MATH 205 HOMEWORK #5 OFFICIAL SOLUTION
|
|
- Augustus Mason
- 5 years ago
- Views:
Transcription
1 MATH 205 HOMEWORK #5 OFFICIAL SOLUTION Problem 1: An inner product on a vector space V over F is a bilinear map, : V V F satisfying the extra conditions v, w = w, v, and v, v 0, with equality if and only if v = 0. (a) Show that the standard dot product on R n is an inner product. (b) Show that (f, g) f(x)g(x) dx is an inner product on C ([0, 1], R). (c) Suppose that F is ordered. Prove that for any v, w V, v, w 2 v, v w, w. When does equality hold? What standard inequality in trigonometry does this reflect when V = R n? (d) We say that two vectors v, w in V are orthogonal if v, w = 0. Suppose that T : V V is a linear transformation satisfying T v, w = v, T w for all v, w. Show that eigenvectors of T with different eigenvalues are orthogonal. Solution: Note that if we can show that a bilinear form is symmetric, then it is linear in the first variable if and only if it is linear in the second. Thus showing linearity in one variable is enough. (a) Let v = (a 1,..., a n ) and w = (b 1,..., b n ). The standard dot product is symmetric, as v, w = a 1 b a n b n = b 1 a b n a n = w, v. Let λ R and u = (c 1,..., c n ). Then v+λu, w = (a 1 +λc 1 )b 1 + +(a n +λc n )b n = a 1 b 1 + +a n b n +λ(c 1 b 1 + +c n b n ) = v, w +λ u, w. Thus, is bilinear. In addition, v, v = a a 2 n 0, with equality if and only if a i = 0 for all i. Thus the standard dot product is an inner products, as desired. (b) Note that f, g = f(x)g(x) dx = g(x)f(x) dx = g, f, so that, is symmetric. Let λ R, h C ([0, 1], R). Then (f(x) + λh(x))g(x) dx = f(x)g(x) dx + λ h(x)g(x) dx, so that, is bilinear. Lastly, suppose f(x) 0, so that there exists x 0 such that f(x 0 ) 0. Then there exists some δ such that f(x) > f(x 0 )/2 for x x 0 < δ. Then 1 0 f(x) 2 dx x0 +δ x 0 f(x 0 )/2 2 dx = f(x 0 )/2 2 δ > 0. Thus if f 0 then f, f > 0; clearly if f = 0 f, f = 0. Thus the second property of an inner product holds as well, and we are done. 1
2 2 MATH 205 HOMEWORK #5 OFFICIAL SOLUTION (c) First note that if either v or u is 0 then the inequality is an equality and holds trivially; thus we now consider the case when the two are nonzero. Let w = u u, v / v, v v. Then we have Now consider w, v = u, v u, v = 0. 0 w, w = u, u 2 u, v 2 / v, v + u, v 2 / v, v = u, u u, v 2 / v, v. Rearranging this, we get u, u v, v u, v 2, as desired. Equality will hold exactly when w = 0, which means that u is a scalar multiple of v. Note that in the classical case, this reduces to the inequality cos 2 θ 1. (d) Suppose that u is an eigenvector of T with eigenvalue λ and v is an eigenvector of T with eigenvalue ρ. Then λ u, v = T u, v = u, T v = ρ u, v. Thus if λ ρ we must have u, v = 0. In other words, u and v are orthogonal. Problem 2: Show that (F ) = F. Conclude that it is not the case that V and V are always isomorphic. Solution: Suppose that f (F ). Then f is uniquely determined by its value on a basis of F. Taking the standard basis {e i } i=1, where e i has a 1 in the ith position and 0s elsewhere, we see that the data of f consists exactly of a scalar in F for each i I. These add and scale pointwise. Thus f corresponds exactly to a vector in F. Since F is never isomorphic to F we see that V is not always isomorphic to V. Problem 3: Suppose that F is a field containing F and V is an F -vector space. If we consider F to be an F -vector space, we can form the tensor product F V, which is naturally an F -vector space. Show that it is also an F -vector space. This is called the change of base of V. Solution: In order to show that F V is an F -vector space we need to define scalar multiplication. For a pure tensor α v we define scalar multiplication by λ F by λ(α v) = (λα) v. If this is well-defined then it is clearly associative, since multiplication in F is associative. Similarly, it is unital: if λ = 1 then it clearly returns the same vector. We also have and λ(α v + β w) = (λα) v + (λβ) w (λ + ρ)(α v) = ((λ + ρ)α) v = (λα + ρα) v = (λα) v + (ρα) v. Thus if scalar multiplication is well-defined then it satisfies the axioms of a vector space. We have defined scalar multiplication by its action on the generators of F V. To show that this is well-defined we need to check that it preserves the relations; in other words, that the scalar
3 MATH 205 HOMEWORK #5 OFFICIAL SOLUTION 3 multiple of any element in A 0 stays in A 0. We therefore check: λ((α + β) v α v β v) = (λ(α + beta)) v (λα) v (λβ) v = ((λα) + (λβ)) v (λα) v (λβ) v A 0. λ(α (v + v ) α v α v ) = (λα) (v + v ) (λα) v (λα) v A 0. λ((aα) v a(α v)) = (λaα) v (λa)(α v) = (aλα) v (a(λ(α v))) = (aλα) v a((λα) v) A 0. λ(α (av) a(α v)) = (λα) (av) a(λ(α v)) = (λα) (av) a((λα) v) A 0. Thus multiplication by λ preserves A 0, and is thus well-defined on F V. Thus F V is an F -vector space. Problem 4: Prove the universal property for tensor products. In other words, show that for any vector spaces U, V, and W there is a bijection { } { } bilinear maps linear maps. U V W U V W Solution: Note that there exists a bilinear map ϕ : U V U V which maps (u, v) to u v. Thus we have a function { } { linear maps bilinear maps R: U V W U V W defined by mapping a linear map T : U V W to the bilinear map T ϕ. We need to show that this map is a bijection. FIrst we check that R is injective. Recall that S = {u v u U, v V } is a spanning set for U V ; thus any linear map T : U V W is uniquely defined by its values on S. if T, T : U V W satisfy T ϕ = T ϕ; then by definition T S = T S ; thus T = T. Thus R is injective. Now we need to check that R is surjective. Let T : U V W be a bilinear map. We need to show that there exists T : U V W such that T ϕ = T. We define T (u v) = T (u, v). If this is well-defined then it satisfies the desired condition; thus we need to check that it is zero on A 0. We check this on the generators of A 0 : T ((u + u ) v u v u v ) = T (u + u, v) T (u, v) T (u, v) = T (u + u, v) T (u + u, v) = 0 T (u (v + v ) u v u v ) = T (u, v + v ) T (u, v) T (u, v ) = T (u, v + v ) T (u, v + v ) = 0 T ((λu) v λ(u v)) = T (λu, v) λt (u, v) = λt (u, v) λt (u, v) = 0 T (u (λv) λ(u v)) = T (u, λv) λt (u, v) = λt (u, v) λt (u, v) = 0. Thus it is well-defined and R is surjective. Problem 5: Let {u i } m i=1 and {v j} n j=1 be bases of U and V, respectively. Show that a general element w = m n i=1 j=1 w iju i v j is the sum of r pure tensors if and only if the m n matrix (w ij ) has rank at most r. }
4 4 MATH 205 HOMEWORK #5 OFFICIAL SOLUTION Solution: Note that if we replace the basis {u i } m i=1 with the basis (u 1,..., λu i + u j, u i+1,..., u m ) then the relationship between the matrix (w ij ) and the matrix for w written in terms of the new basis is the same, except that column j turns into C j λ 1 C i. Thus we can do column reductions on the matrix (w ij ) by replacing the basis {u i } m i=1. Similarly, we can do row reductions by replacing the basis {v j } n j=1. A matrix has rank r if and only if it can be reduced to a matrix with r 1 s, with no two 1 s in any row or column, using row and column reductions. If the matrix (w ij ) has only r entries then we can rewrite w as a sum of r pure tensors. Thus if the matrix has rank r then by doing changes of basis corresponding to row and column operations we can write w as a sum of r pure tensors. Note that if w = (a 1 u a m u m ) (b 1 v b n v n ) then w ij = a i b j, so that (w ij ) has rank 1, and every row is a scalar multiple of another row. Thus if w = x 1 y x r y r each row in (w ij ) is in the span of the vectors x 1,..., x r, so the row space of (w ij ) has dimension at most r. Thus the rank of the matrix (w ij ) is at most r, as desired. Problem 6: This next problem will investigate tensor products of modules. Note that the definition of linear and bilinear still work for rings instead of fields. Suppose that R is a commutative ring with unit, and M and N are R-modules. We define the tensor product M R N to be the R-module such that there is a bijection { } { } bilinear maps linear maps M N S M R N S for any R-module S. (a) Suppose that M = R m and N = R n. Show that M R N = R mn. (b) Suppose that R S is a homomorphism of rings. Show that S is an R-module. Show that S R M is an S-module. (c) Now suppose that R = Z. As we discussed before, Z-modules are just abelian groups. What is Z Z Z/nZ? What is Z/pZ Z Z/qZ for not necessarily distinct primes p and q? Find a general description of Z/mZ Z Z/nZ. Solution: Note that the construction of the tensor product that we did for vector spaces works equally well in the case of R-modules, since we did not use any properties of fields in the construction. Before we begin the problem we will show that for any R-modules M, M and N, (M M ) N = (M N) (M N). We define ϕ : (M M ) N (M N) (M N) by mappint (m, m ) n to (m n, m n). To check that this is a well-defined morphism of R-modules we need to check that it is 0 on the generators of A 0. We check: ϕ(((m, m ) + (m 0, m 0)) n (m, m ) n (m 0, m 0) n) = ((m + m 0 ) n, (m + m 0) n) (m n, m n) (m 0 n, m 0 n) ϕ((m, m ) (n + n ) = ((m + m 0 ) n m n m 0 n, (m + m 0) n m n m 0 n) = (0, 0) (M N) (M N). (m, m ) n (m, m ) n ) = (m (n + n ), m (n + n )) (m n, m n) (m n, m n ) = (m (n + n ) m n m n, m (n + n ) m n m n ) = (0, 0) (M N) (M N).
5 MATH 205 HOMEWORK #5 OFFICIAL SOLUTION 5 The other two relations follow analogously. We define φ : (M N) (M N) (M M ) N by mapping (m n, m n ) to (m, 0) n + (0, m ) n. We check that this is well-defined: φ((m + m 0 ) n m n m 0 n, (m + m 0) n m n m 0 n ) = (m + m 0, 0) n (m, 0) n (m 0, 0) n +(m + m 0, 0) n (m, 0) n (m 0, 0) n = 0. The other relations follow analogously. Thus these are well-defined maps. We have and φ(ϕ((m, m ) n)) = φ(m n, m n) = (m, 0) n + (0, m ) n = (m, m ) n ϕ(φ(m n, m n )) = ϕ((m, 0) n + (0, m ) n ) = (m n, 0 n) + (0 n, m n ) = (m n + 0 n, 0 n + m n ) = (m n, m n ). Thus these are mutually inverse isomorphisms. We make one extra observation before starting. For any m M, n N, Indeed, 0 n = m 0 = 0 M R N. 0 n = (0 + 0) n = 0 n + 0 n, and subtracting 0 n from each side gives us the desired equality. The other one follows analogously. We are now ready to solve the problem. (a) Note that R n is the n-fold product of R with itself. Thus applying the above we know that R n R R m = (R R R m ) n = R mn. The last step follows because R R R m = R m. Indeed, we note that for any pure tensor r v R R R m we have r v = (r 1) v = r(1 v) = 1 rv. So we have a map R R R m R m which takes r v to rv. We also have an inverse map which takes v R m to 1 v. (b) The proof we wrote in problem 3 works verbatim here. (c) We define mutually inverse isomorphisms Z Z Z/n Z/n and Z/n Z Z Z/n. The first takes a [b] to [ab], and the second takes [b] to 1 [b]. Thus Z Z Z/n = Z/n. Now suppose that m and n are relatively prime numbers. Let a b Z/m Z Z/n. Since m and n are relatively prime, n has a multiplicative inverse n in Z/m. Then we have a b = (1a) b = (nn a) b = n(n a b) = (n a) (nb). But n = 0 in Z/n, so nb = 0. Thus a b = (n a) 0 = 0, so all pure tensors are equal to 0. Therefore Z/m Z/n = 0 if m and n are relatively prime. On the other hand, if m n then Z/m Z Z/n = Z/m. Indeed, note that a b = (a 1) b = a(1 b) = a(1 (b 1)) = ab(1 1). Thus Z/m Z Z/n is generated by the multiples of 1 1, and is thus isomorphic to Z/k for some k. Note that m(1 1) = (m 1) 1 = 0 1 = 0, so k m. On the other hand, we have a surjective homomorphism Z/m Z Z/n Z/m given by a b ab. Thus m k, and we see that k = m, as desired.
6 6 MATH 205 HOMEWORK #5 OFFICIAL SOLUTION Write m = p k 1 1 pk l l and n = p m 1 1 p m l l, where some of the k i or m j can be 0. We can then write Z/mZ = Z/p k 1 1 Z Z/pk l l Z and Z/nZ = Z/p m 1 1 Z/p m l l. Then Z/mZ Z Z/nZ l r = Z/p k i i Z Z/pm j j Z. i=1 j=1 If i j then that summand is the trivial group. On the other hand, if i = j then the tensor product is just the smaller power of p i. Thus we have Z/mZ Z Z/nZ l = Z/p min(k i,m i ) i Z = Z/ gcd(m, n)z. i=1
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND
More informationMath 121 Homework 5: Notes on Selected Problems
Math 121 Homework 5: Notes on Selected Problems 12.1.2. Let M be a module over the integral domain R. (a) Assume that M has rank n and that x 1,..., x n is any maximal set of linearly independent elements
More informationMath 593: Problem Set 10
Math 593: Problem Set Feng Zhu, edited by Prof Smith Hermitian inner-product spaces (a By conjugate-symmetry and linearity in the first argument, f(v, λw = f(λw, v = λf(w, v = λf(w, v = λf(v, w. (b We
More informationMath 581 Problem Set 3 Solutions
Math 581 Problem Set 3 Solutions 1. Prove that complex conjugation is a isomorphism from C to C. Proof: First we prove that it is a homomorphism. Define : C C by (z) = z. Note that (1) = 1. The other properties
More informationNotes on tensor products. Robert Harron
Notes on tensor products Robert Harron Department of Mathematics, Keller Hall, University of Hawai i at Mānoa, Honolulu, HI 96822, USA E-mail address: rharron@math.hawaii.edu Abstract. Graduate algebra
More informationLinear Algebra. Min Yan
Linear Algebra Min Yan January 2, 2018 2 Contents 1 Vector Space 7 1.1 Definition................................. 7 1.1.1 Axioms of Vector Space..................... 7 1.1.2 Consequence of Axiom......................
More informationReview of linear algebra
Review of linear algebra 1 Vectors and matrices We will just touch very briefly on certain aspects of linear algebra, most of which should be familiar. Recall that we deal with vectors, i.e. elements of
More informationMATH 205 HOMEWORK #3 OFFICIAL SOLUTION. Problem 1: Find all eigenvalues and eigenvectors of the following linear transformations. (a) F = R, V = R 3,
MATH 205 HOMEWORK #3 OFFICIAL SOLUTION Problem 1: Find all eigenvalues and eigenvectors of the following linear transformations. a F = R, V = R 3, b F = R or C, V = F 2, T = T = 9 4 4 8 3 4 16 8 7 0 1
More informationLINEAR ALGEBRA REVIEW
LINEAR ALGEBRA REVIEW JC Stuff you should know for the exam. 1. Basics on vector spaces (1) F n is the set of all n-tuples (a 1,... a n ) with a i F. It forms a VS with the operations of + and scalar multiplication
More informationMATH 235. Final ANSWERS May 5, 2015
MATH 235 Final ANSWERS May 5, 25. ( points) Fix positive integers m, n and consider the vector space V of all m n matrices with entries in the real numbers R. (a) Find the dimension of V and prove your
More informationMath 210B: Algebra, Homework 4
Math 210B: Algebra, Homework 4 Ian Coley February 5, 2014 Problem 1. Let S be a multiplicative subset in a commutative ring R. Show that the localisation functor R-Mod S 1 R-Mod, M S 1 M, is exact. First,
More informationMATH 101B: ALGEBRA II PART A: HOMOLOGICAL ALGEBRA 23
MATH 101B: ALGEBRA II PART A: HOMOLOGICAL ALGEBRA 23 6.4. Homotopy uniqueness of projective resolutions. Here I proved that the projective resolution of any R-module (or any object of an abelian category
More information( 9x + 3y. y 3y = (λ 9)x 3x + y = λy 9x + 3y = 3λy 9x + (λ 9)x = λ(λ 9)x. (λ 2 10λ)x = 0
Math 46 (Lesieutre Practice final ( minutes December 9, 8 Problem Consider the matrix M ( 9 a Prove that there is a basis for R consisting of orthonormal eigenvectors for M This is just the spectral theorem:
More informationMath 121 Homework 4: Notes on Selected Problems
Math 121 Homework 4: Notes on Selected Problems 11.2.9. If W is a subspace of the vector space V stable under the linear transformation (i.e., (W ) W ), show that induces linear transformations W on W
More informationMath 121 Homework 2: Notes on Selected Problems
Math 121 Homework 2: Notes on Selected Problems Problem 2. Let M be the ring of 2 2 complex matrices. Let C be the left M-module of 2 1 complex vectors; the module structure is given by matrix multiplication:
More informationSome notes on linear algebra
Some notes on linear algebra Throughout these notes, k denotes a field (often called the scalars in this context). Recall that this means that there are two binary operations on k, denoted + and, that
More informationHomework #05, due 2/17/10 = , , , , , Additional problems recommended for study: , , 10.2.
Homework #05, due 2/17/10 = 10.3.1, 10.3.3, 10.3.4, 10.3.5, 10.3.7, 10.3.15 Additional problems recommended for study: 10.2.1, 10.2.2, 10.2.3, 10.2.5, 10.2.6, 10.2.10, 10.2.11, 10.3.2, 10.3.9, 10.3.12,
More informationLECTURES MATH370-08C
LECTURES MATH370-08C A.A.KIRILLOV 1. Groups 1.1. Abstract groups versus transformation groups. An abstract group is a collection G of elements with a multiplication rule for them, i.e. a map: G G G : (g
More informationSPRING 2006 PRELIMINARY EXAMINATION SOLUTIONS
SPRING 006 PRELIMINARY EXAMINATION SOLUTIONS 1A. Let G be the subgroup of the free abelian group Z 4 consisting of all integer vectors (x, y, z, w) such that x + 3y + 5z + 7w = 0. (a) Determine a linearly
More informationMATH 115A: SAMPLE FINAL SOLUTIONS
MATH A: SAMPLE FINAL SOLUTIONS JOE HUGHES. Let V be the set of all functions f : R R such that f( x) = f(x) for all x R. Show that V is a vector space over R under the usual addition and scalar multiplication
More informationHonors Algebra 4, MATH 371 Winter 2010 Assignment 4 Due Wednesday, February 17 at 08:35
Honors Algebra 4, MATH 371 Winter 2010 Assignment 4 Due Wednesday, February 17 at 08:35 1. Let R be a commutative ring with 1 0. (a) Prove that the nilradical of R is equal to the intersection of the prime
More informationMath Linear Algebra Final Exam Review Sheet
Math 15-1 Linear Algebra Final Exam Review Sheet Vector Operations Vector addition is a component-wise operation. Two vectors v and w may be added together as long as they contain the same number n of
More informationSolutions to Example Sheet 1
Solutions to Example Sheet 1 1 The symmetric group S 3 acts on R 2, by permuting the vertices of an equilateral triangle centered at 0 Choose a basis of R 2, and for each g S 3, write the matrix of g in
More informationINTRO TO TENSOR PRODUCTS MATH 250B
INTRO TO TENSOR PRODUCTS MATH 250B ADAM TOPAZ 1. Definition of the Tensor Product Throughout this note, A will denote a commutative ring. Let M, N be two A-modules. For a third A-module Z, consider the
More informationLecture 7. This set is the set of equivalence classes of the equivalence relation on M S defined by
Lecture 7 1. Modules of fractions Let S A be a multiplicative set, and A M an A-module. In what follows, we will denote by s, t, u elements from S and by m, n elements from M. Similar to the concept of
More informationSystems of Linear Equations
Systems of Linear Equations Math 108A: August 21, 2008 John Douglas Moore Our goal in these notes is to explain a few facts regarding linear systems of equations not included in the first few chapters
More informationD-MATH Algebra I HS 2013 Prof. Brent Doran. Exercise 11. Rings: definitions, units, zero divisors, polynomial rings
D-MATH Algebra I HS 2013 Prof. Brent Doran Exercise 11 Rings: definitions, units, zero divisors, polynomial rings 1. Show that the matrices M(n n, C) form a noncommutative ring. What are the units of M(n
More information18.312: Algebraic Combinatorics Lionel Levine. Lecture 22. Smith normal form of an integer matrix (linear algebra over Z).
18.312: Algebraic Combinatorics Lionel Levine Lecture date: May 3, 2011 Lecture 22 Notes by: Lou Odette This lecture: Smith normal form of an integer matrix (linear algebra over Z). 1 Review of Abelian
More informationReview of Linear Algebra
Review of Linear Algebra Throughout these notes, F denotes a field (often called the scalars in this context). 1 Definition of a vector space Definition 1.1. A F -vector space or simply a vector space
More informationNote that a unit is unique: 1 = 11 = 1. Examples: Nonnegative integers under addition; all integers under multiplication.
Algebra fact sheet An algebraic structure (such as group, ring, field, etc.) is a set with some operations and distinguished elements (such as 0, 1) satisfying some axioms. This is a fact sheet with definitions
More informationNOTES ON SPLITTING FIELDS
NOTES ON SPLITTING FIELDS CİHAN BAHRAN I will try to define the notion of a splitting field of an algebra over a field using my words, to understand it better. The sources I use are Peter Webb s and T.Y
More informationLecture Notes for Math 414: Linear Algebra II Fall 2015, Michigan State University
Lecture Notes for Fall 2015, Michigan State University Matthew Hirn December 11, 2015 Beginning of Lecture 1 1 Vector Spaces What is this course about? 1. Understanding the structural properties of a wide
More informationAlgebra Exam Syllabus
Algebra Exam Syllabus The Algebra comprehensive exam covers four broad areas of algebra: (1) Groups; (2) Rings; (3) Modules; and (4) Linear Algebra. These topics are all covered in the first semester graduate
More informationA Little Beyond: Linear Algebra
A Little Beyond: Linear Algebra Akshay Tiwary March 6, 2016 Any suggestions, questions and remarks are welcome! 1 A little extra Linear Algebra 1. Show that any set of non-zero polynomials in [x], no two
More informationMath 593: Problem Set 7
Math 593: Problem Set 7 Feng Zhu, Punya Satpathy, Alex Vargo, Umang Varma, Daniel Irvine, Joe Kraisler, Samantha Pinella, Lara Du, Caleb Springer, Jiahua Gu, Karen Smith 1 Basics properties of tensor product
More informationMathematics Department Stanford University Math 61CM/DM Vector spaces and linear maps
Mathematics Department Stanford University Math 61CM/DM Vector spaces and linear maps We start with the definition of a vector space; you can find this in Section A.8 of the text (over R, but it works
More informationSolving a system by back-substitution, checking consistency of a system (no rows of the form
MATH 520 LEARNING OBJECTIVES SPRING 2017 BROWN UNIVERSITY SAMUEL S. WATSON Week 1 (23 Jan through 27 Jan) Definition of a system of linear equations, definition of a solution of a linear system, elementary
More informationLinear Algebra Review
Chapter 1 Linear Algebra Review It is assumed that you have had a course in linear algebra, and are familiar with matrix multiplication, eigenvectors, etc. I will review some of these terms here, but quite
More information1. Foundations of Numerics from Advanced Mathematics. Linear Algebra
Foundations of Numerics from Advanced Mathematics Linear Algebra Linear Algebra, October 23, 22 Linear Algebra Mathematical Structures a mathematical structure consists of one or several sets and one or
More information2.2. Show that U 0 is a vector space. For each α 0 in F, show by example that U α does not satisfy closure.
Hints for Exercises 1.3. This diagram says that f α = β g. I will prove f injective g injective. You should show g injective f injective. Assume f is injective. Now suppose g(x) = g(y) for some x, y A.
More information2.1 Modules and Module Homomorphisms
2.1 Modules and Module Homomorphisms The notion of a module arises out of attempts to do classical linear algebra (vector spaces over fields) using arbitrary rings of coefficients. Let A be a ring. An
More informationThe following definition is fundamental.
1. Some Basics from Linear Algebra With these notes, I will try and clarify certain topics that I only quickly mention in class. First and foremost, I will assume that you are familiar with many basic
More informationMath 121 Homework 3 Solutions
Math 121 Homework 3 Solutions Problem 13.4 #6. Let K 1 and K 2 be finite extensions of F in the field K, and assume that both are splitting fields over F. (a) Prove that their composite K 1 K 2 is a splitting
More informationMULTILINEAR ALGEBRA: THE TENSOR PRODUCT
MULTILINEAR ALGEBRA: THE TENSOR PRODUCT This writeup is drawn closely from chapter 27 of Paul Garrett s text Abstract Algebra, available from Chapman and Hall/CRC publishers and also available online at
More informationA Primer on Homological Algebra
A Primer on Homological Algebra Henry Y Chan July 12, 213 1 Modules For people who have taken the algebra sequence, you can pretty much skip the first section Before telling you what a module is, you probably
More informationALGEBRA QUALIFYING EXAM PROBLEMS
ALGEBRA QUALIFYING EXAM PROBLEMS Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND MODULES General
More informationWritten Homework # 2 Solution
Math 517 Spring 2007 Radford Written Homework # 2 Solution 02/23/07 Throughout R and S are rings with unity; Z denotes the ring of integers and Q, R, and C denote the rings of rational, real, and complex
More informationA PRIMER ON SESQUILINEAR FORMS
A PRIMER ON SESQUILINEAR FORMS BRIAN OSSERMAN This is an alternative presentation of most of the material from 8., 8.2, 8.3, 8.4, 8.5 and 8.8 of Artin s book. Any terminology (such as sesquilinear form
More informationNumerical Linear Algebra
University of Alabama at Birmingham Department of Mathematics Numerical Linear Algebra Lecture Notes for MA 660 (1997 2014) Dr Nikolai Chernov April 2014 Chapter 0 Review of Linear Algebra 0.1 Matrices
More informationINNER PRODUCT SPACE. Definition 1
INNER PRODUCT SPACE Definition 1 Suppose u, v and w are all vectors in vector space V and c is any scalar. An inner product space on the vectors space V is a function that associates with each pair of
More informationA matrix is a rectangular array of. objects arranged in rows and columns. The objects are called the entries. is called the size of the matrix, and
Section 5.5. Matrices and Vectors A matrix is a rectangular array of objects arranged in rows and columns. The objects are called the entries. A matrix with m rows and n columns is called an m n matrix.
More informationMath 120 HW 9 Solutions
Math 120 HW 9 Solutions June 8, 2018 Question 1 Write down a ring homomorphism (no proof required) f from R = Z[ 11] = {a + b 11 a, b Z} to S = Z/35Z. The main difficulty is to find an element x Z/35Z
More informationMath 210B:Algebra, Homework 2
Math 210B:Algebra, Homework 2 Ian Coley January 21, 2014 Problem 1. Is f = 2X 5 6X + 6 irreducible in Z[X], (S 1 Z)[X], for S = {2 n, n 0}, Q[X], R[X], C[X]? To begin, note that 2 divides all coefficients
More informationA matrix is a rectangular array of. objects arranged in rows and columns. The objects are called the entries. is called the size of the matrix, and
Section 5.5. Matrices and Vectors A matrix is a rectangular array of objects arranged in rows and columns. The objects are called the entries. A matrix with m rows and n columns is called an m n matrix.
More informationMath 24 Spring 2012 Sample Homework Solutions Week 8
Math 4 Spring Sample Homework Solutions Week 8 Section 5. (.) Test A M (R) for diagonalizability, and if possible find an invertible matrix Q and a diagonal matrix D such that Q AQ = D. ( ) 4 (c) A =.
More informationMATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)
MATH 20F: LINEAR ALGEBRA LECTURE B00 (T KEMP) Definition 01 If T (x) = Ax is a linear transformation from R n to R m then Nul (T ) = {x R n : T (x) = 0} = Nul (A) Ran (T ) = {Ax R m : x R n } = {b R m
More informationMath 120: Homework 6 Solutions
Math 120: Homewor 6 Solutions November 18, 2018 Problem 4.4 # 2. Prove that if G is an abelian group of order pq, where p and q are distinct primes then G is cyclic. Solution. By Cauchy s theorem, G has
More informationReal representations
Real representations 1 Definition of a real representation Definition 1.1. Let V R be a finite dimensional real vector space. A real representation of a group G is a homomorphism ρ VR : G Aut V R, where
More informationEXERCISES IN MODULAR FORMS I (MATH 726) (2) Prove that a lattice L is integral if and only if its Gram matrix has integer coefficients.
EXERCISES IN MODULAR FORMS I (MATH 726) EYAL GOREN, MCGILL UNIVERSITY, FALL 2007 (1) We define a (full) lattice L in R n to be a discrete subgroup of R n that contains a basis for R n. Prove that L is
More informationCOURSE SUMMARY FOR MATH 504, FALL QUARTER : MODERN ALGEBRA
COURSE SUMMARY FOR MATH 504, FALL QUARTER 2017-8: MODERN ALGEBRA JAROD ALPER Week 1, Sept 27, 29: Introduction to Groups Lecture 1: Introduction to groups. Defined a group and discussed basic properties
More information13 More on free abelian groups
13 More on free abelian groups Recall. G is a free abelian group if G = i I Z for some set I. 13.1 Definition. Let G be an abelian group. A set B G is a basis of G if B generates G if for some x 1,...x
More informationDefinitions for Quizzes
Definitions for Quizzes Italicized text (or something close to it) will be given to you. Plain text is (an example of) what you should write as a definition. [Bracketed text will not be given, nor does
More informationMATH SOLUTIONS TO PRACTICE MIDTERM LECTURE 1, SUMMER Given vector spaces V and W, V W is the vector space given by
MATH 110 - SOLUTIONS TO PRACTICE MIDTERM LECTURE 1, SUMMER 2009 GSI: SANTIAGO CAÑEZ 1. Given vector spaces V and W, V W is the vector space given by V W = {(v, w) v V and w W }, with addition and scalar
More informationAN ALGEBRA PRIMER WITH A VIEW TOWARD CURVES OVER FINITE FIELDS
AN ALGEBRA PRIMER WITH A VIEW TOWARD CURVES OVER FINITE FIELDS The integers are the set 1. Groups, Rings, and Fields: Basic Examples Z := {..., 3, 2, 1, 0, 1, 2, 3,...}, and we can add, subtract, and multiply
More informationNotes on singular value decomposition for Math 54. Recall that if A is a symmetric n n matrix, then A has real eigenvalues A = P DP 1 A = P DP T.
Notes on singular value decomposition for Math 54 Recall that if A is a symmetric n n matrix, then A has real eigenvalues λ 1,, λ n (possibly repeated), and R n has an orthonormal basis v 1,, v n, where
More informationALGEBRA HW 3 CLAY SHONKWILER
ALGEBRA HW 3 CLAY SHONKWILER (a): Show that R[x] is a flat R-module. 1 Proof. Consider the set A = {1, x, x 2,...}. Then certainly A generates R[x] as an R-module. Suppose there is some finite linear combination
More informationSpectra of Semidirect Products of Cyclic Groups
Spectra of Semidirect Products of Cyclic Groups Nathan Fox 1 University of Minnesota-Twin Cities Abstract The spectrum of a graph is the set of eigenvalues of its adjacency matrix A group, together with
More informationJohns Hopkins University, Department of Mathematics Abstract Algebra - Spring 2009 Midterm
Johns Hopkins University, Department of Mathematics 110.401 Abstract Algebra - Spring 2009 Midterm Instructions: This exam has 8 pages. No calculators, books or notes allowed. You must answer the first
More informationhomogeneous 71 hyperplane 10 hyperplane 34 hyperplane 69 identity map 171 identity map 186 identity map 206 identity matrix 110 identity matrix 45
address 12 adjoint matrix 118 alternating 112 alternating 203 angle 159 angle 33 angle 60 area 120 associative 180 augmented matrix 11 axes 5 Axiom of Choice 153 basis 178 basis 210 basis 74 basis test
More informationMonoids. Definition: A binary operation on a set M is a function : M M M. Examples:
Monoids Definition: A binary operation on a set M is a function : M M M. If : M M M, we say that is well defined on M or equivalently, that M is closed under the operation. Examples: Definition: A monoid
More informationTENSOR PRODUCTS II KEITH CONRAD
TENSOR PRODUCTS II KEITH CONRAD 1. Introduction Continuing our study of tensor products, we will see how to combine two linear maps M M and N N into a linear map M R N M R N. This leads to flat modules
More informationLecture notes - Math 110 Lec 002, Summer The reference [LADR] stands for Axler s Linear Algebra Done Right, 3rd edition.
Lecture notes - Math 110 Lec 002, Summer 2016 BW The reference [LADR] stands for Axler s Linear Algebra Done Right, 3rd edition. 1 Contents 1 Sets and fields - 6/20 5 1.1 Set notation.................................
More informationTwo subgroups and semi-direct products
Two subgroups and semi-direct products 1 First remarks Throughout, we shall keep the following notation: G is a group, written multiplicatively, and H and K are two subgroups of G. We define the subset
More informationChapter 6: Orthogonality
Chapter 6: Orthogonality (Last Updated: November 7, 7) These notes are derived primarily from Linear Algebra and its applications by David Lay (4ed). A few theorems have been moved around.. Inner products
More informationLinear Algebra Review. Vectors
Linear Algebra Review 9/4/7 Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa (UCSD) Cogsci 8F Linear Algebra review Vectors
More informationLecture 29: Free modules, finite generation, and bases for vector spaces
Lecture 29: Free modules, finite generation, and bases for vector spaces Recall: 1. Universal property of free modules Definition 29.1. Let R be a ring. Then the direct sum module is called the free R-module
More informationFormal power series rings, inverse limits, and I-adic completions of rings
Formal power series rings, inverse limits, and I-adic completions of rings Formal semigroup rings and formal power series rings We next want to explore the notion of a (formal) power series ring in finitely
More informationGEOMETRIC CONSTRUCTIONS AND ALGEBRAIC FIELD EXTENSIONS
GEOMETRIC CONSTRUCTIONS AND ALGEBRAIC FIELD EXTENSIONS JENNY WANG Abstract. In this paper, we study field extensions obtained by polynomial rings and maximal ideals in order to determine whether solutions
More information1.8 Dual Spaces (non-examinable)
2 Theorem 1715 is just a restatement in terms of linear morphisms of a fact that you might have come across before: every m n matrix can be row-reduced to reduced echelon form using row operations Moreover,
More informationAlgebraic Number Theory
TIFR VSRP Programme Project Report Algebraic Number Theory Milind Hegde Under the guidance of Prof. Sandeep Varma July 4, 2015 A C K N O W L E D G M E N T S I would like to express my thanks to TIFR for
More information0.1 Universal Coefficient Theorem for Homology
0.1 Universal Coefficient Theorem for Homology 0.1.1 Tensor Products Let A, B be abelian groups. Define the abelian group A B = a b a A, b B / (0.1.1) where is generated by the relations (a + a ) b = a
More information1. General Vector Spaces
1.1. Vector space axioms. 1. General Vector Spaces Definition 1.1. Let V be a nonempty set of objects on which the operations of addition and scalar multiplication are defined. By addition we mean a rule
More informationMath 414: Linear Algebra II, Fall 2015 Final Exam
Math 414: Linear Algebra II, Fall 2015 Final Exam December 17, 2015 Name: This is a closed book single author exam. Use of books, notes, or other aids is not permissible, nor is collaboration with any
More informationThere are two things that are particularly nice about the first basis
Orthogonality and the Gram-Schmidt Process In Chapter 4, we spent a great deal of time studying the problem of finding a basis for a vector space We know that a basis for a vector space can potentially
More informationMoreover this binary operation satisfies the following properties
Contents 1 Algebraic structures 1 1.1 Group........................................... 1 1.1.1 Definitions and examples............................. 1 1.1.2 Subgroup.....................................
More informationLinear algebra 2. Yoav Zemel. March 1, 2012
Linear algebra 2 Yoav Zemel March 1, 2012 These notes were written by Yoav Zemel. The lecturer, Shmuel Berger, should not be held responsible for any mistake. Any comments are welcome at zamsh7@gmail.com.
More informationLinear Algebra. and
Instructions Please answer the six problems on your own paper. These are essay questions: you should write in complete sentences. 1. Are the two matrices 1 2 2 1 3 5 2 7 and 1 1 1 4 4 2 5 5 2 row equivalent?
More informationMath 306 Topics in Algebra, Spring 2013 Homework 7 Solutions
Math 306 Topics in Algebra, Spring 203 Homework 7 Solutions () (5 pts) Let G be a finite group. Show that the function defines an inner product on C[G]. We have Also Lastly, we have C[G] C[G] C c f + c
More informationTotal 100
Math 542 Midterm Exam, Spring 2016 Prof: Paul Terwilliger Your Name (please print) SOLUTIONS NO CALCULATORS/ELECTRONIC DEVICES ALLOWED. MAKE SURE YOUR CELL PHONE IS OFF. Problem Value 1 10 2 10 3 10 4
More informationRepresentations of algebraic groups and their Lie algebras Jens Carsten Jantzen Lecture III
Representations of algebraic groups and their Lie algebras Jens Carsten Jantzen Lecture III Lie algebras. Let K be again an algebraically closed field. For the moment let G be an arbitrary algebraic group
More informationMath 581 Problem Set 6 Solutions
Math 581 Problem Set 6 Solutions 1. Let F K be a finite field extension. Prove that if [K : F ] = 1, then K = F. Proof: Let v K be a basis of K over F. Let c be any element of K. There exists α c F so
More informationMATH 221, Spring Homework 10 Solutions
MATH 22, Spring 28 - Homework Solutions Due Tuesday, May Section 52 Page 279, Problem 2: 4 λ A λi = and the characteristic polynomial is det(a λi) = ( 4 λ)( λ) ( )(6) = λ 6 λ 2 +λ+2 The solutions to the
More informationReview of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem
Review of Linear Algebra Definitions, Change of Basis, Trace, Spectral Theorem Steven J. Miller June 19, 2004 Abstract Matrices can be thought of as rectangular (often square) arrays of numbers, or as
More informationRings and Fields Theorems
Rings and Fields Theorems Rajesh Kumar PMATH 334 Intro to Rings and Fields Fall 2009 October 25, 2009 12 Rings and Fields 12.1 Definition Groups and Abelian Groups Let R be a non-empty set. Let + and (multiplication)
More informationNOTES ON LINEAR ALGEBRA OVER INTEGRAL DOMAINS. Contents. 1. Introduction 1 2. Rank and basis 1 3. The set of linear maps 4. 1.
NOTES ON LINEAR ALGEBRA OVER INTEGRAL DOMAINS Contents 1. Introduction 1 2. Rank and basis 1 3. The set of linear maps 4 1. Introduction These notes establish some basic results about linear algebra over
More informationMath 546, Exam 2 Information.
Math 546, Exam 2 Information. 10/21/09, LC 303B, 10:10-11:00. Exam 2 will be based on: Sections 3.2, 3.3, 3.4, 3.5; The corresponding assigned homework problems (see http://www.math.sc.edu/ boylan/sccourses/546fa09/546.html)
More informationLINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM
LINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM Unless otherwise stated, all vector spaces in this worksheet are finite dimensional and the scalar field F is R or C. Definition 1. A linear operator
More informationVector Spaces and Linear Transformations
Vector Spaces and Linear Transformations Wei Shi, Jinan University 2017.11.1 1 / 18 Definition (Field) A field F = {F, +, } is an algebraic structure formed by a set F, and closed under binary operations
More information2. Linear algebra. matrices and vectors. linear equations. range and nullspace of matrices. function of vectors, gradient and Hessian
FE661 - Statistical Methods for Financial Engineering 2. Linear algebra Jitkomut Songsiri matrices and vectors linear equations range and nullspace of matrices function of vectors, gradient and Hessian
More information08a. Operators on Hilbert spaces. 1. Boundedness, continuity, operator norms
(February 24, 2017) 08a. Operators on Hilbert spaces Paul Garrett garrett@math.umn.edu http://www.math.umn.edu/ garrett/ [This document is http://www.math.umn.edu/ garrett/m/real/notes 2016-17/08a-ops
More information