24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS

Size: px
Start display at page:

Download "24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS"

Transcription

1 24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS Corollary Suppose that the semisimple decompositio of the G- module V is V = i S i. The i = χ V,χ i Proof. Sice χ V W = χ V + χ W, we have: χ V = j χ j. So, χ V,χ i = j χ j,χ i = i proof of the mai theorem. The theorem will follow from three lemmas. The first lemma calculates the dimesio of the fixed poit set of V. Defiitio If V is a G-module the the fixed poit set of the actio of G is give by V G := {v V σv = v σ G} Lemma The dimesio of the fixed poit set is equal to the average value of the correspodig character: dim C V G = 1 χ V (σ) Proof. The projectio map is give by σ G π : V V G π(v) = 1 σv It is clear that (1) π(v) V G sice multiplicatio by ay τ G will just permute the summads. (2) π(v) =v if v V G because, i that case, each σv = v ad there are terms. Therefore, π is a projectio map, i.e., a liear retractio oto V G. Lookig at the formula we see that π is multiplicatio by the idempotet e 1 = 1 σ G σ. (This is the idempotet correspodig to the trivial represetatio.) So: dim V G = Tr(π) =χ V (e 1 )=χ V ( 1 Explaatios: ) σ = 1 χ V (σ) σ G σ G

2 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS 25 (1) dim V G = Tr(π) because V = V G W (W = ker π). So, the matrix of π is: ( ) 1V G 0 π = 0 0 W makig Tr(π) = Tr(1 V G) = dim C V G. (2) Tr(π) =χ V (e 1 ) by defiitio of the character: χ V (e 1 ) := Tr(e 1 : V V ) This is the trace of the mappig V V give by multiplicatio by e 1. But we are callig that mappig π. Lemma If V, W are represetatios of G the Hom G (V, W ) = Hom C (V, W ) G where G acts o Hom C (V, W ) by cojugatio, i.e., σf = σ f σ 1 which meas that (σf)(v) =σf(σ 1 v) Proof. This is trivial. Give ay liear map f : V W, f is a G- homomorphism iff σ f = f σ σ f σ 1 = f σf = f iff f Hom C (V, W ) G. Lemma Hom C (V, W ) = V W as G-modules. Proof. Let φ : V W Hom C (V, W ) be give by φ(f w)(v) =f(v)w To check that this is a G-homomorphism we eed to show that φσ = σφ for ay σ G. So, we compute both sides: which seds v V to φσ(f w) =φ(σf σw) =φ(f σ 1 σw) O the other side we have: which also seds v V to φ(f σ 1 σw)(v) =f(σ 1 v)σw σφ(f w) =σ φ(f w) σ 1 σ φ(f w) σ 1 v = σ(f(σ 1 v)w) =f(σ 1 v)σw This shows that φ commutes with the actio of G. The fact that φ is a isomorphism is well-kow: If v i,v i form a basis-dual basis pair for V ad w j form a basis for W the v j w i form a basis for V W ad φ(v j w i ):v = a j v j v j (v)w i = a j w i

3 26 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS is the mappig whose matrix has ij-etry equal to 1 ad all other etries 0. So, these homomorphisms form a basis for Hom C (V, W ) ad φ is a isomorphism. Proof of mai theorem Usig the three lemmas we get: dim C Hom G (V, W )= 2.33 dim C Hom C (V, W ) G = 2.34 dim C (V W ) G 1 = 2.32 χ V W (σ) σ G = 1 χ V (σ)χ W (σ) σ = 1 χ V (σ 1 )χ W (σ) = χ V,χ W σ character table of S 4. Usig these formulas we ca calculate the character table for S 4. First ote that there are five cojugacy classes represeted by 1, (12), (123), (12)(34), (1234) The elemets of cycle form (12)(34) form (with 1) a ormal subgroup K = {1, (12)(34), (13)(24), (14)(23)} S 4 called the Klei 4-group. The quotiet S 4 /K is isomorphic to the symmetric group o 3 letters. Imitatig the case of D 4, this allows us to costruct the followig portio of the character table for S 4 : c j (12) (123) (12)(34) (1234) χ χ χ χ 4 3 χ 5 3 Explaatios: (1) Sice (12)(34) K, the value of the first three characters o this cojugacy class is d i, the same as i the first colum. (2) Sice (1234)K = (12)K, these two colums have the same values of χ 1,χ 2,χ 3.

4 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS 27 (3) Fially, the two ukow characters χ 4,χ 5 must be 3-dimesioal sice 24 = d 2 i = d d 2 5 has oly oe solutio: d 4 = d 5 = 3. To figure out the ukow characters we eed aother represetatio. The permutatio represetatio P is the 4-dimesioal represetatio of S 4 i which the elemets of S 4 act by permutig the uit coordiate vectors. For example ρ P (12) = Note that the trace of ρ P (σ) is equal to the umber of letters left fixed by σ. So, χ P takes values 4, 2, 1, 0, 0 as show: c j (12) (123) (12)(34) (1234) χ χ χ χ P χ V = χ P χ The represetatio P cotais oe copy of the trivial represetatio ad o copies of the other two: χ P,χ 1 = 1 (4 + 6(2) + 8(1)) = 1 24 χ P,χ 2 = 1 (4 + 6( 1)(2) + 8(1)(1)) = 0 24 χ P,χ 3 = 1 ((2)(4) + 8( 1)(1)) = 0 24 So, P = S 1 V where V is a 3-dimesioal module which does ot cotai S 1,S 2 or S 3. So, V = S 4 ms 5. But S 4,S 5 are both 3- dimesioal. So, V = S 4 (or S 5 ). Usig the fact that χ 1 + χ 2 +2χ 3 +3χ 4 +3χ 5 = χ reg

5 28 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS we ca ow complete the character table of S 4 : c j (12) (123) (12)(34) (1234) χ χ χ χ χ From the character table of S 4 we ca fid all ormal subgroups. First, the kerels of the 5 irreducible represetatios are: (1) ker ρ 1 = S 4. (2) ker ρ 2 = A 4 cotaiig the cojugacy classes of 1, (123), (12)(34). (3) ker ρ 3 = K cotaiig 1, (12)(34) ad cojugates. (4) ker ρ 4 = 1. I.e., ρ 4 is a faithful represetatio. (5) ker ρ 5 = 1. So, ρ 5 is also faithful. Sice these subgroups cotai each other: 1 < K < A 4 <S 4 itersectig them will ot give ay other subgroups. So, these are the oly ormal subgroups of S 4.

6 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS colum orthogoality. The colums of the character table also satisfy a orthogoality coditio. To see it we first have to write the row orthogoality coditio χ i,χ j = ad write it i matrix form: T b k=1 c k χ i(c k )χ j (c k )=δ ij c c b T t = I b where T is the character table T =(χ i (c j )). This equatio shows that the character table T is a ivertible matrix with iverse T 1 = DT t where D is the diagoal matrix with diagoal etries c i. Multiplyig both sides of this equatio o the right by T ad o the left with D 1 ad we get: T t T = D 1 = c c b Lookig at the etries of these matrices we get the colum orthogoality relatio: Theorem If σ, τ G the b χ i (σ)χ i (τ) = i=1 { c if σ, τ are cojugate 0 if ot Here c is the umber of cojugates of σ i G. (So, / c is the order of the cetralizer C(σ) ={τ G στ = τσ} of σ.) Corollary The character table T =(χ i (c j )) determies the size of each cojugacy class c j. Proof. Takig σ = τ i the above theorem we get C(σ) = i χ i (σ) 2 The size of the cojugacy class c of σ is the idex of its cetralizer: c = G : C(σ) = / C(σ).

7 30 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS As a example, look at the character table for S 3 : 1 (12) (123) χ χ χ Colum orthogoality meas that the usual Hermitia dot product of the colums is zero. For example, the dot product of the first ad third colum is (1)(1) + (1)(1) + (2)( 1) = 0 Also the dot product of the jth vector with itself (its legth squared) is equal to / c j. For example, the legth squared of the third colum vector is = 3 Makig the umber of cojugates of (123) equal to 6/3 = 2.

8 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS Iductio If H is a subgroup of G the ay represetatio of G will restrict to a represetatio of H by compositio: H G ρ Aut C (V ) Iductio is a more complicated process which goes the other way: It starts with a represetatio of H ad produces a represetatio of G. Followig Lag, I will costruct the same object i several differet ways startig with a elemetary equatio for the iduced character iduced characters. Defiitio 3.1. Suppose that H G (H is a subgroup of G) ad χ : H C is a character (or ay class fuctio). The the iduced character Id G H χ : G C is the class fuctio o G defied by Id G H χ(σ) = 1 χ(τστ 1 ) H where χ(σ) = 0 if σ/ H. τ G The mai theorem about the iduced character is the followig. Theorem 3.2. If V is ay represetatio of H the there exists a represetatio W of G so that χ W = Id G H χ V Furthermore, W is uique up to isomorphism. The represetatio W is writte W = Id G H V ad is called the iduced represetatio. We will study that tomorrow. Before provig this theorem let me give two examples example 1. Here is a trivial observatio. Propositio 3.3. If G is abelia the Id G H χ(σ) = G : H χ(σ) Now suppose that G = Z/4 ={1, σ, σ 2,σ 3 } ad H = {1,τ} with τ = σ 2. The the character table of H = Z/2 is H = Z/2 1 τ χ χ 1 1

A brief introduction to linear algebra

A brief introduction to linear algebra CHAPTER 6 A brief itroductio to liear algebra 1. Vector spaces ad liear maps I what follows, fix K 2{Q, R, C}. More geerally, K ca be ay field. 1.1. Vector spaces. Motivated by our ituitio of addig ad

More information

Chimica Inorganica 3

Chimica Inorganica 3 himica Iorgaica Irreducible Represetatios ad haracter Tables Rather tha usig geometrical operatios, it is ofte much more coveiet to employ a ew set of group elemets which are matrices ad to make the rule

More information

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK)

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK) LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK) Everythig marked by is ot required by the course syllabus I this lecture, all vector spaces is over the real umber R. All vectors i R is viewed as a colum

More information

MA 162B LECTURE NOTES: THURSDAY, JANUARY 15

MA 162B LECTURE NOTES: THURSDAY, JANUARY 15 MA 6B LECTURE NOTES: THURSDAY, JANUARY 5 Examples of Galois Represetatios: Complex Represetatios Regular Represetatio Cosider a complex represetatio ρ : Gal ( Q/Q ) GL d (C) with fiite image If we deote

More information

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

Inverse Matrix. A meaning that matrix B is an inverse of matrix A. Iverse Matrix Two square matrices A ad B of dimesios are called iverses to oe aother if the followig holds, AB BA I (11) The otio is dual but we ofte write 1 B A meaig that matrix B is a iverse of matrix

More information

i is the prime factorization of n as a product of powers of distinct primes, then: i=1 pm i

i is the prime factorization of n as a product of powers of distinct primes, then: i=1 pm i Lecture 3. Group Actios PCMI Summer 2015 Udergraduate Lectures o Flag Varieties Lecture 3. The category of groups is discussed, ad the importat otio of a group actio is explored. Defiitio 3.1. A group

More information

Theorem: Let A n n. In this case that A does reduce to I, we search for A 1 as the solution matrix X to the matrix equation A X = I i.e.

Theorem: Let A n n. In this case that A does reduce to I, we search for A 1 as the solution matrix X to the matrix equation A X = I i.e. Theorem: Let A be a square matrix The A has a iverse matrix if ad oly if its reduced row echelo form is the idetity I this case the algorithm illustrated o the previous page will always yield the iverse

More information

Solutions to Example Sheet 3

Solutions to Example Sheet 3 Solutios to Example Sheet 3 1. Let G = A 5. For each pair of irreducible represetatios ρ, ρ, (i decompose ρ ρ ito a sum of irreducible represetatios, (ii decompose 2 ρ ad S 2 ρ ito irreducible represetatios,

More information

MATH10212 Linear Algebra B Proof Problems

MATH10212 Linear Algebra B Proof Problems MATH22 Liear Algebra Proof Problems 5 Jue 26 Each problem requests a proof of a simple statemet Problems placed lower i the list may use the results of previous oes Matrices ermiats If a b R the matrix

More information

Modern Algebra. Previous year Questions from 2017 to Ramanasri

Modern Algebra. Previous year Questions from 2017 to Ramanasri Moder Algebra Previous year Questios from 017 to 199 Ramaasri 017 S H O P NO- 4, 1 S T F L O O R, N E A R R A P I D F L O U R M I L L S, O L D R A J E N D E R N A G A R, N E W D E L H I. W E B S I T E

More information

Lecture 8: October 20, Applications of SVD: least squares approximation

Lecture 8: October 20, Applications of SVD: least squares approximation Mathematical Toolkit Autum 2016 Lecturer: Madhur Tulsiai Lecture 8: October 20, 2016 1 Applicatios of SVD: least squares approximatio We discuss aother applicatio of sigular value decompositio (SVD) of

More information

CHAPTER I: Vector Spaces

CHAPTER I: Vector Spaces CHAPTER I: Vector Spaces Sectio 1: Itroductio ad Examples This first chapter is largely a review of topics you probably saw i your liear algebra course. So why cover it? (1) Not everyoe remembers everythig

More information

, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx)

, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx) Cosider the differetial equatio y '' k y 0 has particular solutios y1 si( kx) ad y cos( kx) I geeral, ay liear combiatio of y1 ad y, cy 1 1 cy where c1, c is also a solutio to the equatio above The reaso

More information

Chapter Vectors

Chapter Vectors Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it

More information

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a Math E-2b Lecture #8 Notes This week is all about determiats. We ll discuss how to defie them, how to calculate them, lear the allimportat property kow as multiliearity, ad show that a square matrix A

More information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j. Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α

More information

The Discrete Fourier Transform

The Discrete Fourier Transform The Discrete Fourier Trasform Complex Fourier Series Represetatio Recall that a Fourier series has the form a 0 + a k cos(kt) + k=1 b k si(kt) This represetatio seems a bit awkward, sice it ivolves two

More information

Assignment 2 Solutions SOLUTION. ϕ 1 Â = 3 ϕ 1 4i ϕ 2. The other case can be dealt with in a similar way. { ϕ 2 Â} χ = { 4i ϕ 1 3 ϕ 2 } χ.

Assignment 2 Solutions SOLUTION. ϕ 1  = 3 ϕ 1 4i ϕ 2. The other case can be dealt with in a similar way. { ϕ 2 Â} χ = { 4i ϕ 1 3 ϕ 2 } χ. PHYSICS 34 QUANTUM PHYSICS II (25) Assigmet 2 Solutios 1. With respect to a pair of orthoormal vectors ϕ 1 ad ϕ 2 that spa the Hilbert space H of a certai system, the operator  is defied by its actio

More information

Algebra of Least Squares

Algebra of Least Squares October 19, 2018 Algebra of Least Squares Geometry of Least Squares Recall that out data is like a table [Y X] where Y collects observatios o the depedet variable Y ad X collects observatios o the k-dimesioal

More information

ON THE EXISTENCE OF A GROUP ORTHONORMAL BASIS. Peter Zizler (Received 20 June, 2014)

ON THE EXISTENCE OF A GROUP ORTHONORMAL BASIS. Peter Zizler (Received 20 June, 2014) NEW ZEALAND JOURNAL OF MATHEMATICS Volume 45 (2015, 45-52 ON THE EXISTENCE OF A GROUP ORTHONORMAL BASIS Peter Zizler (Received 20 Jue, 2014 Abstract. Let G be a fiite group ad let l 2 (G be a fiite dimesioal

More information

Lecture 4: Grassmannians, Finite and Affine Morphisms

Lecture 4: Grassmannians, Finite and Affine Morphisms 18.725 Algebraic Geometry I Lecture 4 Lecture 4: Grassmaias, Fiite ad Affie Morphisms Remarks o last time 1. Last time, we proved the Noether ormalizatio lemma: If A is a fiitely geerated k-algebra, the,

More information

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a Math S-b Lecture # Notes This wee is all about determiats We ll discuss how to defie them, how to calculate them, lear the allimportat property ow as multiliearity, ad show that a square matrix A is ivertible

More information

MATH 205 HOMEWORK #2 OFFICIAL SOLUTION. (f + g)(x) = f(x) + g(x) = f( x) g( x) = (f + g)( x)

MATH 205 HOMEWORK #2 OFFICIAL SOLUTION. (f + g)(x) = f(x) + g(x) = f( x) g( x) = (f + g)( x) MATH 205 HOMEWORK #2 OFFICIAL SOLUTION Problem 2: Do problems 7-9 o page 40 of Hoffma & Kuze. (7) We will prove this by cotradictio. Suppose that W 1 is ot cotaied i W 2 ad W 2 is ot cotaied i W 1. The

More information

Chapter 2. Periodic points of toral. automorphisms. 2.1 General introduction

Chapter 2. Periodic points of toral. automorphisms. 2.1 General introduction Chapter 2 Periodic poits of toral automorphisms 2.1 Geeral itroductio The automorphisms of the two-dimesioal torus are rich mathematical objects possessig iterestig geometric, algebraic, topological ad

More information

Linearly Independent Sets, Bases. Review. Remarks. A set of vectors,,, in a vector space is said to be linearly independent if the vector equation

Linearly Independent Sets, Bases. Review. Remarks. A set of vectors,,, in a vector space is said to be linearly independent if the vector equation Liearly Idepedet Sets Bases p p c c p Review { v v vp} A set of vectors i a vector space is said to be liearly idepedet if the vector equatio cv + c v + + c has oly the trivial solutio = = { v v vp} The

More information

Linear Transformations

Linear Transformations Liear rasformatios 6. Itroductio to Liear rasformatios 6. he Kerel ad Rage of a Liear rasformatio 6. Matrices for Liear rasformatios 6.4 rasitio Matrices ad Similarity 6.5 Applicatios of Liear rasformatios

More information

Recurrence Relations

Recurrence Relations Recurrece Relatios Aalysis of recursive algorithms, such as: it factorial (it ) { if (==0) retur ; else retur ( * factorial(-)); } Let t be the umber of multiplicatios eeded to calculate factorial(). The

More information

(VII.A) Review of Orthogonality

(VII.A) Review of Orthogonality VII.A Review of Orthogoality At the begiig of our study of liear trasformatios i we briefly discussed projectios, rotatios ad projectios. I III.A, projectios were treated i the abstract ad without regard

More information

Symmetric Matrices and Quadratic Forms

Symmetric Matrices and Quadratic Forms 7 Symmetric Matrices ad Quadratic Forms 7.1 DIAGONALIZAION OF SYMMERIC MARICES SYMMERIC MARIX A symmetric matrix is a matrix A such that. A = A Such a matrix is ecessarily square. Its mai diagoal etries

More information

Physics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001.

Physics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001. Physics 324, Fall 2002 Dirac Notatio These otes were produced by David Kapla for Phys. 324 i Autum 2001. 1 Vectors 1.1 Ier product Recall from liear algebra: we ca represet a vector V as a colum vector;

More information

Lemma Let f(x) K[x] be a separable polynomial of degree n. Then the Galois group is a subgroup of S n, the permutations of the roots.

Lemma Let f(x) K[x] be a separable polynomial of degree n. Then the Galois group is a subgroup of S n, the permutations of the roots. 15 Cubics, Quartics ad Polygos It is iterestig to chase through the argumets of 14 ad see how this affects solvig polyomial equatios i specific examples We make a global assumptio that the characteristic

More information

A Note On The Exponential Of A Matrix Whose Elements Are All 1

A Note On The Exponential Of A Matrix Whose Elements Are All 1 Applied Mathematics E-Notes, 8(208), 92-99 c ISSN 607-250 Available free at mirror sites of http://wwwmaththuedutw/ ame/ A Note O The Expoetial Of A Matrix Whose Elemets Are All Reza Farhadia Received

More information

Mon Feb matrix inverses. Announcements: Warm-up Exercise:

Mon Feb matrix inverses. Announcements: Warm-up Exercise: Math 225-4 Week 6 otes We will ot ecessarily fiish the material from a give day's otes o that day We may also add or subtract some material as the week progresses, but these otes represet a i-depth outlie

More information

PAPER : IIT-JAM 2010

PAPER : IIT-JAM 2010 MATHEMATICS-MA (CODE A) Q.-Q.5: Oly oe optio is correct for each questio. Each questio carries (+6) marks for correct aswer ad ( ) marks for icorrect aswer.. Which of the followig coditios does NOT esure

More information

CALCULATION OF FIBONACCI VECTORS

CALCULATION OF FIBONACCI VECTORS CALCULATION OF FIBONACCI VECTORS Stuart D. Aderso Departmet of Physics, Ithaca College 953 Daby Road, Ithaca NY 14850, USA email: saderso@ithaca.edu ad Dai Novak Departmet of Mathematics, Ithaca College

More information

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors 5 Eigevalues ad Eigevectors 5.3 DIAGONALIZATION DIAGONALIZATION Example 1: Let. Fid a formula for A k, give that P 1 1 = 1 2 ad, where Solutio: The stadard formula for the iverse of a 2 2 matrix yields

More information

Singular value decomposition. Mathématiques appliquées (MATH0504-1) B. Dewals, Ch. Geuzaine

Singular value decomposition. Mathématiques appliquées (MATH0504-1) B. Dewals, Ch. Geuzaine Lecture 11 Sigular value decompositio Mathématiques appliquées (MATH0504-1) B. Dewals, Ch. Geuzaie V1.2 07/12/2018 1 Sigular value decompositio (SVD) at a glace Motivatio: the image of the uit sphere S

More information

Efficient GMM LECTURE 12 GMM II

Efficient GMM LECTURE 12 GMM II DECEMBER 1 010 LECTURE 1 II Efficiet The estimator depeds o the choice of the weight matrix A. The efficiet estimator is the oe that has the smallest asymptotic variace amog all estimators defied by differet

More information

Review Problems 1. ICME and MS&E Refresher Course September 19, 2011 B = C = AB = A = A 2 = A 3... C 2 = C 3 = =

Review Problems 1. ICME and MS&E Refresher Course September 19, 2011 B = C = AB = A = A 2 = A 3... C 2 = C 3 = = Review Problems ICME ad MS&E Refresher Course September 9, 0 Warm-up problems. For the followig matrices A = 0 B = C = AB = 0 fid all powers A,A 3,(which is A times A),... ad B,B 3,... ad C,C 3,... Solutio:

More information

A Note on the Symmetric Powers of the Standard Representation of S n

A Note on the Symmetric Powers of the Standard Representation of S n A Note o the Symmetric Powers of the Stadard Represetatio of S David Savitt 1 Departmet of Mathematics, Harvard Uiversity Cambridge, MA 0138, USA dsavitt@mathharvardedu Richard P Staley Departmet of Mathematics,

More information

Matrix Algebra from a Statistician s Perspective BIOS 524/ Scalar multiple: ka

Matrix Algebra from a Statistician s Perspective BIOS 524/ Scalar multiple: ka Matrix Algebra from a Statisticia s Perspective BIOS 524/546. Matrices... Basic Termiology a a A = ( aij ) deotes a m matrix of values. Whe =, this is a am a m colum vector. Whe m= this is a row vector..2.

More information

Matrix Algebra 2.3 CHARACTERIZATIONS OF INVERTIBLE MATRICES Pearson Education, Inc.

Matrix Algebra 2.3 CHARACTERIZATIONS OF INVERTIBLE MATRICES Pearson Education, Inc. 2 Matrix Algebra 2.3 CHARACTERIZATIONS OF INVERTIBLE MATRICES 2012 Pearso Educatio, Ic. Theorem 8: Let A be a square matrix. The the followig statemets are equivalet. That is, for a give A, the statemets

More information

PROPERTIES OF AN EULER SQUARE

PROPERTIES OF AN EULER SQUARE PROPERTIES OF N EULER SQURE bout 0 the mathematicia Leoard Euler discussed the properties a x array of letters or itegers ow kow as a Euler or Graeco-Lati Square Such squares have the property that every

More information

R is a scalar defined as follows:

R is a scalar defined as follows: Math 8. Notes o Dot Product, Cross Product, Plaes, Area, ad Volumes This lecture focuses primarily o the dot product ad its may applicatios, especially i the measuremet of agles ad scalar projectio ad

More information

U8L1: Sec Equations of Lines in R 2

U8L1: Sec Equations of Lines in R 2 MCVU Thursda Ma, Review of Equatios of a Straight Lie (-D) U8L Sec. 8.9. Equatios of Lies i R Cosider the lie passig through A (-,) with slope, as show i the diagram below. I poit slope form, the equatio

More information

THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS

THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS DEMETRES CHRISTOFIDES Abstract. Cosider a ivertible matrix over some field. The Gauss-Jorda elimiatio reduces this matrix to the idetity

More information

CALCULATING FIBONACCI VECTORS

CALCULATING FIBONACCI VECTORS THE GENERALIZED BINET FORMULA FOR CALCULATING FIBONACCI VECTORS Stuart D Aderso Departmet of Physics, Ithaca College 953 Daby Road, Ithaca NY 14850, USA email: saderso@ithacaedu ad Dai Novak Departmet

More information

THE CHAIN CONDITION OF MODULE MATRIX

THE CHAIN CONDITION OF MODULE MATRIX Jural Karya Asli Loreka Ahli atematik Vol 9 No (206) Page 00-00 Jural Karya Asli Loreka Ahli atematik THE CHAIN CONDITION OF ODULE ATRIX Achmad Abdurrazzaq Ismail bi ohd 2 ad Ahmad Kadri bi Juoh Uiversiti

More information

4 The Sperner property.

4 The Sperner property. 4 The Sperer property. I this sectio we cosider a surprisig applicatio of certai adjacecy matrices to some problems i extremal set theory. A importat role will also be played by fiite groups. I geeral,

More information

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT OCTOBER 7, 2016 LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT Geometry of LS We ca thik of y ad the colums of X as members of the -dimesioal Euclidea space R Oe ca

More information

(I.C) Matrix algebra

(I.C) Matrix algebra (IC) Matrix algebra Before formalizig Gauss-Jorda i terms of a fixed procedure for row-reducig A, we briefly review some properties of matrix multiplicatio Let m{ [A ij ], { [B jk ] p, p{ [C kl ] q be

More information

5 Birkhoff s Ergodic Theorem

5 Birkhoff s Ergodic Theorem 5 Birkhoff s Ergodic Theorem Amog the most useful of the various geeralizatios of KolmogorovâĂŹs strog law of large umbers are the ergodic theorems of Birkhoff ad Kigma, which exted the validity of the

More information

Name: MAT 444 Test 2 Instructor: Helene Barcelo March 1, 2004

Name: MAT 444 Test 2 Instructor: Helene Barcelo March 1, 2004 MAT Test 2 Istructor: Helee Barcelo March, 200 Name: You ca take up to 2 hours for completig this exam. Close book, otes ad calculator. Do ot use your ow scratch paper. Write each solutio i the space provided,

More information

Math Solutions to homework 6

Math Solutions to homework 6 Math 175 - Solutios to homework 6 Cédric De Groote November 16, 2017 Problem 1 (8.11 i the book): Let K be a compact Hermitia operator o a Hilbert space H ad let the kerel of K be {0}. Show that there

More information

Name of the Student:

Name of the Student: SUBJECT NAME : Discrete Mathematics SUBJECT CODE : MA 65 MATERIAL NAME : Part A questios MATERIAL CODE : JM08AM1013 REGULATION : R008 UPDATED ON : May-Jue 016 (Sca the above Q.R code for the direct dowload

More information

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d Liear regressio Daiel Hsu (COMS 477) Maximum likelihood estimatio Oe of the simplest liear regressio models is the followig: (X, Y ),..., (X, Y ), (X, Y ) are iid radom pairs takig values i R d R, ad Y

More information

arxiv: v1 [math.fa] 3 Apr 2016

arxiv: v1 [math.fa] 3 Apr 2016 Aticommutator Norm Formula for Proectio Operators arxiv:164.699v1 math.fa] 3 Apr 16 Sam Walters Uiversity of Norther British Columbia ABSTRACT. We prove that for ay two proectio operators f, g o Hilbert

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chapter 04.04 Uary atrix Operatios After readig this chapter, you should be able to:. kow what uary operatios meas, 2. fid the traspose of a square matrix ad it s relatioship to symmetric matrices,. fid

More information

Character rigidity for lattices and commensurators I after Creutz-Peterson

Character rigidity for lattices and commensurators I after Creutz-Peterson Character rigidity for lattices ad commesurators I after Creutz-Peterso Talk C3 for the Arbeitsgemeischaft o Superridigity held i MFO Oberwolfach, 31st March - 4th April 2014 1 Sve Raum 1 Itroductio The

More information

Matrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc.

Matrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc. 2 Matrix Algebra 2.2 THE INVERSE OF A MATRIX MATRIX OPERATIONS A matrix A is said to be ivertible if there is a matrix C such that CA = I ad AC = I where, the idetity matrix. I = I I this case, C is a

More information

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian?

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian? NBHM QUESTION 7 NBHM QUESTION 7 NBHM QUESTION 7 Sectio : Algebra Q Let G be a group of order Which of the followig coditios imply that G is abelia? 5 36 Q Which of the followig subgroups are ecesarily

More information

Symmetrization maps and differential operators. 1. Symmetrization maps

Symmetrization maps and differential operators. 1. Symmetrization maps July 26, 2011 Symmetrizatio maps ad differetial operators Paul Garrett garrett@math.um.edu http://www.math.um.edu/ garrett/ The symmetrizatio map s : Sg Ug is a liear surjectio from the symmetric algebra

More information

LinearAlgebra DMTH502

LinearAlgebra DMTH502 LiearAlgebra DMTH50 LINEAR ALGEBRA Copyright 0 J D Aad All rights reserved Produced & Prited by EXCEL BOOKS PRIVATE LIMITED A-45, Naraia, Phase-I, New Delhi-008 for Lovely Professioal Uiversity Phagwara

More information

denote the set of all polynomials of the form p=ax 2 +bx+c. For example, . Given any two polynomials p= ax 2 +bx+c and q= a'x 2 +b'x+c',

denote the set of all polynomials of the form p=ax 2 +bx+c. For example, . Given any two polynomials p= ax 2 +bx+c and q= a'x 2 +b'x+c', Chapter Geeral Vector Spaces Real Vector Spaces Example () Let u ad v be vectors i R ad k a scalar ( a real umber), the we ca defie additio: u+v, scalar multiplicatio: ku, kv () Let P deote the set of

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Machine Learning for Data Science (CS 4786)

Machine Learning for Data Science (CS 4786) Machie Learig for Data Sciece CS 4786) Lecture & 3: Pricipal Compoet Aalysis The text i black outlies high level ideas. The text i blue provides simple mathematical details to derive or get to the algorithm

More information

Course : Algebraic Combinatorics

Course : Algebraic Combinatorics Course 18.312: Algebraic Combiatorics Lecture Notes # 18-19 Addedum by Gregg Musier March 18th - 20th, 2009 The followig material ca be foud i a umber of sources, icludig Sectios 7.3 7.5, 7.7, 7.10 7.11,

More information

Math 61CM - Solutions to homework 3

Math 61CM - Solutions to homework 3 Math 6CM - Solutios to homework 3 Cédric De Groote October 2 th, 208 Problem : Let F be a field, m 0 a fixed oegative iteger ad let V = {a 0 + a x + + a m x m a 0,, a m F} be the vector space cosistig

More information

U8L1: Sec Equations of Lines in R 2

U8L1: Sec Equations of Lines in R 2 MCVU U8L: Sec. 8.9. Equatios of Lies i R Review of Equatios of a Straight Lie (-D) Cosider the lie passig through A (-,) with slope, as show i the diagram below. I poit slope form, the equatio of the lie

More information

ALGEBRA HW 11 CLAY SHONKWILER

ALGEBRA HW 11 CLAY SHONKWILER ALGEBRA HW 11 CLAY SHONKWILER 1 Let V, W, Y be fiite dimesioal vector spaces over K. (a: Show that there are atural isomorphisms (V W V W Hom(V, W Hom(W, V. Proof. (V W V W : Defie the map φ : V W (V W

More information

2 Geometric interpretation of complex numbers

2 Geometric interpretation of complex numbers 2 Geometric iterpretatio of complex umbers 2.1 Defiitio I will start fially with a precise defiitio, assumig that such mathematical object as vector space R 2 is well familiar to the studets. Recall that

More information

Math Homotopy Theory Spring 2013 Homework 6 Solutions

Math Homotopy Theory Spring 2013 Homework 6 Solutions Math 527 - Homotopy Theory Sprig 2013 Homework 6 Solutios Problem 1. (The Hopf fibratio) Let S 3 C 2 = R 4 be the uit sphere. Stereographic projectio provides a homeomorphism S 2 = CP 1, where the North

More information

Name of the Student:

Name of the Student: SUBJECT NAME : Discrete Mathematics SUBJECT CODE : MA 65 MATERIAL NAME : Problem Material MATERIAL CODE : JM08ADM010 (Sca the above QR code for the direct dowload of this material) Name of the Studet:

More information

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) =

NICK DUFRESNE. 1 1 p(x). To determine some formulas for the generating function of the Schröder numbers, r(x) = a(x) = AN INTRODUCTION TO SCHRÖDER AND UNKNOWN NUMBERS NICK DUFRESNE Abstract. I this article we will itroduce two types of lattice paths, Schröder paths ad Ukow paths. We will examie differet properties of each,

More information

Introduction to Optimization Techniques

Introduction to Optimization Techniques Itroductio to Optimizatio Techiques Basic Cocepts of Aalysis - Real Aalysis, Fuctioal Aalysis 1 Basic Cocepts of Aalysis Liear Vector Spaces Defiitio: A vector space X is a set of elemets called vectors

More information

1 lim. f(x) sin(nx)dx = 0. n sin(nx)dx

1 lim. f(x) sin(nx)dx = 0. n sin(nx)dx Problem A. Calculate ta(.) to 4 decimal places. Solutio: The power series for si(x)/ cos(x) is x + x 3 /3 + (2/5)x 5 +. Puttig x =. gives ta(.) =.3. Problem 2A. Let f : R R be a cotiuous fuctio. Show that

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 11

Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 11 Machie Learig Theory Tübige Uiversity, WS 06/07 Lecture Tolstikhi Ilya Abstract We will itroduce the otio of reproducig kerels ad associated Reproducig Kerel Hilbert Spaces (RKHS). We will cosider couple

More information

Math 451: Euclidean and Non-Euclidean Geometry MWF 3pm, Gasson 204 Homework 3 Solutions

Math 451: Euclidean and Non-Euclidean Geometry MWF 3pm, Gasson 204 Homework 3 Solutions Math 451: Euclidea ad No-Euclidea Geometry MWF 3pm, Gasso 204 Homework 3 Solutios Exercises from 1.4 ad 1.5 of the otes: 4.3, 4.10, 4.12, 4.14, 4.15, 5.3, 5.4, 5.5 Exercise 4.3. Explai why Hp, q) = {x

More information

(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:

(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row: Math 5-4 Tue Feb 4 Cotiue with sectio 36 Determiats The effective way to compute determiats for larger-sized matrices without lots of zeroes is to ot use the defiitio, but rather to use the followig facts,

More information

AH Checklist (Unit 3) AH Checklist (Unit 3) Matrices

AH Checklist (Unit 3) AH Checklist (Unit 3) Matrices AH Checklist (Uit 3) AH Checklist (Uit 3) Matrices Skill Achieved? Kow that a matrix is a rectagular array of umbers (aka etries or elemets) i paretheses, each etry beig i a particular row ad colum Kow

More information

Orthogonal transformations

Orthogonal transformations Orthogoal trasformatios October 12, 2014 1 Defiig property The squared legth of a vector is give by takig the dot product of a vector with itself, v 2 v v g ij v i v j A orthogoal trasformatio is a liear

More information

Chain conditions. 1. Artinian and noetherian modules. ALGBOOK CHAINS 1.1

Chain conditions. 1. Artinian and noetherian modules. ALGBOOK CHAINS 1.1 CHAINS 1.1 Chai coditios 1. Artiia ad oetheria modules. (1.1) Defiitio. Let A be a rig ad M a A-module. The module M is oetheria if every ascedig chai!!m 1 M 2 of submodules M of M is stable, that is,

More information

8. Applications To Linear Differential Equations

8. Applications To Linear Differential Equations 8. Applicatios To Liear Differetial Equatios 8.. Itroductio 8.. Review Of Results Cocerig Liear Differetial Equatios Of First Ad Secod Orders 8.3. Eercises 8.4. Liear Differetial Equatios Of Order N 8.5.

More information

LECTURE 11: POSTNIKOV AND WHITEHEAD TOWERS

LECTURE 11: POSTNIKOV AND WHITEHEAD TOWERS LECTURE 11: POSTNIKOV AND WHITEHEAD TOWERS I the previous sectio we used the techique of adjoiig cells i order to costruct CW approximatios for arbitrary spaces Here we will see that the same techique

More information

Homework 3 Solutions

Homework 3 Solutions Math 4506 Sprig 04 Homework 3 Solutios. a The ACF of a MA process has a o-zero value oly at lags, 0, ad. Problem 4.3 from the textbook which you did t do, so I did t expect you to metio this shows that

More information

6.003 Homework #3 Solutions

6.003 Homework #3 Solutions 6.00 Homework # Solutios Problems. Complex umbers a. Evaluate the real ad imagiary parts of j j. π/ Real part = Imagiary part = 0 e Euler s formula says that j = e jπ/, so jπ/ j π/ j j = e = e. Thus the

More information

(for homogeneous primes P ) defining global complex algebraic geometry. Definition: (a) A subset V CP n is algebraic if there is a homogeneous

(for homogeneous primes P ) defining global complex algebraic geometry. Definition: (a) A subset V CP n is algebraic if there is a homogeneous Math 6130 Notes. Fall 2002. 4. Projective Varieties ad their Sheaves of Regular Fuctios. These are the geometric objects associated to the graded domais: C[x 0,x 1,..., x ]/P (for homogeeous primes P )

More information

PROBLEM SET I (Suggested Solutions)

PROBLEM SET I (Suggested Solutions) Eco3-Fall3 PROBLE SET I (Suggested Solutios). a) Cosider the followig: x x = x The quadratic form = T x x is the required oe i matrix form. Similarly, for the followig parts: x 5 b) x = = x c) x x x x

More information

Second day August 2, Problems and Solutions

Second day August 2, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 1997, Plovdiv, BULGARIA Secod day August, 1997 Problems ad Solutios Let Problem 1. Let f be a C 3 (R) o-egative

More information

1 Last time: similar and diagonalizable matrices

1 Last time: similar and diagonalizable matrices Last time: similar ad diagoalizable matrices Let be a positive iteger Suppose A is a matrix, v R, ad λ R Recall that v a eigevector for A with eigevalue λ if v ad Av λv, or equivaletly if v is a ozero

More information

In number theory we will generally be working with integers, though occasionally fractions and irrationals will come into play.

In number theory we will generally be working with integers, though occasionally fractions and irrationals will come into play. Number Theory Math 5840 otes. Sectio 1: Axioms. I umber theory we will geerally be workig with itegers, though occasioally fractios ad irratioals will come ito play. Notatio: Z deotes the set of all itegers

More information

b i u x i U a i j u x i u x j

b i u x i U a i j u x i u x j M ath 5 2 7 Fall 2 0 0 9 L ecture 1 9 N ov. 1 6, 2 0 0 9 ) S ecod- Order Elliptic Equatios: Weak S olutios 1. Defiitios. I this ad the followig two lectures we will study the boudary value problem Here

More information

GROUPS AND APPLICATIONS

GROUPS AND APPLICATIONS MATHEMATICS: CONCEPTS, AND FOUNDATIONS Vol. I - Groups ad Applicatios - Tadao ODA GROUPS AND APPLICATIONS Tadao ODA Tohoku Uiversity, Japa Keywords: group, homomorphism, quotiet group, group actio, trasformatio,

More information

Machine Learning for Data Science (CS 4786)

Machine Learning for Data Science (CS 4786) Machie Learig for Data Sciece CS 4786) Lecture 9: Pricipal Compoet Aalysis The text i black outlies mai ideas to retai from the lecture. The text i blue give a deeper uderstadig of how we derive or get

More information

Math E-21b Spring 2018 Homework #2

Math E-21b Spring 2018 Homework #2 Math E- Sprig 08 Homework # Prolems due Thursday, Feruary 8: Sectio : y = + 7 8 Fid the iverse of the liear trasformatio [That is, solve for, i terms of y, y ] y = + 0 Cosider the circular face i the accompayig

More information

Lesson 10: Limits and Continuity

Lesson 10: Limits and Continuity www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals

More information

Math 155 (Lecture 3)

Math 155 (Lecture 3) Math 55 (Lecture 3) September 8, I this lecture, we ll cosider the aswer to oe of the most basic coutig problems i combiatorics Questio How may ways are there to choose a -elemet subset of the set {,,,

More information

Chapter 3 Inner Product Spaces. Hilbert Spaces

Chapter 3 Inner Product Spaces. Hilbert Spaces Chapter 3 Ier Product Spaces. Hilbert Spaces 3. Ier Product Spaces. Hilbert Spaces 3.- Defiitio. A ier product space is a vector space X with a ier product defied o X. A Hilbert space is a complete ier

More information

SOLVED EXAMPLES

SOLVED EXAMPLES Prelimiaries Chapter PELIMINAIES Cocept of Divisibility: A o-zero iteger t is said to be a divisor of a iteger s if there is a iteger u such that s tu I this case we write t s (i) 6 as ca be writte as

More information