When intuition is wrong Heuristics for clique and maximum clique

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When intuition is wrong Heuristics for clique and maximum clique Patrick Prosser esq.

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When intuition is wrong Heuristics for clique and maximum clique. Patrick Prosser esq. You have a set of people You have to produce the largest group of people such t hat everyone in the group knows each other How would you do that?. Solve it!. - PowerPoint PPT Presentation

Transcript of When intuition is wrong Heuristics for clique and maximum clique

Page 1: When intuition is wrong Heuristics for clique and maximum clique

When intuition is wrongHeuristics for clique and maximum clique

Patrick Prosser esq.

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You have a set of people

You have to produce the largest group of people suchthat everyone in the group knows each other

How would you do that?

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To a man with a hammer everything looks like a nail

Solve it!

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lovely lovely java

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Model 3

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Model 3

colouring lower bound

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clique(n,p,k)

Given a random graph G(n,p) is there a clique of size k or more?

GT[19]

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clique(n,p,k)

Given a random graph G(n,p) is there a clique of size k or more?

GT[19]

A “decision problem”, NP-complete

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clique(n,p,k)

Given a random graph G(n,p) is there a clique of size k or more?

GT[19]

A “decision problem”, NP-complete

What we did:• Generate 100 instances of G(50,0.9)• Vary k from 1 to 50

• apply Model 1 with max-degree heuristic• measure search cost of clique(G(50,0.9), k)• determine if sat or unsat

• Analyse results (5,000 points)

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Search effort (decisions)

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scatter

max

mean

med

Search effort (decisions)

Vary that

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Complexity peak coincide with crossover point

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vary p

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vary n

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clique(50,0.9,k) for four heuristics

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Search is on a log scale!

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What are those heuristics?!

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H00: choose max degree and rejectH01: choose max degree and acceptH10: choose min degree and rejectH11: choose min degree and accept

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H00: choose max degree and rejectH01: choose max degree and acceptH10: choose min degree and rejectH11: choose min degree and accept

H1*

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WHY?

H1*

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SoCS-TV

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SoCS-TV Normal service will soon be resumed

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… but just to build tension, here’s what happens whenwe compare models (normal service will soon be resumed)

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SoCS-TV

… and we are back

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What’s all that “phase transition” malarkey?

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Constrainedness (circa 1996)

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kappa for clique

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kappa for clique

kappa seems to work … it fits the empirical results reasonably well

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minimise kappa

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minimise kappa

When you make a decision make sure it drives you into the easy

soluble region … capiche?easysoluble

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minimise kappa

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minimise kappa

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minimise kappa

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minimise kappa

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minimise kappa

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I wonder if kappa can

predict heuristic

behaviour?

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If a heuristic is good for the decision problem, will it be

good for optimisation?

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optimisation

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What about the DIMACS benchmarks?

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DIMACS

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DIMACS

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DIMACS

?

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DIMACS

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DIMACS

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DIMACS

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How not to do it

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How not to do it

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Could we predict the size of the largest clique?

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But we must apply this with caution … we cannot always

assume that the graph is random.

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What lessons have I learned?

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What lessons have I learned?

If ethical approval is not required, go wild!

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What lessons have I learned?

Try and explain why things are the way

they are.

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What lessons have I learned?

If there is theory … use it!

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What lessons have I learned?

Be honest … report all of your results

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What lessons have I learned?

And finally … never lend anyone any

books

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But what about Michaela Regneri’s dinner?

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FATA-TV