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# The scenario: have an army of ants (or whatever) all starting at the START - PowerPoint PPT Presentation

The scenario: have an army of ants (or whatever) all starting at the START vertex (say, A), moving at unit speed away from it in all directions. Whenever a group of ants hits a vertex, they split apart going in all directions. Clearly, the first ant to hit a node has gone along the

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## PowerPoint Slideshow about 'The scenario: have an army of ants (or whatever) all starting at the START' - yamka

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Presentation Transcript

The scenario: have an army of ants (or whatever) all starting at the START

vertex (say, A), moving at unit speed away from it in all directions.

Whenever a group of ants hits a vertex, they split apart going in all

directions. Clearly, the first ant to hit a node has gone along the

shortest path.

Some things to notice about these ants:

1. If an ant gets to a vertex that's already been visited, it can quit

since it can't possibly be the first to anywhere.

(this will map to the "if V is visited..." line below)

2. In fact, there's no reason to even start down a path to a vertex if

you know it's been visited before.

(this will map to the "if X is visited..." line below)

Say we wanted to implement this idea in a computer program. What

would we need to keep track of? In terms of the vertices, we could

keep track of:

unseen[X] = false if X has already been visited by the ants

val[X] = the time the ants first got there (the length of the

shortest path to X)

dad[X] = where those ants came from.

Then we also need to keep track of the ants: for each group of ants

currently active we want to know where they are (what edge are they

on) and when are they going to get to the endpoint they're heading

towards.

Then, in each step of the algorithm, we just need to find out

what the next event is ("event" == ants hitting a vertex) and update

our data structures.

Priority-First Search starting at the STARTMinimal Spanning Tree

init: update (start, start, 0) into Pqueue

set all val[k] = 0 and unseen[k] = true

While queue not empty:

(U, V, priority) = pqueue.removeMin();

if V already visited, continue; // corresponds to (1) above

mark V as visited (seen).

set val[V] = priority

for each unvisted (unseen) neighbor X of V:

update (V, X, weight(VXedge)) into pqueue.

Priority-First Search starting at the STARTShortest Path

init: update (start, start, 0) into Pqueueset all val[k] = 0 and unseen[k] = true

While queue not empty:

(U, V, priority) = pqueue.removeMin();

if V already visited, continue; // corresponds to (1) above

mark V as visited (seen).

set val[V] = priority

for each unvisted (unseen) neighbor X of V:

update (V, X, val[V] + weight(VX edge)) into pqueue.

How long does the above algorithm take? starting at the START

Answer: there are at most |E| "inserts" (because once you send ants

down an edge, you will never send them down that edge again) and so

also at most |E| removeMins. So, the time is

O(|E|*(time for insert) + |E|*(time for removeMin))

Max number of items in pqueue at any point in time is |E|. So, if we

can get the inserts and removeMins to O(log n) time we will have

time = O(|E| * log(|E|))

We can do this by using a min-heap. To removemin we take the root off

(that's the minimum) and then re-make the heap by replacing it with

the rightmost leaf and doing a "downheap"/"siftUp" procedure.