280wk14_x4

# 280wk14_x4 - Breadth-First Search Input G V E[a connected...

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Unformatted text preview: Breadth-First Search Input G ( V, E ) [a connected graph] v [start vertex] Algorithm Breadth-First Search visit v V ← { v } [ V is the vertices already visited] Put v on Q [ Q is a queue] repeat while Q 6 = ∅ u ← head ( Q ) [ head ( Q ) is the first item on Q ] for w ∈ A ( u ) [ A ( u ) = { w |{ u, w } ∈ E } ] if w / ∈ V then visit w Put w on Q V ← V ∪ { w } endif endfor Delete u from Q The BFS algorithm basically finds a tree embedded in the graph. • This is called the BFS search tree 1 BFS and Shortest Length Paths If all edges have equal length, we can extend this algo- rithm to find the shortest path length from v to any other vertex: • Store the path length with each node when you add it. • Length( v ) = 0. • Length( w ) = Length( u ) + 1 With a little more work, can actually output the shortest path from u to v . • This is an example of how BFS and DFS arise unex- pectedly in a number of applications. ◦ We’ll see a few more 2 Depth-First Search Input G ( V, E ) [a connected graph] v [start vertex] Algorithm Depth-First Search visit v V ← { v } [ V is the vertices already visited] Put v on S [ S is a stack] u ← v repeat while S 6 = ∅ if A ( u )- V 6 = ∅ then Choose w ∈ A ( u )- V visit w V = V ∪ { w } Put w on stack u ← w else u ← top ( S ) [Pop the stack] endif endrepeat DFS uses backtracking • Go as far as you can until you get stuck • Then go back to the first point you had an untried choice 3 Spanning Trees A spanning tree of a connected graph G ( V, E ) is a con- nected acyclic subgraph of G , which includes all the ver- tices in V and only (some) edges from E . Think of a spanning tree as a “backbone”; a minimal set of edges that will let you get everywhere in a graph. • Technically, a spanning tree isn’t a tree, because it isn’t directed. The BFS search tree and the DFS search tree are both spanning trees. • In the text, they give algorithms to produce minimum weight spanning trees • That’s done in CS 482, so we won’t do it here. 4 Graph Coloring How many colors do you need to color the vertices of a graph so that no two adjacent vertices have the same color? • Application: scheduling ◦ Vertices of the graph are courses ◦ Two courses taught by same prof are joined by edge ◦ Colors are possible times class can be taught. Lots of similar applications: • E.g. assigning wavelengths to cell phone conversations to avoid interference. ◦ Vertices are conversations ◦ Edges between “nearby” conversations ◦ Colors are wavelengths. • Scheduling final exams ◦ Vertices are courses ◦ Edges between courses with overlapping enrollment ◦ Colors are exam times. 5 Chromatic Number The chromatic number of a graph G , written χ ( G ), is the smallest number of colors needed to color it so that no two adjacent vertices have the same color....
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280wk14_x4 - Breadth-First Search Input G V E[a connected...

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