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Graphs and Tree Decomposition

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Graph Representations adjacency matrix node list edge list ADF CH BEG A B C D E F G H A - 1 1 1 1 1 0 0 B 1 - 1 0 1 0 0 1 C 1 1 - 1 1 0 0 1 D 1 0 1 - 0 1 1 1 E 1 1 1 0 - 1 1 0 F 1 0 0 1 1 - 1 1 G 0 0 0 1 1 1 - 1 H 0 1 1 1 0 1 1 - A - B C D E F B - A C E H C - A B D E H D - A C F G H E - A B C F G F - A D E G H G - D E F H H - B C D F G A B A C A D A E A F B A B C B E B H C A C B C D C E C H D A D C D F D G D H E A E B E C E F E G F A F D F E F G F H G D G E G F G H H B H C H D H F H G node list - lists the nodes connected to each node edge list - lists each of the edges as a pair of nodes undirected edges may be listed twice XY and YX in order to simplify algorithm implementation adjacency matrix - for an n-node graph we build an n x n array with 1's indicating edges and 0's no edge the main diagonal of the matrix is unused unless a node has an edge connected to itself. If graph is weighted, 1's are replaced with edge weight values

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Graph Depth-First Traversal (DFT) Algorithm Implementation text file representation Given a graph G(V,E) and a starting node v start perform a depth-first traversal. Provide a list of the nodes in the order they are traversed. Graph is defined in a text file of the following format: 1st Line - # of nodes, N # of edges, E 2nd Line - List of node labels (you may assume 1 char each) 3rd Line - through N+3 Line an N x N adjacency matrix ADF CH BEG 8 A B C D E F G H 0 1 1 1 1 1 0 0 1 0 1 0 1 0 0 1 1 1 0 1 1 0 0 1 1 0 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 1 1 0 1 1 0

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Graph Depth-First Traversal (DFT) Algorithm Implementation pseudo-code DFT( node v k ) { // deal with current node for each node, vi { if (node_avail(v i )) then DFT(v i ) } node_avail( v i ) is a Boolean function that returns true if node v i is available for traversal, which means that v i has not been traversed, and v i is adjacent to v k.

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Floyd's Algorithm for Shortest Paths V1V1 V2V2 V5V5 V4V4 V3V3 1 5 1 1 2 6 1 3 2 1 2 3 4 5 1 0 2 - - - 2 3 0 6 - - 3 - - 0 - 1 4 2 - 1 0 1 5 1 - - - 0 procedure floyd(W,D:matype) is begin D:=W; for k in 1..n loop for i in 1..n loop for j in 1..n loop D(i,j):=min(D(i,j),D(i,k)+D(k,j)); end loop; end floyd; Floyd's algorithm is very simple to implement. The fact that it works at all is not obvious. Be sure to work through the proof of algorithm correctness in the text.

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ADF CH BEG Graph Breadth-First Traversal Given a graph G(V,E) and a starting vertex s, perform a breadth-first traversal (BFT) of G. such that each reachable vertex is entered exactly once. If all vertices are reachable, the edges traversed and the set of vertices will represent a spanning tree embedded in the graph G. 1) BFT suggests an iterative process (rather than a recursive one) 2) BFT vertices order of traversal can be maintained using a Queue data structure 3) The preferred representation for the graph is an adjacency matrix 4) We will need a way to keep up with which vertices have been "used" (e.g. a Boolean list) 5) Process begins by placing the starting vertex in the Queue 6) A vertex is taken from the Queue, every unused vertex adj to this vertex is added to the Queue This operation is repeated until the Queue is empty. 8) The output (answer) is returned in the form of a list of vertices in the order they entered the Queue

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3 0 2 5 6 4 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 01234560123456 0 1 2 3 4 5 6 Breadth-First Traversal of a Graph There are many applications in computer science for which we need to examine the nodes of a graph (or tree) in some orderly fashion. Previously we have looked at depth-first traversal using a recursive method operating on a stack data structure. Now we will investigate breadth-first traversal using an iterative method operating on a queue data structure. A graph consisting of nodes and edges connecting pairs of nodes can be represented as an adjacency matrix in which a 1 in row i column j indicates that there is an edge connecting node i to node j. If there is no edge, the corresponding value in the adjacency matrix will be zero (0).

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import java.util.Scanner; import java.io.*; public class BFT { public static void main(String[] args) throws Exception { int[][] mat = readMat(); showMat(mat); } public static int[][] readMat() { try { Scanner input = new Scanner(new File("adjmat.txt")); int nrows = input.nextInt(); int ncols = input.nextInt(); int[][] m = new int[nrows][ncols]; for(int i = 0; i < nrows; i++) for(int j = 0; j < ncols; j++) m[i][j] = input.nextInt(); input.close(); return m; } catch(IOException e) { e.printStackTrace(); return null; } } public static void showMat(int[][] m) { for(int i=0;i

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import java.util.Scanner; import java.io.*; public class BFT { public static int[][] mat; public static boolean[] avail; public static void main(String[] args) throws Exception { mat = readMat(); avail = new boolean[mat.length]; for(int i=0;i

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public static void bft(int n) { int cur; java.util.Queue q = new java.util.LinkedList (); q.offer(n); avail[n] = false; while(q.size()>0) { cur = q.remove(); System.out.print(cur + " "); for(int k=0;k

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public class sortTree { public Node root = null; public sortTree(double val) { root = new Node(val); } public Node add(Node node, double val) { if (node == null) return node = new Node(val); if (val < node.data) node.left = add(node.left, val); else node.right = add(node.right, val); return node; } public void showAll(Node node) { if (node != null) { showAll(node.left); System.out.println(node.data); showAll(node.right); } } protected class Node { double data; Node left; Node right; public Node(double val) { this(val,null,null); } public Node(double val, Node left_node, Node right_node) { data = val; left = left_node; right = right_node; } } } sortTree Class

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public class sortTreeDemo { public static void main(String[] args) { sortTree myTree = new sortTree(Math.random()*1000.0); for(int i=1;i<1000000;i++) myTree.add(myTree.root,Math.random()*1000.0); myTree.showAll(myTree.root); } } sortTreeDemo

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