CHAPTER 2 -idempotent matrices

Size: px
Start display at page:

Download "CHAPTER 2 -idempotent matrices"

Transcription

1 CHAPTER 2 -idempotent matrices A -idempotent matrix is defined and some of its basic characterizations are derived (see [33]) in this chapter. It is shown that if is a -idempotent matrix then it is quadripotent (i.e.,. Necessary and sufficient condition for the sum of two -idempotent matrices to be -idempotent, is determined and then it is generalized for the sum of -idempotent matrices. A condition for the product of two -idempotent matrices to be -idempotent is also determined and then it is generalized for the product of -idempotent matrices. Relations between power hermitian matrices and -idempotent matrices are investigated (cf. [32]). It is proved that a -idempotent matrix reduces to an idempotent matrix when it commutes with the associated permutation matrix (i.e., ). 20

2 2.1 Characterizations of k-idempotent matrices A -idempotent matrix is defined and its characterizations are discussed in this section. Definition For a fixed product of disjoint transpositions, a matrix in C is said to be -idempotent if. This is equivalent to, where is the associated permutation matrix of. Example Then Here is a -idempotent matrix with. The associated permutation matrix is a matrix with ones on its southwest northeast diagonal and zeros everywhere else. That is, It can be easily verified that. Note In particular if then the associated permutation matrix reduces to identity matrix and -idempotent matrix reduces to idempotent matrix. 21

3 Remark implies that. The following relations can also be obtained which would be useful in computational aspects or or or Theorem Let be a -idempotent matrix. Then is -idempotent if and only if is idempotent.,which implies that is idempotent. Conversely, if is idempotent then commutes with the permutation matrix (cf. lemma 2.2.5) is -idempotent. Remark Consider. Then the associated permutation matrix. With reference to this, (i) is -idempotent as well as idempotent. 22

4 is -idempotent. (ii) is -idempotent but not idempotent. is not -idempotent. Theorem Let be a -idempotent matrix. Then (a) and (when it exists) are also -idempotent. (b) is -idempotent for all positive integers. (c) is quadripotent when is not an idempotent. Further, is non-singular then and. (d) is idempotent. (e) and are tripotent matrices. (a) A similar proof may be given for the remaining matrices. (b) - times (c) 23

5 Since, we have is quadripotent. is non-singular then and are immediate consequences of. (d) (e) The proof is similar for. Example made. Consider the matrix in (ii) of remark The following observations can be (i) is -idempotent. (ii) is also -idempotent. (iii) It can be seen that. (iv) Clearly, is idempotent. (v) and are tripotent matrices. Theorem is a -idempotent matrix then is a group under matrix multiplication. 24

6 Using the remark 2.1.4, the following matrix multiplication table can be found.. From the above table, we see that is closed under matrix multiplication. It is obvious that matrix multiplication is associative. We observe that acts as an identity element in. Besides, it is the only element in having this property. The inverse for each elements are given by, and is a group under matrix multiplication. Remark (i) then is a cyclic subgroup of.. (ii) is non-singular then by theorem (c), the group becomes. 25

7 2.2 Sums and Products of -idempotent matrices In this section, the sums and products of -idempotent matrices are discussed and some related results are obtained. Theorem Let and be two -idempotent matrices. Then is -idempotent if and only if. iff Generalization: Let be -idempotent matrices. Then is -idempotent if and only if for and in. Since s are -idempotent matrices, we have Here 26

8 is -idempotent then from,, it follows that. Conversely, if we assume that then from is -idempotent. Remark example, Theorem fails when we relax the condition that and anty commute. For Let and Clearly, and are -idempotent matrices. But and i.e.,. Also, is not a -idempotent matrix. 27

9 Theorem Let and be -idempotent matrices. then is also a -idempotent matrix. the matrix is -idempotent. Generalization: matrices then be -idempotent matrices belonging to a commuting family of is a -idempotent matrix. the matrix is -idempotent. Remark we relax the condition of commutability of matrices and in theorem then the product need not be k-idempotent. For example the matrices and in remark can be considered. It can be seen that. Also the product is not a -idempotent matrix. 28

10 denotes the commutator of the matrices and (see definition ), theorems and can be restated as and are two -idempotent matrices then is -idempotent if and only if. is -idempotent if. By theorem 1.2.1, the generalization of theorem can also be restated as For -idempotent matrices, if there is a unique matrix such that then the product is -idempotent. Lemma Let be a -idempotent matrix. Then is idempotent if and only if, where is the associated permutation matrix of. Assume that. Pre multiplying by, we have But is -idempotent is idempotent. By retracing the above steps the converse follows. Example is a -idempotent matrix and it also commutes with the associated permutation matrix, that is. We see that is idempotent. Lemma fails if we relax the condition of commutability of matrices and. Examples and are not idempotents. Note that in such cases. Theorem and are k-idempotent matrices then commutes with the permutation matrix. 29

11 Theorem Let and are two commuting k-idempotent matrices. The -idempotency of necessarily implies that it is a null matrix. For any two k-idempotent matrices and we have commutes with the permutation matrix by theorem is k-idempotent then by lemma 2.2.5, it reduces to an idempotent matrix. i.e., Since and are k-idempotent matrices, we have and by theorem (c). Substituting in, Since and are commuting -idempotent matrices, is also -idempotent by theorem by theorem (c) 30

12 Pre multiplying by, we have. It follows that. Remark For example the matrices and in remark can be considered..clearly the matrix commutes with the permutation matrix. It can be easily verified that is not a -idempotent matrix. 31

13 2.3 -idempotency of power hermitian matrices In this section, conditions for power hermitian matrices to be -idempotent are derived and some related results are given. Theorem Any two of the following imply the other one. C then (a) (b) (c) is -idempotent is -hermitian is square hermitian (a) and (b) (c): and. is square hermitian. (b) and (c) (a): Substituting in, We have. is -idempotent. (c) and (a) (b): Substituting in, We have. is -hermitian. Corollary Let be -hermitian -idempotent matrix. is non-singular then is unitary. Since is -hermitian -idempotent, is square hermitian by theorem is non-singular then by theorem (c) Therefore and hence is unitary. 32

14 Example Let (i) is -idempotent. (ii) is square hermitian. (iii) is -hermitian. (iv) is unitary Theorem Let be a -idempotent matrix. is cube hermitian then it reduces to an orthogonal projector. Since is a -idempotent matrix, we have by remark is cube hermitian then Pre and post multiplying by, we have by by Substituting in, we have. is an orthogonal projector. 33

15 Corollary Let be a -idempotent matrix. is -cube hermitian then it reduces to an orthogonal projector. is -cube hermitian then by remark reduces to cube hermitian matrix. By theorem 2.3.4, the matrix is an orthogonal projector. Note Let be a -idempotent matrix. then by theorem (c) we have (i.e., reduces to hermitian matrix ) Theorem Let be a -idempotent matrix. Then the following are equivalent. (i) (ii) (iii) is cube hermitian. is hermitian. is square hermitian. (i) (ii): is cube hermitian then by theorem (e) is hermitian. (ii) (iii): is hermitian then. is square hermitian. 34

16 (iii) (ii): is square hermitian then But is cube hermitian. Theorem Let be a -idempotent matrix. Then the necessary and sufficient condition for the matrix to be square hermitian is (i) (ii) is idempotent. Assume that is square hermitian. Pre and post multiplying by respectively, we have since and, we have is idempotent and substituting this in, we have. Conversely, assume that is idempotent with.. 35

17 is square hermitian. Corollary Let be a -idempotent matrix. Then the necessary and sufficient condition for to be -square hermitian is (i) (ii) is idempotent Assume that is -square hermitian. is square hermitian. By theorem 2.3.8, this is equivalent to is idempotent and Theorem Let be a -idempotent matrix. Then the following are equivalent. (i) (ii) (iii) (iv) is -cube hermitian is hermitian is -square hermitian is -hermitian 36

18 (i) (ii): is -cube hermitian then by theorem (e). is hermitian. (ii) (iii): is hermitian then (iii) (iv): is -square hermitian.. is -square hermitian then Pre and post multiplying by, we have is -hermitian.. (iv) (i): is -hermitian then But by theorem (e). is -cube hermitian. 37

19 Note The following theorem can be considered to be a converse of the above theorem Theorem is hermitian and -square hermitian then is -idempotent. Assume that and Combining the above two relations, we have. is -idempotent. 38

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. Adjoint operator and adjoint matrix Given a linear operator L on an inner product space V, the adjoint of L is a transformation

More information

SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear Algebra

SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear Algebra SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to 1.1. Introduction Linear algebra is a specific branch of mathematics dealing with the study of vectors, vector spaces with functions that

More information

Matrices. Chapter What is a Matrix? We review the basic matrix operations. An array of numbers a a 1n A = a m1...

Matrices. Chapter What is a Matrix? We review the basic matrix operations. An array of numbers a a 1n A = a m1... Chapter Matrices We review the basic matrix operations What is a Matrix? An array of numbers a a n A = a m a mn with m rows and n columns is a m n matrix Element a ij in located in position (i, j The elements

More information

A property of orthogonal projectors

A property of orthogonal projectors Linear Algebra and its Applications 354 (2002) 35 39 www.elsevier.com/locate/laa A property of orthogonal projectors Jerzy K. Baksalary a,, Oskar Maria Baksalary b,tomaszszulc c a Department of Mathematics,

More information

Equivalent Conditions on Conjugate Unitary Matrices

Equivalent Conditions on Conjugate Unitary Matrices International Journal of Mathematics Trends and Technology (IJMTT Volume 42 Number 1- February 2017 Equivalent Conditions on Conjugate Unitary Matrices A. Govindarasu #, S.Sassicala #1 # Department of

More information

SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear Algebra

SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear Algebra 1.1. Introduction SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 1 Introduction to Linear algebra is a specific branch of mathematics dealing with the study of vectors, vector spaces with functions that

More information

On pairs of generalized and hypergeneralized projections on a Hilbert space

On pairs of generalized and hypergeneralized projections on a Hilbert space On pairs of generalized and hypergeneralized projections on a Hilbert space Sonja Radosavljević and Dragan S Djordjević February 16 01 Abstract In this paper we characterize generalized and hypergeneralized

More information

REPRESENTATION THEORY. WEEK 4

REPRESENTATION THEORY. WEEK 4 REPRESENTATION THEORY. WEEK 4 VERA SERANOVA 1. uced modules Let B A be rings and M be a B-module. Then one can construct induced module A B M = A B M as the quotient of a free abelian group with generators

More information

Lecture 19: Isometries, Positive operators, Polar and singular value decompositions; Unitary matrices and classical groups; Previews (1)

Lecture 19: Isometries, Positive operators, Polar and singular value decompositions; Unitary matrices and classical groups; Previews (1) Lecture 19: Isometries, Positive operators, Polar and singular value decompositions; Unitary matrices and classical groups; Previews (1) Travis Schedler Thurs, Nov 18, 2010 (version: Wed, Nov 17, 2:15

More information

Linear Algebra using Dirac Notation: Pt. 2

Linear Algebra using Dirac Notation: Pt. 2 Linear Algebra using Dirac Notation: Pt. 2 PHYS 476Q - Southern Illinois University February 6, 2018 PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, 2018

More information

INTRODUCTION TO REPRESENTATION THEORY AND CHARACTERS

INTRODUCTION TO REPRESENTATION THEORY AND CHARACTERS INTRODUCTION TO REPRESENTATION THEORY AND CHARACTERS HANMING ZHANG Abstract. In this paper, we will first build up a background for representation theory. We will then discuss some interesting topics in

More information

Group representations

Group representations Group representations A representation of a group is specified by a set of hermitian matrices that obey: (the original set of NxN dimensional matrices for SU(N) or SO(N) corresponds to the fundamental

More information

Quantum Computing Lecture 2. Review of Linear Algebra

Quantum Computing Lecture 2. Review of Linear Algebra Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces

More information

Definition 2.3. We define addition and multiplication of matrices as follows.

Definition 2.3. We define addition and multiplication of matrices as follows. 14 Chapter 2 Matrices In this chapter, we review matrix algebra from Linear Algebra I, consider row and column operations on matrices, and define the rank of a matrix. Along the way prove that the row

More information

Quantum Mechanics- I Prof. Dr. S. Lakshmi Bala Department of Physics Indian Institute of Technology, Madras

Quantum Mechanics- I Prof. Dr. S. Lakshmi Bala Department of Physics Indian Institute of Technology, Madras Quantum Mechanics- I Prof. Dr. S. Lakshmi Bala Department of Physics Indian Institute of Technology, Madras Lecture - 6 Postulates of Quantum Mechanics II (Refer Slide Time: 00:07) In my last lecture,

More information

Algebraic Structures Exam File Fall 2013 Exam #1

Algebraic Structures Exam File Fall 2013 Exam #1 Algebraic Structures Exam File Fall 2013 Exam #1 1.) Find all four solutions to the equation x 4 + 16 = 0. Give your answers as complex numbers in standard form, a + bi. 2.) Do the following. a.) Write

More information

On Sums of Conjugate Secondary Range k-hermitian Matrices

On Sums of Conjugate Secondary Range k-hermitian Matrices Thai Journal of Mathematics Volume 10 (2012) Number 1 : 195 202 www.math.science.cmu.ac.th/thaijournal Online ISSN 1686-0209 On Sums of Conjugate Secondary Range k-hermitian Matrices S. Krishnamoorthy,

More information

0 Sets and Induction. Sets

0 Sets and Induction. Sets 0 Sets and Induction Sets A set is an unordered collection of objects, called elements or members of the set. A set is said to contain its elements. We write a A to denote that a is an element of the set

More information

Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane.

Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane. Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 2018 8 Lecture 8 8.1 Matrices July 22, 2018 We shall study

More information

Ep Matrices and Its Weighted Generalized Inverse

Ep Matrices and Its Weighted Generalized Inverse Vol.2, Issue.5, Sep-Oct. 2012 pp-3850-3856 ISSN: 2249-6645 ABSTRACT: If A is a con s S.Krishnamoorthy 1 and B.K.N.MuthugobaI 2 Research Scholar Ramanujan Research Centre, Department of Mathematics, Govt.

More information

Math 3121, A Summary of Sections 0,1,2,4,5,6,7,8,9

Math 3121, A Summary of Sections 0,1,2,4,5,6,7,8,9 Math 3121, A Summary of Sections 0,1,2,4,5,6,7,8,9 Section 0. Sets and Relations Subset of a set, B A, B A (Definition 0.1). Cartesian product of sets A B ( Defintion 0.4). Relation (Defintion 0.7). Function,

More information

1 Linear Algebra Problems

1 Linear Algebra Problems Linear Algebra Problems. Let A be the conjugate transpose of the complex matrix A; i.e., A = A t : A is said to be Hermitian if A = A; real symmetric if A is real and A t = A; skew-hermitian if A = A and

More information

18.702: Quiz 1 Solutions

18.702: Quiz 1 Solutions MIT MATHEMATICS 18.702: Quiz 1 Solutions February 28 2018 There are four problems on this quiz worth equal value. You may quote without proof any result stated in class or in the assigned reading, unless

More information

MA441: Algebraic Structures I. Lecture 18

MA441: Algebraic Structures I. Lecture 18 MA441: Algebraic Structures I Lecture 18 5 November 2003 1 Review from Lecture 17: Theorem 6.5: Aut(Z/nZ) U(n) For every positive integer n, Aut(Z/nZ) is isomorphic to U(n). The proof used the map T :

More information

Mathematics. EC / EE / IN / ME / CE. for

Mathematics.   EC / EE / IN / ME / CE. for Mathematics for EC / EE / IN / ME / CE By www.thegateacademy.com Syllabus Syllabus for Mathematics Linear Algebra: Matrix Algebra, Systems of Linear Equations, Eigenvalues and Eigenvectors. Probability

More information

Kevin James. MTHSC 412 Section 3.4 Cyclic Groups

Kevin James. MTHSC 412 Section 3.4 Cyclic Groups MTHSC 412 Section 3.4 Cyclic Groups Definition If G is a cyclic group and G =< a > then a is a generator of G. Definition If G is a cyclic group and G =< a > then a is a generator of G. Example 1 Z is

More information

2 Garrett: `A Good Spectral Theorem' 1. von Neumann algebras, density theorem The commutant of a subring S of a ring R is S 0 = fr 2 R : rs = sr; 8s 2

2 Garrett: `A Good Spectral Theorem' 1. von Neumann algebras, density theorem The commutant of a subring S of a ring R is S 0 = fr 2 R : rs = sr; 8s 2 1 A Good Spectral Theorem c1996, Paul Garrett, garrett@math.umn.edu version February 12, 1996 1 Measurable Hilbert bundles Measurable Banach bundles Direct integrals of Hilbert spaces Trivializing Hilbert

More information

Homework 1 Elena Davidson (B) (C) (D) (E) (F) (G) (H) (I)

Homework 1 Elena Davidson (B) (C) (D) (E) (F) (G) (H) (I) CS 106 Spring 2004 Homework 1 Elena Davidson 8 April 2004 Problem 1.1 Let B be a 4 4 matrix to which we apply the following operations: 1. double column 1, 2. halve row 3, 3. add row 3 to row 1, 4. interchange

More information

Tripotents: a class of strongly clean elements in rings

Tripotents: a class of strongly clean elements in rings DOI: 0.2478/auom-208-0003 An. Şt. Univ. Ovidius Constanţa Vol. 26(),208, 69 80 Tripotents: a class of strongly clean elements in rings Grigore Călugăreanu Abstract Periodic elements in a ring generate

More information

Representation theory

Representation theory Representation theory Dr. Stuart Martin 2. Chapter 2: The Okounkov-Vershik approach These guys are Andrei Okounkov and Anatoly Vershik. The two papers appeared in 96 and 05. Here are the main steps: branching

More information

Lecture 7 Cyclic groups and subgroups

Lecture 7 Cyclic groups and subgroups Lecture 7 Cyclic groups and subgroups Review Types of groups we know Numbers: Z, Q, R, C, Q, R, C Matrices: (M n (F ), +), GL n (F ), where F = Q, R, or C. Modular groups: Z/nZ and (Z/nZ) Dihedral groups:

More information

0.2 Vector spaces. J.A.Beachy 1

0.2 Vector spaces. J.A.Beachy 1 J.A.Beachy 1 0.2 Vector spaces I m going to begin this section at a rather basic level, giving the definitions of a field and of a vector space in much that same detail as you would have met them in a

More information

Range Symmetric Matrices in Indefinite Inner Product Space

Range Symmetric Matrices in Indefinite Inner Product Space Intern. J. Fuzzy Mathematical Archive Vol. 5, No. 2, 2014, 49-56 ISSN: 2320 3242 (P), 2320 3250 (online) Published on 20 December 2014 www.researchmathsci.org International Journal of Range Symmetric Matrices

More information

Abstract Algebra, HW6 Solutions. Chapter 5

Abstract Algebra, HW6 Solutions. Chapter 5 Abstract Algebra, HW6 Solutions Chapter 5 6 We note that lcm(3,5)15 So, we need to come up with two disjoint cycles of lengths 3 and 5 The obvious choices are (13) and (45678) So if we consider the element

More information

Phys 201. Matrices and Determinants

Phys 201. Matrices and Determinants Phys 201 Matrices and Determinants 1 1.1 Matrices 1.2 Operations of matrices 1.3 Types of matrices 1.4 Properties of matrices 1.5 Determinants 1.6 Inverse of a 3 3 matrix 2 1.1 Matrices A 2 3 7 =! " 1

More information

MATH 223A NOTES 2011 LIE ALGEBRAS 35

MATH 223A NOTES 2011 LIE ALGEBRAS 35 MATH 3A NOTES 011 LIE ALGEBRAS 35 9. Abstract root systems We now attempt to reconstruct the Lie algebra based only on the information given by the set of roots Φ which is embedded in Euclidean space E.

More information

5.6. PSEUDOINVERSES 101. A H w.

5.6. PSEUDOINVERSES 101. A H w. 5.6. PSEUDOINVERSES 0 Corollary 5.6.4. If A is a matrix such that A H A is invertible, then the least-squares solution to Av = w is v = A H A ) A H w. The matrix A H A ) A H is the left inverse of A and

More information

On some linear combinations of hypergeneralized projectors

On some linear combinations of hypergeneralized projectors Linear Algebra and its Applications 413 (2006) 264 273 www.elsevier.com/locate/laa On some linear combinations of hypergeneralized projectors Jerzy K. Baksalary a, Oskar Maria Baksalary b,, Jürgen Groß

More information

Matrix Factorization and Analysis

Matrix Factorization and Analysis Chapter 7 Matrix Factorization and Analysis Matrix factorizations are an important part of the practice and analysis of signal processing. They are at the heart of many signal-processing algorithms. Their

More information

Introduction to Association Schemes

Introduction to Association Schemes Introduction to Association Schemes Akihiro Munemasa Tohoku University June 5 6, 24 Algebraic Combinatorics Summer School, Sendai Assumed results (i) Vandermonde determinant: a a m =. a m a m m i

More information

Basic Definitions: Group, subgroup, order of a group, order of an element, Abelian, center, centralizer, identity, inverse, closed.

Basic Definitions: Group, subgroup, order of a group, order of an element, Abelian, center, centralizer, identity, inverse, closed. Math 546 Review Exam 2 NOTE: An (*) at the end of a line indicates that you will not be asked for the proof of that specific item on the exam But you should still understand the idea and be able to apply

More information

A Sudoku Submatrix Study

A Sudoku Submatrix Study A Sudoku Submatrix Study Merciadri Luca LucaMerciadri@studentulgacbe Abstract In our last article ([1]), we gave some properties of Sudoku matrices We here investigate some properties of the Sudoku submatrices

More information

文件中找不不到关系 ID 为 rid3 的图像部件. Review of LINEAR ALGEBRA I. TA: Yujia Xie CSE 6740 Computational Data Analysis. Georgia Institute of Technology

文件中找不不到关系 ID 为 rid3 的图像部件. Review of LINEAR ALGEBRA I. TA: Yujia Xie CSE 6740 Computational Data Analysis. Georgia Institute of Technology 文件中找不不到关系 ID 为 rid3 的图像部件 Review of LINEAR ALGEBRA I TA: Yujia Xie CSE 6740 Computational Data Analysis Georgia Institute of Technology 1. Notations A R $ & : a matrix with m rows and n columns, where

More information

Groups and Symmetries

Groups and Symmetries Groups and Symmetries Definition: Symmetry A symmetry of a shape is a rigid motion that takes vertices to vertices, edges to edges. Note: A rigid motion preserves angles and distances. Definition: Group

More information

RING ELEMENTS AS SUMS OF UNITS

RING ELEMENTS AS SUMS OF UNITS 1 RING ELEMENTS AS SUMS OF UNITS CHARLES LANSKI AND ATTILA MARÓTI Abstract. In an Artinian ring R every element of R can be expressed as the sum of two units if and only if R/J(R) does not contain a summand

More information

Vector Space Concepts

Vector Space Concepts Vector Space Concepts ECE 174 Introduction to Linear & Nonlinear Optimization Ken Kreutz-Delgado ECE Department, UC San Diego Ken Kreutz-Delgado (UC San Diego) ECE 174 Fall 2016 1 / 25 Vector Space Theory

More information

Assignment 3. A tutorial on the applications of discrete groups.

Assignment 3. A tutorial on the applications of discrete groups. Assignment 3 Given January 16, Due January 3, 015. A tutorial on the applications of discrete groups. Consider the group C 3v which is the cyclic group with three elements, C 3, augmented by a reflection

More information

Symmetric and anti symmetric matrices

Symmetric and anti symmetric matrices Symmetric and anti symmetric matrices In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally, matrix A is symmetric if. A = A Because equal matrices have equal

More information

Solutions to Assignment 3

Solutions to Assignment 3 Solutions to Assignment 3 Question 1. [Exercises 3.1 # 2] Let R = {0 e b c} with addition multiplication defined by the following tables. Assume associativity distributivity show that R is a ring with

More information

This operation is - associative A + (B + C) = (A + B) + C; - commutative A + B = B + A; - has a neutral element O + A = A, here O is the null matrix

This operation is - associative A + (B + C) = (A + B) + C; - commutative A + B = B + A; - has a neutral element O + A = A, here O is the null matrix 1 Matrix Algebra Reading [SB] 81-85, pp 153-180 11 Matrix Operations 1 Addition a 11 a 12 a 1n a 21 a 22 a 2n a m1 a m2 a mn + b 11 b 12 b 1n b 21 b 22 b 2n b m1 b m2 b mn a 11 + b 11 a 12 + b 12 a 1n

More information

Gaussian automorphisms whose ergodic self-joinings are Gaussian

Gaussian automorphisms whose ergodic self-joinings are Gaussian F U N D A M E N T A MATHEMATICAE 164 (2000) Gaussian automorphisms whose ergodic self-joinings are Gaussian by M. L e m a ńc z y k (Toruń), F. P a r r e a u (Paris) and J.-P. T h o u v e n o t (Paris)

More information

Singular Value Decomposition (SVD) and Polar Form

Singular Value Decomposition (SVD) and Polar Form Chapter 2 Singular Value Decomposition (SVD) and Polar Form 2.1 Polar Form In this chapter, we assume that we are dealing with a real Euclidean space E. Let f: E E be any linear map. In general, it may

More information

6 Permutations Very little of this section comes from PJE.

6 Permutations Very little of this section comes from PJE. 6 Permutations Very little of this section comes from PJE Definition A permutation (p147 of a set A is a bijection ρ : A A Notation If A = {a b c } and ρ is a permutation on A we can express the action

More information

Chapter 5 Groups of permutations (bijections) Basic notation and ideas We study the most general type of groups - groups of permutations

Chapter 5 Groups of permutations (bijections) Basic notation and ideas We study the most general type of groups - groups of permutations Chapter 5 Groups of permutations (bijections) Basic notation and ideas We study the most general type of groups - groups of permutations (bijections). Definition A bijection from a set A to itself is also

More information

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations

Definitions. Notations. Injective, Surjective and Bijective. Divides. Cartesian Product. Relations. Equivalence Relations Page 1 Definitions Tuesday, May 8, 2018 12:23 AM Notations " " means "equals, by definition" the set of all real numbers the set of integers Denote a function from a set to a set by Denote the image of

More information

GENERATING SETS KEITH CONRAD

GENERATING SETS KEITH CONRAD GENERATING SETS KEITH CONRAD 1 Introduction In R n, every vector can be written as a unique linear combination of the standard basis e 1,, e n A notion weaker than a basis is a spanning set: a set of vectors

More information

Section I.6. Symmetric, Alternating, and Dihedral Groups

Section I.6. Symmetric, Alternating, and Dihedral Groups I.6. Symmetric, alternating, and Dihedral Groups 1 Section I.6. Symmetric, Alternating, and Dihedral Groups Note. In this section, we conclude our survey of the group theoretic topics which are covered

More information

The Cyclic Decomposition of a Nilpotent Operator

The Cyclic Decomposition of a Nilpotent Operator The Cyclic Decomposition of a Nilpotent Operator 1 Introduction. J.H. Shapiro Suppose T is a linear transformation on a vector space V. Recall Exercise #3 of Chapter 8 of our text, which we restate here

More information

Lecture 8 : Eigenvalues and Eigenvectors

Lecture 8 : Eigenvalues and Eigenvectors CPS290: Algorithmic Foundations of Data Science February 24, 2017 Lecture 8 : Eigenvalues and Eigenvectors Lecturer: Kamesh Munagala Scribe: Kamesh Munagala Hermitian Matrices It is simpler to begin with

More information

Singular Value Decomposition

Singular Value Decomposition Chapter 5 Singular Value Decomposition We now reach an important Chapter in this course concerned with the Singular Value Decomposition of a matrix A. SVD, as it is commonly referred to, is one of the

More information

1. Groups Definitions

1. Groups Definitions 1. Groups Definitions 1 1. Groups Definitions A group is a set S of elements between which there is defined a binary operation, usually called multiplication. For the moment, the operation will be denoted

More information

Symmetries, Groups, and Conservation Laws

Symmetries, Groups, and Conservation Laws Chapter Symmetries, Groups, and Conservation Laws The dynamical properties and interactions of a system of particles and fields are derived from the principle of least action, where the action is a 4-dimensional

More information

Lecture 10: A (Brief) Introduction to Group Theory (See Chapter 3.13 in Boas, 3rd Edition)

Lecture 10: A (Brief) Introduction to Group Theory (See Chapter 3.13 in Boas, 3rd Edition) Lecture 0: A (Brief) Introduction to Group heory (See Chapter 3.3 in Boas, 3rd Edition) Having gained some new experience with matrices, which provide us with representations of groups, and because symmetries

More information

Index. Banach space 630 Basic Jordan block 378, 420

Index. Banach space 630 Basic Jordan block 378, 420 Index Absolute convergence 710 Absolute value 15, 20 Accumulation point 622, 690, 700 Adjoint classsical 192 of a linear operator 493, 673 of a matrix 183, 384 Algebra 227 Algebraic number 16 Algebraically

More information

Groups Subgroups Normal subgroups Quotient groups Homomorphisms Cyclic groups Permutation groups Cayley s theorem Class equations Sylow theorems

Groups Subgroups Normal subgroups Quotient groups Homomorphisms Cyclic groups Permutation groups Cayley s theorem Class equations Sylow theorems Group Theory Groups Subgroups Normal subgroups Quotient groups Homomorphisms Cyclic groups Permutation groups Cayley s theorem Class equations Sylow theorems Groups Definition : A non-empty set ( G,*)

More information

ALGEBRA I (LECTURE NOTES 2017/2018) LECTURE 9 - CYCLIC GROUPS AND EULER S FUNCTION

ALGEBRA I (LECTURE NOTES 2017/2018) LECTURE 9 - CYCLIC GROUPS AND EULER S FUNCTION ALGEBRA I (LECTURE NOTES 2017/2018) LECTURE 9 - CYCLIC GROUPS AND EULER S FUNCTION PAVEL RŮŽIČKA 9.1. Congruence modulo n. Let us have a closer look at a particular example of a congruence relation on

More information

Stab(t) = {h G h t = t} = {h G h (g s) = g s} = {h G (g 1 hg) s = s} = g{k G k s = s} g 1 = g Stab(s)g 1.

Stab(t) = {h G h t = t} = {h G h (g s) = g s} = {h G (g 1 hg) s = s} = g{k G k s = s} g 1 = g Stab(s)g 1. 1. Group Theory II In this section we consider groups operating on sets. This is not particularly new. For example, the permutation group S n acts on the subset N n = {1, 2,...,n} of N. Also the group

More information

Algebraic Methods in Combinatorics

Algebraic Methods in Combinatorics Algebraic Methods in Combinatorics Po-Shen Loh 27 June 2008 1 Warm-up 1. (A result of Bourbaki on finite geometries, from Răzvan) Let X be a finite set, and let F be a family of distinct proper subsets

More information

ECE 275A Homework # 3 Due Thursday 10/27/2016

ECE 275A Homework # 3 Due Thursday 10/27/2016 ECE 275A Homework # 3 Due Thursday 10/27/2016 Reading: In addition to the lecture material presented in class, students are to read and study the following: A. The material in Section 4.11 of Moon & Stirling

More information

Problem set 2. Math 212b February 8, 2001 due Feb. 27

Problem set 2. Math 212b February 8, 2001 due Feb. 27 Problem set 2 Math 212b February 8, 2001 due Feb. 27 Contents 1 The L 2 Euler operator 1 2 Symplectic vector spaces. 2 2.1 Special kinds of subspaces....................... 3 2.2 Normal forms..............................

More information

Foundations of Matrix Analysis

Foundations of Matrix Analysis 1 Foundations of Matrix Analysis In this chapter we recall the basic elements of linear algebra which will be employed in the remainder of the text For most of the proofs as well as for the details, the

More information

4 Group representations

4 Group representations Physics 9b Lecture 6 Caltech, /4/9 4 Group representations 4. Examples Example : D represented as real matrices. ( ( D(e =, D(c = ( ( D(b =, D(b =, D(c = Example : Circle group as rotation of D real vector

More information

CLASSICAL GROUPS DAVID VOGAN

CLASSICAL GROUPS DAVID VOGAN CLASSICAL GROUPS DAVID VOGAN 1. Orthogonal groups These notes are about classical groups. That term is used in various ways by various people; I ll try to say a little about that as I go along. Basically

More information

Vector spaces. EE 387, Notes 8, Handout #12

Vector spaces. EE 387, Notes 8, Handout #12 Vector spaces EE 387, Notes 8, Handout #12 A vector space V of vectors over a field F of scalars is a set with a binary operator + on V and a scalar-vector product satisfying these axioms: 1. (V, +) is

More information

Problem Set (T) If A is an m n matrix, B is an n p matrix and D is a p s matrix, then show

Problem Set (T) If A is an m n matrix, B is an n p matrix and D is a p s matrix, then show MTH 0: Linear Algebra Department of Mathematics and Statistics Indian Institute of Technology - Kanpur Problem Set Problems marked (T) are for discussions in Tutorial sessions (T) If A is an m n matrix,

More information

A group G is a set of discrete elements a, b, x alongwith a group operator 1, which we will denote by, with the following properties:

A group G is a set of discrete elements a, b, x alongwith a group operator 1, which we will denote by, with the following properties: 1 Why Should We Study Group Theory? Group theory can be developed, and was developed, as an abstract mathematical topic. However, we are not mathematicians. We plan to use group theory only as much as

More information

Exercises on chapter 1

Exercises on chapter 1 Exercises on chapter 1 1. Let G be a group and H and K be subgroups. Let HK = {hk h H, k K}. (i) Prove that HK is a subgroup of G if and only if HK = KH. (ii) If either H or K is a normal subgroup of G

More information

1.1 Definition. A monoid is a set M together with a map. 1.3 Definition. A monoid is commutative if x y = y x for all x, y M.

1.1 Definition. A monoid is a set M together with a map. 1.3 Definition. A monoid is commutative if x y = y x for all x, y M. 1 Monoids and groups 1.1 Definition. A monoid is a set M together with a map M M M, (x, y) x y such that (i) (x y) z = x (y z) x, y, z M (associativity); (ii) e M such that x e = e x = x for all x M (e

More information

Properties of Matrices and Operations on Matrices

Properties of Matrices and Operations on Matrices Properties of Matrices and Operations on Matrices A common data structure for statistical analysis is a rectangular array or matris. Rows represent individual observational units, or just observations,

More information

GROUP THEORY AND THE 2 2 RUBIK S CUBE

GROUP THEORY AND THE 2 2 RUBIK S CUBE GROUP THEORY AND THE 2 2 RUBIK S CUBE VICTOR SNAITH Abstract. This essay was motivated by my grandson Giulio being given one of these toys as a present. If I have not made errors the moves described here,

More information

Secondary κ-kernel Symmetric Fuzzy Matrices

Secondary κ-kernel Symmetric Fuzzy Matrices Intern. J. Fuzzy Mathematical rchive Vol. 5, No. 2, 24, 89-94 ISSN: 232 3242 (P), 232 325 (online) Published on 2 December 24 www.researchmathsci.org International Journal of Secondary κ-ernel Symmetric

More information

5 Group theory. 5.1 Binary operations

5 Group theory. 5.1 Binary operations 5 Group theory This section is an introduction to abstract algebra. This is a very useful and important subject for those of you who will continue to study pure mathematics. 5.1 Binary operations 5.1.1

More information

CHAPTEER - TWO SUBGROUPS. ( Z, + ) is subgroup of ( R, + ). 1) Find all subgroups of the group ( Z 8, + 8 ).

CHAPTEER - TWO SUBGROUPS. ( Z, + ) is subgroup of ( R, + ). 1) Find all subgroups of the group ( Z 8, + 8 ). CHAPTEER - TWO SUBGROUPS Definition 2-1. Let (G, ) be a group and H G be a nonempty subset of G. The pair ( H, ) is said to be a SUBGROUP of (G, ) if ( H, ) is group. Example. ( Z, + ) is subgroup of (

More information

is an isomorphism, and V = U W. Proof. Let u 1,..., u m be a basis of U, and add linearly independent

is an isomorphism, and V = U W. Proof. Let u 1,..., u m be a basis of U, and add linearly independent Lecture 4. G-Modules PCMI Summer 2015 Undergraduate Lectures on Flag Varieties Lecture 4. The categories of G-modules, mostly for finite groups, and a recipe for finding every irreducible G-module of a

More information

Introduction to Group Theory

Introduction to Group Theory Chapter 10 Introduction to Group Theory Since symmetries described by groups play such an important role in modern physics, we will take a little time to introduce the basic structure (as seen by a physicist)

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 1982 NOTES ON MATRIX METHODS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 1982 NOTES ON MATRIX METHODS MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 198 NOTES ON MATRIX METHODS 1. Matrix Algebra Margenau and Murphy, The Mathematics of Physics and Chemistry, Chapter 10, give almost

More information

MATHEMATICS 217 NOTES

MATHEMATICS 217 NOTES MATHEMATICS 27 NOTES PART I THE JORDAN CANONICAL FORM The characteristic polynomial of an n n matrix A is the polynomial χ A (λ) = det(λi A), a monic polynomial of degree n; a monic polynomial in the variable

More information

Review of some mathematical tools

Review of some mathematical tools MATHEMATICAL FOUNDATIONS OF SIGNAL PROCESSING Fall 2016 Benjamín Béjar Haro, Mihailo Kolundžija, Reza Parhizkar, Adam Scholefield Teaching assistants: Golnoosh Elhami, Hanjie Pan Review of some mathematical

More information

Chapter 5. Modular arithmetic. 5.1 The modular ring

Chapter 5. Modular arithmetic. 5.1 The modular ring Chapter 5 Modular arithmetic 5.1 The modular ring Definition 5.1. Suppose n N and x, y Z. Then we say that x, y are equivalent modulo n, and we write x y mod n if n x y. It is evident that equivalence

More information

Math Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88

Math Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88 Math Camp 2010 Lecture 4: Linear Algebra Xiao Yu Wang MIT Aug 2010 Xiao Yu Wang (MIT) Math Camp 2010 08/10 1 / 88 Linear Algebra Game Plan Vector Spaces Linear Transformations and Matrices Determinant

More information

Algebra I Fall 2007

Algebra I Fall 2007 MIT OpenCourseWare http://ocw.mit.edu 18.701 Algebra I Fall 007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.701 007 Geometry of the Special Unitary

More information

Basic Quantum Mechanics Prof. Ajoy Ghatak Department of Physics Indian Institute of Technology, Delhi

Basic Quantum Mechanics Prof. Ajoy Ghatak Department of Physics Indian Institute of Technology, Delhi Basic Quantum Mechanics Prof. Ajoy Ghatak Department of Physics Indian Institute of Technology, Delhi Module No. # 07 Bra-Ket Algebra and Linear Harmonic Oscillator - II Lecture No. # 01 Dirac s Bra and

More information

Algebraic Methods in Combinatorics

Algebraic Methods in Combinatorics Algebraic Methods in Combinatorics Po-Shen Loh June 2009 1 Linear independence These problems both appeared in a course of Benny Sudakov at Princeton, but the links to Olympiad problems are due to Yufei

More information

(d) Since we can think of isometries of a regular 2n-gon as invertible linear operators on R 2, we get a 2-dimensional representation of G for

(d) Since we can think of isometries of a regular 2n-gon as invertible linear operators on R 2, we get a 2-dimensional representation of G for Solutions to Homework #7 0. Prove that [S n, S n ] = A n for every n 2 (where A n is the alternating group). Solution: Since [f, g] = f 1 g 1 fg is an even permutation for all f, g S n and since A n is

More information

ECE 275A Homework #3 Solutions

ECE 275A Homework #3 Solutions ECE 75A Homework #3 Solutions. Proof of (a). Obviously Ax = 0 y, Ax = 0 for all y. To show sufficiency, note that if y, Ax = 0 for all y, then it must certainly be true for the particular value of y =

More information

The Hurewicz Theorem

The Hurewicz Theorem The Hurewicz Theorem April 5, 011 1 Introduction The fundamental group and homology groups both give extremely useful information, particularly about path-connected spaces. Both can be considered as functors,

More information

Consistent Histories. Chapter Chain Operators and Weights

Consistent Histories. Chapter Chain Operators and Weights Chapter 10 Consistent Histories 10.1 Chain Operators and Weights The previous chapter showed how the Born rule can be used to assign probabilities to a sample space of histories based upon an initial state

More information

On the Moore-Penrose and the Drazin inverse of two projections on Hilbert space

On the Moore-Penrose and the Drazin inverse of two projections on Hilbert space On the Moore-Penrose and the Drazin inverse of two projections on Hilbert space Sonja Radosavljević and Dragan SDjordjević March 13, 2012 Abstract For two given orthogonal, generalized or hypergeneralized

More information

SECTIONS 5.2/5.4 BASIC PROPERTIES OF EIGENVALUES AND EIGENVECTORS / SIMILARITY TRANSFORMATIONS

SECTIONS 5.2/5.4 BASIC PROPERTIES OF EIGENVALUES AND EIGENVECTORS / SIMILARITY TRANSFORMATIONS SECINS 5/54 BSIC PRPERIES F EIGENVUES ND EIGENVECRS / SIMIRIY RNSFRMINS Eigenvalues of an n : there exists a vector x for which x = x Such a vector x is called an eigenvector, and (, x) is called an eigenpair

More information

REPRESENTATION THEORY WEEK 5. B : V V k

REPRESENTATION THEORY WEEK 5. B : V V k REPRESENTATION THEORY WEEK 5 1. Invariant forms Recall that a bilinear form on a vector space V is a map satisfying B : V V k B (cv, dw) = cdb (v, w), B (v 1 + v, w) = B (v 1, w)+b (v, w), B (v, w 1 +

More information