Download presentation

Presentation is loading. Please wait.

1
**ALG0183 Algorithms & Data Structures**

“The shortest-path algorithms are all single-source algorithms, which begin at some starting point and compute the shortest paths from it to all vertices.” Weiss ...to all vertices. ALG0183 Algorithms & Data Structures Lecture 22 n negative weights present (Bellman-Ford algorithm) …as implemented in Graph.java by Weiss Chapter 14 Weiss 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

2
**Java exception handling in Graph.java.**

In u, d, n, and a, an exception is thrown if the start vertex is not found Vertex w = vertexMap.get( startName ); if( w == null ) throw new NoSuchElementException(“Start vertex not found" ); In printPath(String), an exception is thrown if the destination vertex is not found. Vertex w = vertexMap.get( destName ); if( w == null ) throw new NoSuchElementException( "Destination vertex not found" ); In d (Dijkstra), an exception is thrown if the graph has an negative edge if( cvw < 0 ) throw new GraphException( "Graph has negative edges" ); 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

3
**Java exception handling in Graph.java.**

In n (Bellman-Ford), an exception is thrown if a graph has an negative cycle if( v.scratch++ > 2 * vertexMap.size( ) ) throw new GraphException( "Negative cycle detected" ); In a (acyclic topological sort), an exception is thrown if a graph has a cycle if( iterations != vertexMap.size( ) ) throw new GraphException( "Graph has a cycle!" ); scratch iterations 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

4
**The problem with negative cost cycles.**

Figure A graph with a negative cost cycle (Weiss ©Addison-Wesley) The path from V3 to V4 has a cost of 2. The path V3V4V1V3V4, going through the destination V4 twice, has a cost of -3! We could stay in the cycle an arbitrary number of times, lowering the cost each cycle! “Thus the shortest path between these two points (V3 & V4) is undefined.” Weiss 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

5
**The problem with negative cost cycles.**

Figure A graph with a negative cost cycle (Weiss ©Addison-Wesley) The shortest path from V2 to V5 is also undefined i.e. the problem with a negative cost cycle is not restricted to nodes within the cycle (V1, V3, V4). When a negative-cost cycle is present, many shortest paths in a graph can be undefined. “Negative-cost edges by themselves are not necessarily bad; it is the cycles that are.” Weiss 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

6
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

Bellman-Ford Algorithm Definition: An efficient algorithm to solve the single-source shortest-path problem. Weights may be negative. The algorithm initializes the distance to the source vertex to 0 and all other vertices to ∞. It then does V-1 passes (V is the number of vertices) over all edges relaxing, or updating, the distance to the destination of each edge. Finally it checks each edge again to detect negative weight cycles, in which case it returns false. The time complexity is O(VE), where E is the number of edges. The algorithm has to iterate over the edges several times to make sure that the effect of any negative edge is fully propagated. 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

7
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

SUNY Binghamton CS480 mini-lectures -- Bellman-Ford if a better path has been found |V| is the number of vertices |E| is the number of edges 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

8
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

Pseudocode implementation (http://en.wikipedia.org/wiki/Bellman-Ford_algorithm) // Step 1: Initialize graph for each vertex v in vertices: if v is source then v.distance := 0 else v.distance := infinity v.predecessor := null // Step 2: relax edges repeatedly for i from 1 to size(vertices)-1: for each edge uv in edges: // uv is the edge from u to v u := uv.source v := uv.destination if u.distance + uv.weight < v.distance: if a better path has been found v.distance := u.distance + uv.weight v.predecessor := u // Step 3: check for negative-weight cycles for each edge uv in edges: if u.distance + uv.weight < v.distance: error "Graph contains a negative-weight cycle" (-3) 3 -5 zero-cost cycle (2) 2 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

9
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

Example from Digraphs: Theory, Algorithms and Applications, 1st Edition , Jørgen Bang-Jensen and Gregory Gutin Initialize graph. For assessment purposes, students should be able to produce the working shown in (a)-(f) for any small graph. 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

10
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

* Infinity is represented by a large finite number in code, so a distance cost to a node might be reduced from “infinity”. d a ab, b still infinite ac, c still infinite * ba, a still infinite bc, c still infinite * cb, b still infinite da, a still infinite * dc, c still infinite ec, c still infinite *ed, d still infinite sd, d->4 se, e->8 “In the inner loop of the second step of the algorithm the arcs are considered in the lexicographic order: ab, ac, ba, bc, cb, da, dc, ec, ed, sd, se.” 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

11
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

* Infinity is represented by a large finite number in code, so a distance cost to a node might be reduced from “infinity”. d a ab, b still infinite ac, c still infinite * ba, a still infinite bc, c still infinite * cb, b still infinite da, a ->8 dc, c->2 ec, c no change ed, d->3 sd, d no change se, e no change “In the inner loop of the second step of the algorithm the arcs are considered in the lexicographic order: ab, ac, ba, bc, cb, da, dc, ec, ed, sd, se.” 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

12
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

ab, b->17 ac, c no change ba, a no change bc, c no change cb, b->0 da, a ->7 dc, c->1 ec, c no change ed, d no change sd, d no change se, e no change “In the inner loop of the second step of the algorithm the arcs are considered in the lexicographic order: ab, ac, ba, bc, cb, da, dc, ec, ed, sd, se.” 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

13
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

ab, b no change ac, c no change ba, a->-3 bc, c no change cb, b->-1 da, a no change dc, c no change ec, c no change ed, d no change sd, d no change se, e no change “In the inner loop of the second step of the algorithm the arcs are considered in the lexicographic order: ab, ac, ba, bc, cb, da, dc, ec, ed, sd, se.” 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

14
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

6 vertices 5th iteration ab, b no change ac, c no change ba, a->-4 bc, c no change cb, b no change da, a no change dc, c no change ec, c no change ed, d no change sd, d no change se, e no change “In the inner loop of the second step of the algorithm the arcs are considered in the lexicographic order: ab, ac, ba, bc, cb, da, dc, ec, ed, sd, se.” 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

15
**Features of algorithm n by Weiss.**

“Whenever a vertex has its distance lowered, it must be placed on a queue. This may happen repeatedly for each vertex.” “Consequently, we use the queue as in the unweighted algorithm, but we use Dv + cv,w as the distance measure (as in Dijkstra´s algorithm). simple, not prioritised queue a vertex can be enqueued several times Note: In Weiss´s implementation, the outer loop is controlled by a test on the queue. Weiss´s implementation might require less computation than an implementation which is based on a fixed number of iterations through all the edges: we need an experiment! 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

16
**Features of algorithm n by Weiss.**

The variable scratch is incremented when a vertex is put on the queue (“enqueued”). The variable scratch is also incremented when a vertex is taken off the queue (“dequeued”). If the vertex is on the queue, scratch is odd. The number of times a vertex has been dequeued is scratch/2. “We do not enqueue a vertex if it is already on the queue.” 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

17
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

startName from user public void negative( String startName ) { clearAll( ); call to the reset method for each vertex Vertex start = vertexMap.get( startName ); get start vertex from the map if( start == null ) throw new NoSuchElementException( "Start vertex not found" ); Queue<Vertex> q = new LinkedList<Vertex>( ); make queue q.add( start ); add the start to the queue start.dist = 0; start to itself has distance zero start.scratch++; start is enqueued, so increment scratch The increment operator in Java is ++. The increment operator adds one. If a postfix increment is used (e.g. a++) then the value of a is used in the expression in which a resides, and then a is incremented by 1. 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

18
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

while( !q.isEmpty( ) ) { Vertex v = q.remove( ); remove the head of the queue if( v.scratch++ > 2 * vertexMap.size( ) ) scratch/2 is number times dequeued throw new GraphException( "Negative cycle detected" ); for( Edge e : v.adj ) { iterate over a vertex´s edges Vertex w = e.dest; double cvw = e.cost; if( w.dist > v.dist + cvw ) { if a better path has been found w.dist = v.dist + cvw; w.prev = v; note of previous vertext (v) on the current shortest path // Enqueue only if not already on the queue if( w.scratch++ % 2 == 0 ) if w.scratch is odd, w is already on the queue q.add( w ); add to queue else w.scratch--; // undo the enqueue increment w not added to queue } When better paths are no longer found, vertices are no longer added to the queue, and the algorithm terminates when the queue is empty. 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

19
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

Note this graph also contains a positive-cost cycle. NegGraphTest1.txt V2 V0 4 V2 V5 5 V0 V1 2 V0 V3 1 V3 V2 2 V3 V5 8 V3 V6 4 V6 V5 1 V3 V4 2 V1 V3 3 V4 V1 -10 V4 V6 6 running algorithm n File read... 7 vertices Enter start node:V2 Enter destination node:V4 Enter algorithm (u, d, n, a ): n gr.GraphException: Negative cycle detected Enter start node: 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

20
**ALG0183 Algorithms & Data Structures by Dr Andy Brooks**

NegGraphTest1.txt V2 V0 4 V2 V5 5 V0 V1 2 V0 V3 1 V3 V2 2 V3 V5 8 V3 V6 4 V6 V5 1 V3 V4 2 V4 V1 -10 V4 V6 6 X V1 V3 3 running algorithm n Skipping ill-formatted line File read... 7 vertices Enter start node:V2 Enter destination node:V1 Enter algorithm (u, d, n, a ): n (Cost is: -3.0) V2 to V0 to V3 to V4 to V1 Enter start node: 8/25/2009 ALG0183 Algorithms & Data Structures by Dr Andy Brooks

Similar presentations

OK

Fundamentals, Terminology, Traversal, Algorithms Graph Algorithms Telerik Algo Academy

Fundamentals, Terminology, Traversal, Algorithms Graph Algorithms Telerik Algo Academy

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on narendra modi Ppt on computer malwarebytes Download ppt on civil disobedience movement in united Ppt on history of olympics in canada Ppt on cartesian product database Ppt on indian national political parties Ppt on different types of computer languages Ppt on revolt of 1857 ppt Ppt on waxes lipids Ppt on central limit theorem example