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© 2006 Pearson Addison-Wesley. All rights reserved14 B-1 Chapter 14 (continued) Graphs

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-2 Depth-First Search Depth-first search (DFS) traversal –Proceeds along a path from v as deeply into the graph as possible before backing up –Does not completely specify the order in which it should visit the vertices adjacent to v –A last visited, first explored strategy

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-3 Depth-First Search +dfs(in v:Vertex) //Traverses a graph beginning at vertex v by using a depth-first search: Recursive version Mark v as visited for(each unvisited vertex u adjacent to v) dfs(u)

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© 2006 Pearson Addison-Wesley. All rights reserved5 B-4 Breadth-First Search Breadth-first search (BFS) traversal –Visits every vertex adjacent to a vertex v that it can before visiting any other vertex –A first visited, first explored strategy –An iterative form uses a queue –A recursive form is possible, but not simple

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© 2006 Pearson Addison-Wesley. All rights reserved5 B-5 Implementing a BFS Iterator Class Using the JCF BFSIterator class uses the ListIterator class –As a queue to keep track of the order the vertices should be processed BFSIterator constructor –Initiates methods used to determine BFS order of vertices for the graph Graph is searched by processing vertices from each vertex’s adjacency list –In the order that they were pushed onto the queue

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-6 Applications of Graphs: Topological Sorting Topological order –A list of vertices in a directed graph without cycles such that vertex x precedes vertex y if there is a directed edge from x to y in the graph –There may be several topological orders in a given graph Topological sorting –Arranging the vertices into a topological order

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-7 Topological Sorting Figure 14-15 The graph in Figure 14-14 arranged according to the topological orders a) a, g, d, b, e, c, f and b) a, b, g, d, e, f, c Figure 14-14 A directed graph without cycles

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-8 Topological Sorting Simple algorithms for finding a topological order –topSort1 Find a vertex that has no successor Remove from the graph that vertex and all edges that lead to it, and add the vertex to the beginning of a list of vertices Add each subsequent vertex that has no successor to the beginning of the list When the graph is empty, the list of vertices will be in topological order

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-9 Topological Sorting Simple algorithms for finding a topological order (Continued) –topSort2 A modification of the iterative DFS algorithm Strategy –Push all vertices that have no predecessor onto a stack –Each time you pop a vertex from the stack, add it to the beginning of a list of vertices –When the traversal ends, the list of vertices will be in topological order

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-10 Spanning Trees A tree –An undirected connected graph without cycles A spanning tree of a connected undirected graph G –A subgraph of G that contains all of G’s vertices and enough of its edges to form a tree To obtain a spanning tree from a connected undirected graph with cycles –Remove edges until there are no cycles

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-11 Spanning Trees You can determine whether a connected graph contains a cycle by counting its vertices and edges –A connected undirected graph that has n vertices must have at least n – 1 edges –A connected undirected graph that has n vertices and exactly n – 1 edges cannot contain a cycle –A connected undirected graph that has n vertices and more than n – 1 edges must contain at least one cycle

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-12 Spanning Trees Figure 14-19 Connected graphs that each have four vertices and three edges

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-13 The DFS Spanning Tree To create a depth-first search (DFS) spanning tree –Traverse the graph using a depth-first search and mark the edges that you follow –After the traversal is complete, the graph’s vertices and marked edges form the spanning tree

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-14 The BFS Spanning Tree To create a breath-first search (BFS) spanning tree –Traverse the graph using a bread-first search and mark the edges that you follow –When the traversal is complete, the graph’s vertices and marked edges form the spanning tree

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-15 Minimum Spanning Trees Minimum spanning tree –A spanning tree for which the sum of its edge weights is minimal Prim’s algorithm –Finds a minimal spanning tree that begins at any vertex –Strategy Find the least-cost edge (v, u) from a visited vertex v to some unvisited vertex u Mark u as visited Add the vertex u and the edge (v, u) to the minimum spanning tree Repeat the above steps until there are no more unvisited vertices

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-16 Shortest Paths Shortest path between two vertices in a weighted graph –The path that has the smallest sum of its edge weights Dijkstra’s shortest-path algorithm –Determines the shortest paths between a given origin and all other vertices –Uses A set vertexSet of selected vertices An array weight, where weight[v] is the weight of the shortest (cheapest) path from vertex 0 to vertex v that passes through vertices in vertexSet

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-17 Circuits A circuit –A special cycle that passes through every vertex (or edge) in a graph exactly once Euler circuit –A circuit that begins at a vertex v, passes through every edge exactly once, and terminates at v –Exists if and only if each vertex touches an even number of edges Figure 14-27 a) Euler’s bridge problem and b) its multigraph representation

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-18 Some Difficult Problems Three applications of graphs –The traveling salesperson problem –The three utilities problem –The four-color problem

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-19 The Four Colour Problem

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-20 The Four Colour Problem Given graph, a colouring is an assignment of colours to the vertices such that no adjacent vertices are the same colour Related: colouring a map so that adjacent countries are not the same colour Maps can be translated to planar graphs... graphs with no crossing edges

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-21 The Four Colour Problem From maps to graphs

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-22 The Four Colour Problem Colouring a geographic map

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-23 The Four Colour Problem How many colours are needed to colour a map? In 1852, Francis Guthrie observed that he only needed 4 colours to colour a map of the counties in England He conjectured that 4 is always enough

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-24 The Four Colour Problem After 124 years... a “proof” was produced (Appel and Haken, 1976) But the proof basically worked like this: –Find a whole bunch of important special cases that capture all important configurations [there were 1936 of these] –Prove that all of these cases can be solved

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-25 The Four Colour Problem The “proof” uses a computer to find the special cases... and it cannot be verified by hand The written part of the proof is 500 pages, and very tedious –Has never been completely verified

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-26 The Four Colour Problem Is this a proof? –If it can not be verified by hand, how do we know the computer found all the right cases? This sparked major foundational debate We know proof != truth... so even if the computer is right – do we have a proof? Debate has fizzled to some degree...

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-27 Summary The two most common implementations of a graph are the adjacency matrix and the adjacency list Graph searching –Depth-first search goes as deep into the graph as it can before backtracking –Bread-first search visits all possible adjacent vertices before traversing further into the graph Topological sorting produces a linear order of the vertices in a directed graph without cycles

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-28 Summary Trees are connected undirected graphs without cycles –A spanning tree of a connected undirected graph is a subgraph that contains all the graph’s vertices and enough of its edges to form a tree A minimum spanning tree for a weighted undirected graph is a spanning tree whose edge- weight sum is minimal The shortest path between two vertices in a weighted directed graph is the path that has the smallest sum of its edge weights

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-29 Summary An Euler circuit in an undirected graph is a cycle that begins at vertex v, passes through every edge in the graph exactly once, and terminates at v A Hamilton circuit in an undirected graph is a cycle that begins at vertex v, passes through every vertex in the graph exactly once, and terminates at v

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-30 Exercise Write pseudocode for an iterative algorithm that determines a DFS spanning tree for an undirected graph.

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-31 Solution dFSTree(v) // Forms a spanning tree for a connected, undirected graph // beginning at vertex v by using depth-first search: // Iterative version. s.createStack() // push v onto stack and mark it s.push(v) Mark v as visited //...cont’d

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-32 // loop invariant: there is a path from vertex v at the // bottom of the stack s to the vertex at the top of s while (!s.isEmpty()) { if (no unvisited vertices are adjacent to the vertex at the top of the stack) s.pop() // backtrack else { Select an unvisited vertex u adjacent to the vertex at the top of the stack Mark the edge from u to the vertex at the the top of the stack. s.push(u) Mark u as visited } // end if } // end while

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-33 Exercise Revise the topSort1 algorithm by removing predecessors instead of successors. Trace the new algorithm for this graph: a b c d e

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-34 Solution topSort1(g, l) // Arranges the vertices of g into a // topological order and places them in list l. n = number of vertices in g for(step = 1 through n) { select a vertex v that has no predecessors l.listInsert(step, v) Delete from g vertex v and its edges } // end for // end TopSort1

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-35 Solution a b c d e b c d e c d e c e c add a to list: a add b to list: a b add d to list: a b d add e to list: a b d e add c to list: a b d e c (done)

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-36 Exercise In the text it says that a recursive BFS algorithm is not straightforward. a)why is this true? b)Write the pseudocode for the recursive version.

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-37 Solution a) The recursive BFS algorithm is not simple because the underlying data structure is a queue. It must be a queue to ensure that vertices close to the current vertex are visited before those further away. DFS uses a stack and so can naturally be implemented recursively.

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© 2006 Pearson Addison-Wesley. All rights reserved14 B-38 Solution (b) bFS(v) // Traverses a graph beginning at vertex v by using a // breadth-first search: Recursive version. q.createQueue() Mark v as visited for(each unvisited vertex u adjacent to v) q.enqueue(u) // loop invariant: there is a path from vertex v // to every vertex in the queue q while(!q.isEmpty()) { w=q.dequeue() bFS(w) } // end while

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