Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf ·...

92
Outline Groups Probability on Finite Groups Minkowski functionals and Valuations Applications Algebraic and Numeric Programming environments Aspects of Group Theory in Stochastic Problems Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia November 18, 2008 Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Transcript of Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf ·...

Page 1: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Aspects of Group Theory in Stochastic Problems

Dr. Marconi BarbosaNICTA/ANU, Canberra, Australia

November 18, 2008

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 2: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 3: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 4: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 5: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 6: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 7: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 8: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Mallow’s Model

I Harmonic analysis on manifolds

I Fourier transforms on groups

I Graph matching: edge info added into node features

I What we do know about Metropolis algorithm? ExactResults?

I Fastest Mixing Markov Chains

I Parallel Coset enumeration

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 9: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 10: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 11: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 12: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 13: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 14: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 15: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 16: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 17: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 18: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 19: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 20: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

Outline of what is (would be nice) to come...

I Lagrange Theorem

I Example: Fermat Little theorem and cryptography

I Orbit Counting Theorem

I Example: Cube orbits

I Magic cube group: Scary

I More scary: Baby Monster

I Freaking out: The Monster group

I Group classification: one slide soft crash course

I Group representation: one slide hard crash course

I Invariance, equivalence and symmetry

I Differential invariants, variational problems with symmetry.

I Geometric probability, Minkowski functionals and continuousGroups.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 21: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Verducci & Fligner: Distance Based Ranking Models

I Diaconis, Boyd & Xiao: Fastest Mixing Markov Chain withsymmetry, 2006.

I Fagin: Comparing partial ranks, how efficient?

I Guy Lebannon: Partial Rankings and Cosets/Posets(nips2007)

I Risi Kondor:Multi-Object tracking...with...simmetric group (nips2006)Diffusion Kernel in graphs and other structures (manifoldmethods)

I J.Huang C. Guestrin & L. Gibbas: Polytope projection afterFourier inverse transform and estimation in Fourier Domain.(nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 22: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Verducci & Fligner: Distance Based Ranking Models

I Diaconis, Boyd & Xiao: Fastest Mixing Markov Chain withsymmetry, 2006.

I Fagin: Comparing partial ranks, how efficient?

I Guy Lebannon: Partial Rankings and Cosets/Posets(nips2007)

I Risi Kondor:Multi-Object tracking...with...simmetric group (nips2006)Diffusion Kernel in graphs and other structures (manifoldmethods)

I J.Huang C. Guestrin & L. Gibbas: Polytope projection afterFourier inverse transform and estimation in Fourier Domain.(nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 23: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Verducci & Fligner: Distance Based Ranking Models

I Diaconis, Boyd & Xiao: Fastest Mixing Markov Chain withsymmetry, 2006.

I Fagin: Comparing partial ranks, how efficient?

I Guy Lebannon: Partial Rankings and Cosets/Posets(nips2007)

I Risi Kondor:Multi-Object tracking...with...simmetric group (nips2006)Diffusion Kernel in graphs and other structures (manifoldmethods)

I J.Huang C. Guestrin & L. Gibbas: Polytope projection afterFourier inverse transform and estimation in Fourier Domain.(nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 24: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Verducci & Fligner: Distance Based Ranking Models

I Diaconis, Boyd & Xiao: Fastest Mixing Markov Chain withsymmetry, 2006.

I Fagin: Comparing partial ranks, how efficient?

I Guy Lebannon: Partial Rankings and Cosets/Posets(nips2007)

I Risi Kondor:Multi-Object tracking...with...simmetric group (nips2006)Diffusion Kernel in graphs and other structures (manifoldmethods)

I J.Huang C. Guestrin & L. Gibbas: Polytope projection afterFourier inverse transform and estimation in Fourier Domain.(nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 25: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Verducci & Fligner: Distance Based Ranking Models

I Diaconis, Boyd & Xiao: Fastest Mixing Markov Chain withsymmetry, 2006.

I Fagin: Comparing partial ranks, how efficient?

I Guy Lebannon: Partial Rankings and Cosets/Posets(nips2007)

I Risi Kondor:Multi-Object tracking...with...simmetric group (nips2006)Diffusion Kernel in graphs and other structures (manifoldmethods)

I J.Huang C. Guestrin & L. Gibbas: Polytope projection afterFourier inverse transform and estimation in Fourier Domain.(nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 26: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

SceneFinite Groups crash course outlineMotivational Papers

I Verducci & Fligner: Distance Based Ranking Models

I Diaconis, Boyd & Xiao: Fastest Mixing Markov Chain withsymmetry, 2006.

I Fagin: Comparing partial ranks, how efficient?

I Guy Lebannon: Partial Rankings and Cosets/Posets(nips2007)

I Risi Kondor:Multi-Object tracking...with...simmetric group (nips2006)Diffusion Kernel in graphs and other structures (manifoldmethods)

I J.Huang C. Guestrin & L. Gibbas: Polytope projection afterFourier inverse transform and estimation in Fourier Domain.(nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 27: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Finite GroupSubgroups and CosetsLagrange TheoremOrbit CountingRelated issues

A group G is a set S and a binary operation ∗ satisfying twoproperties:

I A) Closure. For a, b ∈ G , then a ∗ b ∈ G .

I B) Associativity. The elements (a ∗ b) ∗ c and a ∗ (b ∗ c) arethe same.

There must be two very special members too:

I C) The identity element e is such that:a ∗ e = e ∗ a = a

I D) The inverse b, for any member a:a ∗ b = b ∗ a = e

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 28: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Finite GroupSubgroups and CosetsLagrange TheoremOrbit CountingRelated issues

A group G is a set S and a binary operation ∗ satisfying twoproperties:

I A) Closure. For a, b ∈ G , then a ∗ b ∈ G .

I B) Associativity. The elements (a ∗ b) ∗ c and a ∗ (b ∗ c) arethe same.

There must be two very special members too:

I C) The identity element e is such that:a ∗ e = e ∗ a = a

I D) The inverse b, for any member a:a ∗ b = b ∗ a = e

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 29: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Finite GroupSubgroups and CosetsLagrange TheoremOrbit CountingRelated issues

A group G is a set S and a binary operation ∗ satisfying twoproperties:

I A) Closure. For a, b ∈ G , then a ∗ b ∈ G .

I B) Associativity. The elements (a ∗ b) ∗ c and a ∗ (b ∗ c) arethe same.

There must be two very special members too:

I C) The identity element e is such that:a ∗ e = e ∗ a = a

I D) The inverse b, for any member a:a ∗ b = b ∗ a = e

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 30: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Finite GroupSubgroups and CosetsLagrange TheoremOrbit CountingRelated issues

A group G is a set S and a binary operation ∗ satisfying twoproperties:

I A) Closure. For a, b ∈ G , then a ∗ b ∈ G .

I B) Associativity. The elements (a ∗ b) ∗ c and a ∗ (b ∗ c) arethe same.

There must be two very special members too:

I C) The identity element e is such that:a ∗ e = e ∗ a = a

I D) The inverse b, for any member a:a ∗ b = b ∗ a = e

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 31: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Finite GroupSubgroups and CosetsLagrange TheoremOrbit CountingRelated issues

A group G is a set S and a binary operation ∗ satisfying twoproperties:

I A) Closure. For a, b ∈ G , then a ∗ b ∈ G .

I B) Associativity. The elements (a ∗ b) ∗ c and a ∗ (b ∗ c) arethe same.

There must be two very special members too:

I C) The identity element e is such that:a ∗ e = e ∗ a = a

I D) The inverse b, for any member a:a ∗ b = b ∗ a = e

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

Page 32: Aspects of Group Theory in Stochastic Problemsssll.cecs.anu.edu.au/files/slides/barbosa.pdf · Probability on Finite Groups ... Algebraic and Numeric Programming environments Aspects

OutlineGroups

Probability on Finite GroupsMinkowski functionals and Valuations

ApplicationsAlgebraic and Numeric Programming environments

Finite GroupSubgroups and CosetsLagrange TheoremOrbit CountingRelated issues

I A subgroup of G , is any subset of elements that still forms agroup.

I Consider a subgroup H of G . Associated with H we createthe following setgH = {g .h : ∀h ∈ H}, This is one of the left cosets of H inG , indexed by g .

Next: An example from integers, the Zn family.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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I A subgroup of G , is any subset of elements that still forms agroup.

I Consider a subgroup H of G . Associated with H we createthe following setgH = {g .h : ∀h ∈ H}, This is one of the left cosets of H inG , indexed by g .

Next: An example from integers, the Zn family.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Coset example from naturals

Group: G = Z4

Set S : {0, 1, 2, 3}Operation ∗: integer addition mod 4Subgroup: H = {0, 2}0 ∗ H = {0, 2} = H, a trivial coset.1 ∗ H = {1, 3}, first one here2 ∗ H = {2, 4} = {2, 0} = H, trivial again3 ∗ H = {3, 5} = {3, 1}, nothing new...So the subgroup H has only two cosets:H itself and {1, 3}.Note that the cosets form a partition of the group:Z4 = (1 ∗ H)

⋃H

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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The order of every subgroup H divides the order of G .

I Show that all cosets of H have the same order. Definef : aH ⇒ bH by f = ba−1. This is a bijective map with inversef −1 = ab−1.

I Show that cosets from H form a partition of G : Cosets are eitheridentical or disjoint: every element belongs to only one coset.

I Then, the number of elements in G is equal the number of cosets(index) times the number of elements in each coset (which inturn is equal to the order of H)

|G | = |union of its H-cosets|= (number of cosets)*(number of elements in a coset)

= |G : H|.|H|

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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The order of every subgroup H divides the order of G .

I Show that all cosets of H have the same order. Definef : aH ⇒ bH by f = ba−1. This is a bijective map with inversef −1 = ab−1.

I Show that cosets from H form a partition of G : Cosets are eitheridentical or disjoint: every element belongs to only one coset.

I Then, the number of elements in G is equal the number of cosets(index) times the number of elements in each coset (which inturn is equal to the order of H)

|G | = |union of its H-cosets|= (number of cosets)*(number of elements in a coset)

= |G : H|.|H|

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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The order of every subgroup H divides the order of G .

I Show that all cosets of H have the same order. Definef : aH ⇒ bH by f = ba−1. This is a bijective map with inversef −1 = ab−1.

I Show that cosets from H form a partition of G : Cosets are eitheridentical or disjoint: every element belongs to only one coset.

I Then, the number of elements in G is equal the number of cosets(index) times the number of elements in each coset (which inturn is equal to the order of H)

|G | = |union of its H-cosets|= (number of cosets)*(number of elements in a coset)

= |G : H|.|H|

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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The order of every subgroup H divides the order of G .

I Show that all cosets of H have the same order. Definef : aH ⇒ bH by f = ba−1. This is a bijective map with inversef −1 = ab−1.

I Show that cosets from H form a partition of G : Cosets are eitheridentical or disjoint: every element belongs to only one coset.

I Then, the number of elements in G is equal the number of cosets(index) times the number of elements in each coset (which inturn is equal to the order of H)

|G | = |union of its H-cosets|= (number of cosets)*(number of elements in a coset)

= |G : H|.|H|

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Consequences

The order of any element a of a finite group (i.e. the smallest k forwhich ak = e divides G ) divides the order of G . Because the order ofa is the order of a cyclic subgroup generated by a.

I Fermat’s Little theoremap ≡ a (mod p) ;p prime, a integer

I Euler’s Theoremaφ(n) ≡ 1 (mod n); a co-prime nφ(n)(Euler function) counts the number of co-primes from 1 to n.

I Carmichael’s Theorem aλ(n) ≡ 1 (mod n)λ(n)(Carmichael’s function) gives the smallest integer m forwhich a(m) ≡ 1 (mod n)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Consequences

The order of any element a of a finite group (i.e. the smallest k forwhich ak = e divides G ) divides the order of G . Because the order ofa is the order of a cyclic subgroup generated by a.

I Fermat’s Little theoremap ≡ a (mod p) ;p prime, a integer

I Euler’s Theoremaφ(n) ≡ 1 (mod n); a co-prime nφ(n)(Euler function) counts the number of co-primes from 1 to n.

I Carmichael’s Theorem aλ(n) ≡ 1 (mod n)λ(n)(Carmichael’s function) gives the smallest integer m forwhich a(m) ≡ 1 (mod n)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Consequences

The order of any element a of a finite group (i.e. the smallest k forwhich ak = e divides G ) divides the order of G . Because the order ofa is the order of a cyclic subgroup generated by a.

I Fermat’s Little theoremap ≡ a (mod p) ;p prime, a integer

I Euler’s Theoremaφ(n) ≡ 1 (mod n); a co-prime nφ(n)(Euler function) counts the number of co-primes from 1 to n.

I Carmichael’s Theorem aλ(n) ≡ 1 (mod n)λ(n)(Carmichael’s function) gives the smallest integer m forwhich a(m) ≡ 1 (mod n)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fermat Little theorem proof by group theory

I Basic idea is to recognize G = {1, 2, ..., p − 1}, with theoperation of multiplication mod p as a Group. Some work toprove that every element is invertible

I Assume that a is an element of G and let k be its order. i.e.

I ak ≡ 1 (mod p)

I by Lagrange theorem, k divides the order of G , which is p− 1.So p− 1 = k ∗m then ap−1 = ak∗m = (ak)m = 1m = 1 mod p�

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fermat Little theorem proof by group theory

I Basic idea is to recognize G = {1, 2, ..., p − 1}, with theoperation of multiplication mod p as a Group. Some work toprove that every element is invertible

I Assume that a is an element of G and let k be its order. i.e.

I ak ≡ 1 (mod p)

I by Lagrange theorem, k divides the order of G , which is p− 1.So p− 1 = k ∗m then ap−1 = ak∗m = (ak)m = 1m = 1 mod p�

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fermat Little theorem proof by group theory

I Basic idea is to recognize G = {1, 2, ..., p − 1}, with theoperation of multiplication mod p as a Group. Some work toprove that every element is invertible

I Assume that a is an element of G and let k be its order. i.e.

I ak ≡ 1 (mod p)

I by Lagrange theorem, k divides the order of G , which is p− 1.So p− 1 = k ∗m then ap−1 = ak∗m = (ak)m = 1m = 1 mod p�

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fermat Little theorem proof by group theory

I Basic idea is to recognize G = {1, 2, ..., p − 1}, with theoperation of multiplication mod p as a Group. Some work toprove that every element is invertible

I Assume that a is an element of G and let k be its order. i.e.

I ak ≡ 1 (mod p)

I by Lagrange theorem, k divides the order of G , which is p− 1.So p− 1 = k ∗m then ap−1 = ak∗m = (ak)m = 1m = 1 mod p�

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fermat Little theorem proof by group theory

I Invertibility property.

I Assume b is co-prime (relative prime) to p. Using Bezoutidentity (a linear Diophantine equation)

I bx + py = 1 ( x , y integers)

I bx ≡ 1(modp) x is an inverse for b!

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Orbit counting Theorem

Aliases:Burnside’s LemmaBurnside’s counting theoremThe theorem that is not Burnside’sThe Cauchy-Frobenious lemma, so on.Number of orbits=average number of point fixed by the action ofelements of G.|X/G | = 1

|G |∑

g∈G |X g |

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Coloring

What is the number R of rotationally distinct colorings of a the facesof a cube using 3 colors?Let the X be the set of all 36 = 729 colored cubes. Two elements arein the same orbit precisely when one is a rotation (or composition ofrotations) of the other.

I 1 identity fix all 36 elements of X

I 6 90 degree face rotations fix 33

I 3 180 degree face rotations fix 34

I 8 120 degree vertex rotation fix 32

I 6 180 edge rotations fix 33

So N = 124(1 ∗ 36 + 6 ∗ 33 + 3 ∗ 34 + 8 ∗ 32 + 6 ∗ 33) = 57

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Coloring

What is the number R of rotationally distinct colorings of a the facesof a cube using 3 colors?Let the X be the set of all 36 = 729 colored cubes. Two elements arein the same orbit precisely when one is a rotation (or composition ofrotations) of the other.

I 1 identity fix all 36 elements of XI 6 90 degree face rotations fix 33

I 3 180 degree face rotations fix 34

I 8 120 degree vertex rotation fix 32

I 6 180 edge rotations fix 33

So N = 124(1 ∗ 36 + 6 ∗ 33 + 3 ∗ 34 + 8 ∗ 32 + 6 ∗ 33) = 57

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Coloring

What is the number R of rotationally distinct colorings of a the facesof a cube using 3 colors?Let the X be the set of all 36 = 729 colored cubes. Two elements arein the same orbit precisely when one is a rotation (or composition ofrotations) of the other.

I 1 identity fix all 36 elements of XI 6 90 degree face rotations fix 33

I 3 180 degree face rotations fix 34

I 8 120 degree vertex rotation fix 32

I 6 180 edge rotations fix 33

So N = 124(1 ∗ 36 + 6 ∗ 33 + 3 ∗ 34 + 8 ∗ 32 + 6 ∗ 33) = 57

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Coloring

What is the number R of rotationally distinct colorings of a the facesof a cube using 3 colors?Let the X be the set of all 36 = 729 colored cubes. Two elements arein the same orbit precisely when one is a rotation (or composition ofrotations) of the other.

I 1 identity fix all 36 elements of XI 6 90 degree face rotations fix 33

I 3 180 degree face rotations fix 34

I 8 120 degree vertex rotation fix 32

I 6 180 edge rotations fix 33

So N = 124(1 ∗ 36 + 6 ∗ 33 + 3 ∗ 34 + 8 ∗ 32 + 6 ∗ 33) = 57

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Coloring

What is the number R of rotationally distinct colorings of a the facesof a cube using 3 colors?Let the X be the set of all 36 = 729 colored cubes. Two elements arein the same orbit precisely when one is a rotation (or composition ofrotations) of the other.

I 1 identity fix all 36 elements of XI 6 90 degree face rotations fix 33

I 3 180 degree face rotations fix 34

I 8 120 degree vertex rotation fix 32

I 6 180 edge rotations fix 33

So N = 124(1 ∗ 36 + 6 ∗ 33 + 3 ∗ 34 + 8 ∗ 32 + 6 ∗ 33) = 57

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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orbit-stabilizer

g1

g2

gn

eGx

Gx = {g ! G|g.x = x}

x

g1

g2x

x

gn

g !" g.xh

x

one orbit

|G/Gx| = |G(x)|

G(x) = {g.x|g ! G}

G/N = {a.N |a ! G}

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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orbit-counting proof

Define the set of all fixed points by an element of G byX g = {x ∈ X |gx = x} and the set of all orbits byX/G = {G (x)|x ∈ X}.∑

g∈G

|X g | =∑g∈G

(∑

x :gx=x

1) =∑x∈X

(∑

g :gx=x

1)

=∑x∈X

|Gx | =∑x∈X

|G ||G (x)|

= |G |∑

ω∈X/G

∑x∈ω

(1

ω)

= |G ||X/G | |ω||ω|

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Structure and Symmetry

I The number of ways of coloring the elements of an n-elementset X with k colors is kn.

I A structural refinement is to have a graph Γ on the vertex setX and count proper coloring of Γ. The answer is a polynomialof degree n in k, the chromatic polynomialχ(Γ; k).

I A refinement involving symmetry is to have a group G ofpermutations of X , and to count colorings up to the action ofG . The answer is what we saw before, the orbit countingtheorem: a polynomial of degree n in k, with leadingcoefficient 1/|G |.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Structure and Symmetry

I The number of ways of coloring the elements of an n-elementset X with k colors is kn.

I A structural refinement is to have a graph Γ on the vertex setX and count proper coloring of Γ. The answer is a polynomialof degree n in k, the chromatic polynomialχ(Γ; k).

I A refinement involving symmetry is to have a group G ofpermutations of X , and to count colorings up to the action ofG . The answer is what we saw before, the orbit countingtheorem: a polynomial of degree n in k, with leadingcoefficient 1/|G |.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Structure and Symmetry

I The number of ways of coloring the elements of an n-elementset X with k colors is kn.

I A structural refinement is to have a graph Γ on the vertex setX and count proper coloring of Γ. The answer is a polynomialof degree n in k, the chromatic polynomialχ(Γ; k).

I A refinement involving symmetry is to have a group G ofpermutations of X , and to count colorings up to the action ofG . The answer is what we saw before, the orbit countingtheorem: a polynomial of degree n in k, with leadingcoefficient 1/|G |.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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I Combining the two approaches leads to counting the G-orbitsof “structurally restricted” where G is the group ofautomorphisms of the structure imposed on X. The answer isthe orbital chromatic polynomial of (Γ,G ). [P.J. Cameron, B.Jackson and Jason Rudd, 2006.]

I The Tutte polynomial (a generalization of the chromaticpolynomial) is are related to q-state Potts model partitionfunction in the Fortuin-Kastelyn representation. [J. J.Jacobsen, J. Salas, A. D. Sokal, 2005].

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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I Combining the two approaches leads to counting the G-orbitsof “structurally restricted” where G is the group ofautomorphisms of the structure imposed on X. The answer isthe orbital chromatic polynomial of (Γ,G ). [P.J. Cameron, B.Jackson and Jason Rudd, 2006.]

I The Tutte polynomial (a generalization of the chromaticpolynomial) is are related to q-state Potts model partitionfunction in the Fortuin-Kastelyn representation. [J. J.Jacobsen, J. Salas, A. D. Sokal, 2005].

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fortuin-Kastelyn representation

H(σ) = −∑

e=ij∈E

Jeδ(σi , σj)

Z =∑

σ

e−βH(σ)

ZG (q, v) ==∑

σ

∏e=ij∈E

{1 + veδ(σi , σj)}

where

ve = eβJe − 1

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fortuin-Kastelyn representation

ZG (q, v) = q|V |∑A⊆E

qc(Z)∏e∈A

ve

q

TG (x , y) =∑A⊆E

(x − 1)k(A)−k(G)(y − 1)c(A)

TG (x , y) = (x − 1)−k(G)(y − 1)|V |ZG ((x − 1)(y − 1), y − 1).

Why they do this? In order to study the limit q → 0, singularitiestells about phase change...

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Mallows Models

I Exact results

I Approximations (sampling)

I Generalization to partial rankings (Guy Lebanon, nips2007)

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Mallows Models

I Exact results

I Approximations (sampling)

I Generalization to partial rankings (Guy Lebanon, nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Mallows Models

I Exact results

I Approximations (sampling)

I Generalization to partial rankings (Guy Lebanon, nips2007)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Mallows-Type Models

idea

(1)thought

(2)play

(3)theory

(4)dream

(5)attention

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Distribution on permutations

Distance between permutations, Kendall’s tau:

d(π, σ) =n−1∑i=1

∑l>i

I (πσ−1(i)− πσ−1(l))

d(π, σ) = i(πσ−1) = i(κ)

Equivalent to the number of adjacent transpositions needed tobring π−1 to σ−1.

pσ(π) =1

Z (c)e−cd(π,σ)

Z (c) =∑π∈Gn

e−cd(π,σ)

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Evaluating Z (c)

For q > 0, [ see Stanley, 2000 ]

∑π∈Gn

qi(π) =n−1∑a1=0

n−2∑a2=0

...

0∑an=0

qa1+a2+...+an

= (n−1∑a1=0

qa1)(n−2∑a2=0

qa2)...(0∑

an=0

qan)

= (1 + q + ... + qn−1)...(1 + q + q2)(1 + q)1.

=n−1∏j=1

j∑k=0

qk

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Evaluating Z (c)

Z (c) =∑π∈Gn

e−ci(κ)

= (1 + e−c + ... + e−(n−1)c)...(1 + e−c + e−2c)(1 + e−c)

=n∏

j=1

1− e−jc

1− e−c

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Partial Ranks and Cosets

G1,1,2 = {e, (3, 4)} G1,...1,n!k = {! ! Gn|!(i) = i, i = 1...k}

G1,...,1,n!k! = {"!|" ! G1,..,1,n!k}

G1,1,2!

Set of permutations consistent with the ordering ! on the k top-ranked

One of the cosets of G1,...,1,n!k ! Gn, indexed by ! " Gn

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fourier transform on groups

P̂(ρ) =∑s∈G

P(s)ρ(s)

f̂ (w) =1√2π

∫ ∞

−∞f (x)e−iwxdx

Upper bound lemma: Let Q be a probability on the finite group G andU the uniform distribution:

|Q − U|2 ≤ 1

4

∑ρ

dρTr(Q̂(ρ)Q̂(ρ)†)

The metric here is the total variation distance, related to other metricssuch as Hellinger distance and Kullback-Leibler separation.

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Fastest mixing Markov Chain with symmetries

I Transitions are interpreted as convolutions.

I Upper bound lemma used to estimate convergence.

I Symmetry (automorphism group) is used to reduce thenumber of variables.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fastest mixing Markov Chain with symmetries

I Transitions are interpreted as convolutions.

I Upper bound lemma used to estimate convergence.

I Symmetry (automorphism group) is used to reduce thenumber of variables.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fastest mixing Markov Chain with symmetries

I Transitions are interpreted as convolutions.

I Upper bound lemma used to estimate convergence.

I Symmetry (automorphism group) is used to reduce thenumber of variables.

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Fastest mixing Markov Chain with symmetries

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Protein Structure ClassificationHarmonic Analysis on SE(3): Spherical Filters

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Protein Structure ClassificationHarmonic Analysis on SE(3): Spherical Filters

F(f ) = f̂ (p) =

∫SE(3)

f (g)U(g−1, p)d(g) (1)

f (g) = F−1(f̂ ) =1

2π2

∫SE(3)

trace(f̂ (p)U(g , p))p2dp (2)

(f1 ∗ f2)(g) =

∫SE(3)

f1(h)f2(h−1og)d(h) (3)

F(f1 ∗ f2) = F2F1 (4)

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Tools

I Gap

I Grape

I Mathematica, Matlab and Maple

I Snob++

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Thanks and take care!

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems

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Thanks and take care!

Dr. Marconi Barbosa NICTA/ANU, Canberra, Australia Aspects of Group Theory in Stochastic Problems