CHRISTOPHER M. P. TOMASZEWSKI 30 JULY MMIX A Spectral Analysis of Cyclic and Elementary Abelian Subgroup Lattices.

Slides:



Advertisements
Similar presentations
Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists.
Advertisements

4.1 Introduction to Matrices
CSNB143 – Discrete Structure
Chapter Matrices Matrix Arithmetic
8.3 Representing Relations Connection Matrices Let R be a relation from A = {a 1, a 2,..., a m } to B = {b 1, b 2,..., b n }. Definition: A n m  n connection.
Section 11 Direct Products and Finitely Generated Abelian Groups One purpose of this section is to show a way to use known groups as building blocks to.
Subgroup Lattices and their Chromatic Number Voula Collins Missouri State University REU Summer 2008.
Eigenvalues and Eigenvectors
Symmetric Matrices and Quadratic Forms
Chapter 2 Matrices Definition of a matrix.
Determinants King Saud University. The inverse of a 2 x 2 matrix Recall that earlier we noticed that for a 2x2 matrix,
Chapter 3 The Inverse. 3.1 Introduction Definition 1: The inverse of an n  n matrix A is an n  n matrix B having the property that AB = BA = I B is.
Matrix Definition A Matrix is an ordered set of numbers, variables or parameters. An example of a matrix can be represented by: The matrix is an ordered.
Arithmetic Operations on Matrices. 1. Definition of Matrix 2. Column, Row and Square Matrix 3. Addition and Subtraction of Matrices 4. Multiplying Row.
CE 311 K - Introduction to Computer Methods Daene C. McKinney
5.1 Orthogonality.
1 Statistical Analysis Professor Lynne Stokes Department of Statistical Science Lecture 5QF Introduction to Vector and Matrix Operations Needed for the.
Compiled By Raj G. Tiwari
GROUPS & THEIR REPRESENTATIONS: a card shuffling approach Wayne Lawton Department of Mathematics National University of Singapore S ,
Section 4.1 Using Matrices to Represent Data. Matrix Terminology A matrix is a rectangular array of numbers enclosed in a single set of brackets. The.
1 MAC 2103 Module 12 Eigenvalues and Eigenvectors.
Wei Wang Xi’an Jiaotong University Generalized Spectral Characterization of Graphs: Revisited Shanghai Conference on Algebraic Combinatorics (SCAC), Shanghai,
 Row and Reduced Row Echelon  Elementary Matrices.
Eigenvalues and Eigenvectors
Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Matrices and Vectors Objective.
WEEK 8 SYSTEMS OF EQUATIONS DETERMINANTS AND CRAMER’S RULE.
Matrices CHAPTER 8.1 ~ 8.8. Ch _2 Contents  8.1 Matrix Algebra 8.1 Matrix Algebra  8.2 Systems of Linear Algebra Equations 8.2 Systems of Linear.
Matrices. Definitions  A matrix is an m x n array of scalars, arranged conceptually as m rows and n columns.  m is referred to as the row dimension.
13.1 Matrices and Their Sums
Chapter 4 – Matrix CSNB 143 Discrete Mathematical Structures.
Domain Range definition: T is a linear transformation, EIGENVECTOR EIGENVALUE.
17. Group Theory 1.Introduction to Group Theory 2.Representation of Groups 3.Symmetry & Physics 4.Discrete Groups 5.Direct Products 6.Symmetric Groups.
Math 3121 Abstract Algebra I Lecture 9 Finish Section 10 Section 11.
The Hamiltonicity of Subgroup Graphs Immanuel McLaughlin Andrew Owens.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 07 Chapter 7: Eigenvalues, Eigenvectors.
Matrices Section 2.6. Section Summary Definition of a Matrix Matrix Arithmetic Transposes and Powers of Arithmetic Zero-One matrices.
Chapter 2 Determinants. With each square matrix it is possible to associate a real number called the determinant of the matrix. The value of this number.
5.1 Eigenvectors and Eigenvalues 5. Eigenvalues and Eigenvectors.
Graphs Basic properties.
Spectral Graph Theory and the Inverse Eigenvalue Problem of a Graph Leslie Hogben Department of Mathematics, Iowa State University, Ames, IA 50011
4.8 Rank Rank enables one to relate matrices to vectors, and vice versa. Definition Let A be an m  n matrix. The rows of A may be viewed as row vectors.
SECTION 2 BINARY OPERATIONS Definition: A binary operation  on a set S is a function mapping S X S into S. For each (a, b)  S X S, we will denote the.
Matrices. Matrix - a rectangular array of variables or constants in horizontal rows and vertical columns enclosed in brackets. Element - each value in.
1 Objective To provide background material in support of topics in Digital Image Processing that are based on matrices and/or vectors. Review Matrices.
Matrix Algebra Definitions Operations Matrix algebra is a means of making calculations upon arrays of numbers (or data). Most data sets are matrix-type.
Matrices. Variety of engineering problems lead to the need to solve systems of linear equations matrixcolumn vectors.
CS 285- Discrete Mathematics Lecture 11. Section 3.8 Matrices Introduction Matrix Arithmetic Transposes and Power of Matrices Zero – One Matrices Boolean.
A very brief introduction to Matrix (Section 2.7) Definitions Some properties Basic matrix operations Zero-One (Boolean) matrices.
2.1 Matrix Operations 2. Matrix Algebra. j -th column i -th row Diagonal entries Diagonal matrix : a square matrix whose nondiagonal entries are zero.
MTH108 Business Math I Lecture 20.
CS479/679 Pattern Recognition Dr. George Bebis
Elementary Linear Algebra Anton & Rorres, 9th Edition
Matrix Representation of Graphs
3.1 Introduction to Determinants
Eigenvalues and Eigenvectors
Combinatorial Spectral Theory of Nonnegative Matrices
Matrices and Vectors Review Objective
Matrix Representation of Graph
GROUPS & THEIR REPRESENTATIONS: a card shuffling approach
2. Matrix Algebra 2.1 Matrix Operations.
MATRICES MATRIX OPERATIONS.
Section 2.4 Matrices.
Objective To provide background material in support of topics in Digital Image Processing that are based on matrices and/or vectors.
Symmetric Matrices and Quadratic Forms
Note Please review 組合矩陣理論筆記(4) 1-3 about
Elementary Linear Algebra Anton & Rorres, 9th Edition
Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Symmetric Matrices and Quadratic Forms
Presentation transcript:

CHRISTOPHER M. P. TOMASZEWSKI 30 JULY MMIX A Spectral Analysis of Cyclic and Elementary Abelian Subgroup Lattices

Background Material Recall the definitions of:  Group  Graph  Subgroup Lattice – A graph where each subgroup is a vertex and two subgroups are connected iff one is a subgroup of the other without any intervening subgroups.  Direct Product  Adjacency Matrix – A square, symmetric matrix which represents a graph by placing a 1 in the i-th row and j-th column if i ~ j and a 0 elsewhere.  Characteristic Polynomial  Eigenvalue  Spectrum – The set of eigenvalues of the adjacency matrix of a graph

Background Material Recall: Group Subgroup graph Adjacency matrix Characteristic polynomial Eigenvalues For example:

Graph Products A graph product is a binary operation, denoted “□”, on graphs which induces a new graph over the Cartesian product of the graph vertices: where iff

Graph Products (Cont’d) For example: NB: When one of the graphs is a path graph, such as here, the graph product has the visual effect of “projecting” the other graph into the next dimension. = (2,1) (1,3) (2,3) (2,2) (1,2) □ (1,1)

Cyclic Groups A cyclic group is a group such that all elements of the group can be expressed as a power of a single element of the group, called a generator, of which there may be more than one in the group. Every cyclic group of order n is isomorphic to (essentially the same as) a group of the form {0, 1, 2, 3…n-1} under addition modulo n, denoted.

Cyclic Groups (Cont’d) Rewriting : where A group of the form is called a p-group.

Cyclic Groups (Cont’d) Theorem: That is, every cyclic group is the direct product of p- groups where the orders of the p-groups are the prime-power factors of the order of the cyclic group.

Graph Product – Direct Product Theorem: That is, the subgroup lattice of a direct product is equal to the graph product of the component subgroup lattices if the groups are coprime. Because the component p-groups of a cyclic group are always pairwise coprime, the subgroup lattice of every cyclic group is the graph product of all its component p-group subgroup lattices.

Graph Product – Direct Product (Cont’d) Because the subgroup lattice of every p-group is a path graph, this means that the subgroup lattice of every cyclic group is a simple cubic lattice in n dimensions, where n is the number of distinct p- groups which produce the cyclic group.

Graph Product – Adjacency Matrix Taking a graph product has a predictably regular effect on the adjacency matrix of the product graph: That is, the adjacency matrix of the product graph is a block matrix composed of one of the adjacency matrices along the diagonal and identities of equal dimension where the other matrix’ entries equal 1.

Tridiagonal Block Matrices Because p-group adjacency matrices are always tridiagonal, (non-zero entries are restricted to the diagonal, superdiagonal, and subdiagonal) the adjacency matrix of arbitrarily many p-groups is always a tridiagonal block matrix. Thus, every cyclic group adjacency matrix has a tridiagonal block form. There is a formula to give the determinant of such matrices. [Molinari, 2008]

Prime Result Theorem: Given any cyclic group,, its eigenvalues are That is, its spectrum is the Cartesian sum of the spectra of all its component p-groups. This settles the matter for all cyclic groups.

Elementary Abelian Groups An elementary Abelian group is a group of the form These are interesting because is a field, which means that is a finite vector space where subgroups correspond to subspaces. This allows us to more easily determine the subgroup lattice structure of these groups and also their spectra.

Elementary Abelian Groups (Cont’d) Elementary Abelian groups are also very interesting because they are the first case of the larger family of groups,, the carefully chosen subgroups of which, for distinct p, comprise all the coprime factors of any Abelian group. Results: This gives the spectrum for all groups of the form These matrices are also tridiagonal in general, so this is promising.

What’s Next Continue exploring block matrix methods in solving all elementary Abelian groups. Expand upon these methods to solve all Abelian groups. Solve dihedral groups and other semidirect products. What can be said about non-isomorphic groups with cospectral subgroup lattices? Spectra of subring lattices? And for something completely different: what groups have subgroup lattices of genus 1 (can be drawn on a torus without intersection)?