all the vertices connected to the presentVertex and perform Dfs on those vertices that are not visited before For iter=0 to size[presentVertex]-1 if (!visited[graph[presentVertex][iter]]) Dfs(graph,size,graph[presentVertex][iter],visited) next Related Tutorials (basic Graph Algorithms) :
Testing Zone For Programmers-Try out our online Multiple-Choice-Question tests in Programming and Computer Science!Photo-credits: www.istockphoto.com Some Important Data Structures and Algorithms, at a glance:
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Basic Data Structures and Algorithms Stacks Last In First Out data structures ( LIFO ). Like a stack of cards from which you pick up the one on the top ( which is the last one to be placed on top of the stack ). Documentation of the various operations and the stages a stack passes through when elements are inserted or deleted. C program to help you get an idea of how a stack is implemented in code. Queues First in First Out data structure (FIFO). Like people waiting to buy tickets in a queue - the first one to stand in the queue, gets the ticket first and gets to leave the queue first. Documentation of the various operations and the stages a queue passes through as elements are inserted or deleted. C Program source code to help you get an idea of how a queue is implemented in code. Single Linked List A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind. Double Linked List- A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind in front of it, as well as another of its own kind, which happens to be behind it in the sequence. Circular Linked List Linked list with no head and tail - elements point to each other in a circular fashion. Binary Search Trees A basic form of tree data structures. Inserting and deleting elements in them. Different kind of binary tree traversal algorithms. Heaps - A tree like data structure where every element is lesser (or greater) than the one above it. Heap formation, sorting using heaps in O(n log n) time. Height Balanced Trees - Ensuring that trees remain balanced to optimize complexity of operations which are performed on them. Graphs Depth First Search - Traversing through a graph using Depth First Search in which unvisited neighbors of the current vertex are pushed into a stack and visited in that order. Breadth First Search - Traversing through a graph using Breadth First Search in which unvisited neighbors of the current vertex are pushed into a queue and then visited in that order. Minimum Spanning Trees: Kruskal Algorithm- Finding the Minimum Spanning Tree using the Kruskal Algorithm which is a greedy technique. Introducing the concept of Union Find. Minumum Spanning Trees: Prim's Algorithm- Finding the Minimum Spanning Tree using the Prim's Algorithm. Dijkstra Algorithm for Shortest Paths- Popular algorithm for finding shortest paths : Dijkstra Algorithm. Floyd Warshall Algorithm for Shortest Paths- All the all shortest path algorithm: Floyd Warshall Algorithm Bellman Ford Algorithm - Another common shortest path algorithm : Bellman Ford Algorithm. Dynamic Programming A technique used to solve optimization problems, based on identifying and solving sub-parts of a problem first. Integer Knapsack problemAn elementary problem, often used to introduce the concept of dynamic programming. Matrix Chain Multiplication Given a long chain of matrices of various sizes, how do you parenthesize them for the purpose of multiplication - how do you chose which ones to start multiplying first? Longest Common Subsequence Given two strings, find the longest common sub sequence between them. Elementary cases : Fractional Knapsack Problem, Task Scheduling - Elementary problems in Greedy algorithms - Fractional Knapsack, Task Scheduling. Along with C Program source code. Data Compression using Huffman TreesCompression using Huffman Trees. A greedy technique for encoding information. |