# CSC 413/513: Intro to Algorithms - PowerPoint PPT Presentation

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CSC 413/513: Intro to Algorithms. Graph Algorithms DFS. Depth-First Search. Depth-first search is another strategy for exploring a graph Explore “deeper” in the graph whenever possible Edges are explored out of the most recently discovered vertex v that still has unexplored edges

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CSC 413/513: Intro to Algorithms

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## CSC 413/513: Intro to Algorithms

Graph Algorithms

DFS

### Depth-First Search

• Depth-first search is another strategy for exploring a graph

• Explore “deeper” in the graph whenever possible

• Edges are explored out of the most recently discovered vertex v that still has unexplored edges

• When all of v’s edges have been explored, backtrack to the vertex from which v was discovered

### Depth-First Search

• Vertices initially colored white

• Then colored gray when discovered

• Then black when finished

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

What does u->d represent?

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

What does u->f represent?

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

Will all vertices eventually be colored black?

sourcevertex

sourcevertex

d f

1 |

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sourcevertex

d f

1 |

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2 |

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sourcevertex

d f

1 |

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2 |

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3 |

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sourcevertex

d f

1 |

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2 |

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3 | 4

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sourcevertex

d f

1 |

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2 |

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3 | 4

5 |

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sourcevertex

d f

1 |

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2 |

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3 | 4

5 | 6

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sourcevertex

d f

1 |

8 |

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2 | 7

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3 | 4

5 | 6

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sourcevertex

d f

1 |

8 |

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2 | 7

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3 | 4

5 | 6

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### DFS Example

sourcevertex

d f

1 |

8 |

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2 | 7

9 |

3 | 4

5 | 6

|

What is the structure of the grey vertices? What do they represent?

sourcevertex

d f

1 |

8 |

|

2 | 7

9 |10

3 | 4

5 | 6

|

sourcevertex

d f

1 |

8 |11

|

2 | 7

9 |10

3 | 4

5 | 6

|

sourcevertex

d f

1 |12

8 |11

|

2 | 7

9 |10

3 | 4

5 | 6

|

sourcevertex

d f

1 |12

8 |11

13|

2 | 7

9 |10

3 | 4

5 | 6

|

sourcevertex

d f

1 |12

8 |11

13|

2 | 7

9 |10

3 | 4

5 | 6

14|

sourcevertex

d f

1 |12

8 |11

13|

2 | 7

9 |10

3 | 4

5 | 6

14|15

### DFS Example

sourcevertex

d f

1 |12

8 |11

13|16

2 | 7

9 |10

3 | 4

5 | 6

14|15

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

What will be the running time?

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

Running time: O(n2) because call DFS_Visit on each vertex, and the loop over Adj[] can run as many as |V| times

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### Depth-First Search: The Code

BUT, there is actually a tighter bound. How many times will DFS_Visit() actually be called?

### Depth-First Search Analysis

• For a node u

• DFS_Visit(u) cannot be called more than once

• DFS_Visit(u) cannot be called less than once

• Therefore, exactly once

• DFS_Visit(u) requires c + ϴ(|Adj(u)|) time

• The fact that recursive calls are made to v’s is accounted for by DFS_Visit(v)

• Total= ∑u ( c + ϴ(|Adj(u)|) ) = ϴ(V+E)

• Considered linear in input size, since adjacency list is a ϴ(V+E) representation

### DFS: Kinds of edges

• DFS introduces an important distinction among edges in the original graph:

• Tree edge: encounter new (white) vertex

• The tree edges form a spanning forest

• Can tree edges form cycles? Why or why not?

sourcevertex

d f

1 |12

8 |11

13|16

2 | 7

9 |10

3 | 4

5 | 6

14|15

Tree edges

### DFS: Kinds of edges

• DFS introduces an important distinction among edges in the original graph:

• Tree edge: encounter new (white) vertex

• Back edge: from descendent to ancestor

• Encounter a grey vertex (grey to grey)

sourcevertex

d f

1 |12

8 |11

13|16

2 | 7

9 |10

3 | 4

5 | 6

14|15

Tree edges

Back edges

### DFS: Kinds of edges

• DFS introduces an important distinction among edges in the original graph:

• Tree edge: encounter new (white) vertex

• Back edge: from descendent to ancestor

• Forward edge: from ancestor to descendent

• Not a tree edge, though

• If forward edge, then grey node to black node

• Converse not true; could be a cross edge (see next)

sourcevertex

d f

1 |12

8 |11

13|16

2 | 7

9 |10

3 | 4

5 | 6

14|15

Tree edges

Back edges

Forward edges

### DFS: Kinds of edges

• DFS introduces an important distinction among edges in the original graph:

• Tree edge: encounter new (white) vertex

• Back edge: from descendent to ancestor

• Forward edge: from ancestor to descendent

• Cross edge: between or within trees

• (Also) from a grey node to a black node

• No ancestor-descendent relation, if in the same DFS tree

sourcevertex

d f

1 |12

8 |11

13|16

2 | 7

9 |10

3 | 4

5 | 6

14|15

Tree edges

Back edges

Forward edges

Cross edges

### DFS: Kinds of edges

• DFS introduces an important distinction among edges in the original graph:

• Tree edge: encounter new (white) vertex

• Back edge: from descendent to ancestor

• Forward edge: from ancestor to descendent

• Cross edge: between a tree or subtrees

• Note: tree & back edges are important; most algorithms don’t distinguish forward & cross

### DFS: Kinds Of Edges

• Thm 22.10: If G is undirected, a DFS produces only tree and back edges

• Assume there’s a forward edge

• But F? edge must actually be a back edge (why?)

source

F?

### DFS: Kinds Of Edges

• Thm 22.10: If G is undirected, a DFS produces only tree and back edges

• Assume there’s a cross edge

• But C? edge cannot be cross:

• must be explored from one of the vertices it connects, becoming a treevertex, before other vertex is explored

• So in fact the picture is wrong…bothlower tree edges cannot in fact betree edges

source

C?

### DFS And Graph Cycles

• Thm: An undirected graph is acyclic iff a DFS yields no back edges

• If acyclic, then no back edges (because a back edge implies a cycle)

• If no back edges, then acyclic

• No back edges implies only tree edges (Why?)

• Only tree edges implies we have a tree or a forest

• Which by definition is acyclic

• Thus, can run DFS to find whether a graph has a cycle

### DFS And Cycles

• How would you modify the code to detect cycles?

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

If v->color == GREY and Pred(u)≠v, …

### DFS And Cycles

• What will be the running time?

DFS(G)

{

for each vertex u  G->V

{

u->color = WHITE;

}

time = 0;

for each vertex u  G->V

{

if (u->color == WHITE)

DFS_Visit(u);

}

}

DFS_Visit(u)

{

u->color = GREY;

time = time+1;

u->d = time;

{

if (v->color == WHITE)

DFS_Visit(v);

}

u->color = BLACK;

time = time+1;

u->f = time;

}

### DFS And Cycles

• What will be the running time?

• A: O(V+E)

• We can actually determine if cycles exist in O(V) time:

• In an undirected acyclic forest, |E|  |V| - 1. So if no cycle, then O(V+E) is O(V)

• If cycle, count the edges: if ever see |V| distinct edges, must have seen a back edge along the way

• Can halt and output “cycle exists”

• O(V) time