Math 110: Worksheet 1 Solutions

Similar documents
Vector Spaces and SubSpaces

Math 421, Homework #9 Solutions

XV - Vector Spaces and Subspaces

Linear algebra I Homework #1 due Thursday, Oct. 5

Chapter 7: Exponents

[ Here 21 is the dot product of (3, 1, 2, 5) with (2, 3, 1, 2), and 31 is the dot product of

Math 24 Spring 2012 Sample Homework Solutions Week 8

EXERCISE SET 5.1. = (kx + kx + k, ky + ky + k ) = (kx + kx + 1, ky + ky + 1) = ((k + )x + 1, (k + )y + 1)

Linear algebra II Tutorial solutions #1 A = x 1

1. Select the unique answer (choice) for each problem. Write only the answer.

Evaluating Determinants by Row Reduction

Polynomial Functions

Solution to Set 7, Math 2568

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

Recitation 8: Graphs and Adjacency Matrices

Dr. Abdulla Eid. Section 4.2 Subspaces. Dr. Abdulla Eid. MATHS 211: Linear Algebra. College of Science

Properties of Linear Transformations from R n to R m

1. Let r, s, t, v be the homogeneous relations defined on the set M = {2, 3, 4, 5, 6} by

What is a Linear Space/Vector Space?

MATH 31 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL

Lecture 16: 9.2 Geometry of Linear Operators

2.6 Logarithmic Functions. Inverse Functions. Question: What is the relationship between f(x) = x 2 and g(x) = x?

MAT 419 Lecture Notes Transcribed by Eowyn Cenek 6/1/2012

18.S34 linear algebra problems (2007)

Mathematics for Computer Science

LINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM

Instructions. 2. Four possible answers are provided for each question and only one of these is correct.

MATRICES The numbers or letters in any given matrix are called its entries or elements

Math 21b: Linear Algebra Spring 2018

2. Linear algebra. matrices and vectors. linear equations. range and nullspace of matrices. function of vectors, gradient and Hessian

NATIONAL BOARD FOR HIGHER MATHEMATICS. M. A. and M.Sc. Scholarship Test. September 22, Time Allowed: 150 Minutes Maximum Marks: 30

Linear algebra I Homework #1 due Thursday, Oct Show that the diagonals of a square are orthogonal to one another.

Math 601 Solutions to Homework 3

Linear algebra II Homework #1 due Thursday, Feb A =

Math 0320 Final Exam Review

Chapter SSM: Linear Algebra Section Fails to be invertible; since det = 6 6 = Invertible; since det = = 2.

MA 1B ANALYTIC - HOMEWORK SET 7 SOLUTIONS

Math 369 Exam #2 Practice Problem Solutions

Math 313 (Linear Algebra) Exam 2 - Practice Exam

Graphing Square Roots - Class Work Graph the following equations by hand. State the domain and range of each using interval notation.

Math 309 Notes and Homework for Days 4-6

MATH 369 Linear Algebra

Math Camp II. Basic Linear Algebra. Yiqing Xu. Aug 26, 2014 MIT

MH1200 Final 2014/2015

Linear Algebra- Final Exam Review

Exercise Sheet 1.

PRACTICE PROBLEMS FOR MIDTERM I

Math 205, Summer I, Week 3a (continued): Chapter 4, Sections 5 and 6. Week 3b. Chapter 4, [Sections 7], 8 and 9

(a) If A is a 3 by 4 matrix, what does this tell us about its nullspace? Solution: dim N(A) 1, since rank(a) 3. Ax =

Diagonalization. MATH 322, Linear Algebra I. J. Robert Buchanan. Spring Department of Mathematics

Exercise Set Suppose that A, B, C, D, and E are matrices with the following sizes: A B C D E

MATH 307 Test 1 Study Guide

Dimension. Eigenvalue and eigenvector

MATH 320: PRACTICE PROBLEMS FOR THE FINAL AND SOLUTIONS

Math 21b Final Exam Thursday, May 15, 2003 Solutions

Review of Some Concepts from Linear Algebra: Part 2

Therefore, A and B have the same characteristic polynomial and hence, the same eigenvalues.

Recall : Eigenvalues and Eigenvectors

Chapter 2. General Vector Spaces. 2.1 Real Vector Spaces

2. Every linear system with the same number of equations as unknowns has a unique solution.

Contents. 2.1 Vectors in R n. Linear Algebra (part 2) : Vector Spaces (by Evan Dummit, 2017, v. 2.50) 2 Vector Spaces

Vector Spaces ปร ภ ม เวกเตอร

Math 315: Linear Algebra Solutions to Assignment 7

Vector Spaces ปร ภ ม เวกเตอร

2.3. VECTOR SPACES 25

MATH 23a, FALL 2002 THEORETICAL LINEAR ALGEBRA AND MULTIVARIABLE CALCULUS Solutions to Final Exam (in-class portion) January 22, 2003

Linear Algebra Exam 1 Spring 2007

LINEAR ALGEBRA (PMTH213) Tutorial Questions

EIGENVALUES AND EIGENVECTORS

Eigenvalues and Eigenvectors

Chapter 2. Vectors and Vector Spaces

0 Sets and Induction. Sets

Remark By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.

Math 520 Exam 2 Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

INNER PRODUCT SPACE. Definition 1

Chain Rule. MATH 311, Calculus III. J. Robert Buchanan. Spring Department of Mathematics

Math 1060 Linear Algebra Homework Exercises 1 1. Find the complete solutions (if any!) to each of the following systems of simultaneous equations:

Math Exam 2, October 14, 2008

The Law of Averages. MARK FLANAGAN School of Electrical, Electronic and Communications Engineering University College Dublin

Department of Mathematics, University of Wisconsin-Madison Math 114 Worksheet Sections 3.1, 3.3, and 3.5

MATH Topics in Applied Mathematics Lecture 12: Evaluation of determinants. Cross product.

Reading Mathematical Expressions & Arithmetic Operations Expression Reads Note

Online Exercises for Linear Algebra XM511

MATH 409 Advanced Calculus I Lecture 12: Uniform continuity. Exponential functions.

Polynomials of small degree evaluated on matrices

MATH 51H Section 4. October 16, Recall what it means for a function between metric spaces to be continuous:

Math 320, spring 2011 before the first midterm

MAC 1140: Test 1 Review, Fall 2017 Exam covers Lectures 1 5, Sections A.1 A.5. II. distance between a and b on the number line is d(a, b) = b a

2, or x 5, 3 x 0, x 2

Web Solutions for How to Read and Do Proofs

Math 207 Honors Calculus III Final Exam Solutions

Math 110, Spring 2015: Midterm Solutions

Math 250B Final Exam Review Session Spring 2015 SOLUTIONS

Differential Geometry qualifying exam 562 January 2019 Show all your work for full credit

LB 220 Homework 4 Solutions

Bishop Kelley High School Summer Math Program Course: Honors Pre-Calculus

Row Space, Column Space, and Nullspace

Determine for which real numbers s the series n>1 (log n)s /n converges, giving reasons for your answer.

There are six more problems on the next two pages

Abstract Vector Spaces

Transcription:

Math 110: Worksheet 1 Solutions August 30 Thursday Aug. 4 1. Determine whether or not the following sets form vector spaces over the given fields. (a) The set V of all matrices of the form where a, b R, over R with standard addition and scalar multiplication. and Note that V is not closed under addition: for a, b, c, d R, we have 1 c but + 1 c ( ) a + c b + d We conclude that V is not a vector space with the given operations. (b) The set V of all matrices of the form where a, b R, over R with addition and scalar multiplication defined by 1 a + c 1 a 1 ka, k. b + k / V. We claim that V is indeed a vector space with the given operations. Note first that ( V) is( closed ) under the addtion and scalar multiplication operations: for, V and k R, we have and k 1 c 1 a + c V b + 1 ka V. k 1

VS 1: Observe that VS : Note that ( ) 1 a + c b + 1 c + a d + 1 c 1 a 1 e f 1 ( defn. of addition) ( comm. of addition in R) 1 a + c 1 e b + f 1 ( ) 1 (a + c) + e (b + d) + f 1 ( ) 1 a + (c + e) b + (d + f) 1 + e d + f 1 ( ( 1 e f 1 1 0 VS 3: The matrix V and acts as the zero vector: 0 1 1 a 1 0 1 a 0 1 1 a 1 a VS 4: Given any V, its additive inverse is as 1 a 1 a 1 0 0 1 VS 5: Observe that 1. VS 6: Let k, l R, we have 1 a 1 (kl)a (kl) ( defn. of scalar mult) (kl) 1 k(la) ( mult. assoc. in R) k(lb) 1 1 la k l ( ) 1 a k l ( defn. of addition) ( addition again) ( add. assoc. in R) ))

VS 7: We have ( ) k VS 8: We have (k + l) 1 a + c k ( addition.) b + ( ) 1 k(a + c) k(b + d) 1 1 ka + kc ( dist. in R) kb + k 1 ka 1 kc k k 1 a 1 c k k 1 (k + l)a (k + l) 1 ka + la kb + l 1 ka 1 la k l ( ) ( 1 a k l ( dist. in R) ( addition.) )) ( 1 a (c) The set V of all positive real numbers over R with addition and scalar multiplication defined by x y xy, a x x a. We show that V is indeed a vector space with the given operations. Note first that if x, y V and a R, we have x y xy V, a x x a V so V is closed under addition and scalar multiplication. VS 1: We have VS : Note that x y xy yx ( mult. comm. in R) y x (x y) z (xy) z (xy)z x(yz) ( mult. assoc. in R) x (yz) x (y z) 3

VS 3: Observe that 1 V and 1 x 1x x. VS 4: For any x V, note that x 1 V so that x x 1 xx 1 1. VS 5: Note that 1 x x 1 x. VS 6: Let a, b R. We then have VS 7: Note that VS 8: We have (ab) x x ab (x b ) a ( exponents in R) a (x b ) a (b x) a (x y) a (xy) (xy) a x a y a ( exponents in R) (x a ) (y a ) (a x) (a y) (a + b) x x a+b x a x b ( exponents in R) (x a ) (x b ) (a x) (b x) (d) The set V of solutions of the differential equation f (t) 4f(t) t, t R over R with standard addition and scalar multiplication. Observe that V is not closed under addition: if f 1, f V, then for all t R so that f 1 (t) 4f 1 (t) t, f (t) 4f (t) t (f 1 + f ) (t) 4(f 1 + f )(t) (f 1 (t) 4f 1 (t)) + (f (t) 4f (t)) t t. We conclude that V is not a vector space with the given operations. 4

(e) The set V of invertible matrices with real entries over R with standard addition and scalar multiplication. 1 0 1 0 Observe that V is not closed under addition: we have, V but 0 1 0 1 1 0 1 0 0 + 0 1 0 1 0 0 is not invertible. We conclude that V is not a vector space with the given operations.. By definition, every field F has a multiplicative identity, an element e such that e x x for every element x F. What is the multiplicative identity for R? Prove that the multiplicative identity is unique for any given field. The multiplicative identity for R is the number 1 as 1 x x for all x R. To show that the identity is unique, let e and e be two identities. Consider then the product e e. By thinking of e as an identity, we have e e e. Likewise, thinking of e as an identity leads to e e e so that e e. Thus, the identity is unique. Tuesday Aug. 9 3. Prove that the set of matrices with zero trace form a subspace of M n n (F ). Does the same hold for matrices with zero determinant? Let T be the set of matrices with zero trace. As M n n (F ) is a vector space over F and T is its subset, we merely need to check three properties: the matrix Z consisting only of zero entries evidently has zero trace so Z T. let A, B T ; it follows then that tr(a) tr(b) 0. Note then that tr(a + B) (A + B) ii i1 A ii + i1 let A T and k F ; we have tr(a) 0 so that B ii tr(a) + tr(b) 0. i1 tr(ka) (ka) ii k i1 A ii ktr(a) 0. i1 We conclude that T is a subspace of M n n (F ). The same cannot be said however about the set of matrices with zero determinant as it is not closed under addition. As an example, let A be the diagonal matrix with A 11 0 and A ii 1 for i,..., n and let B consist only of zeros except for B 11 1. Then, det(a) det(b) 0 but det(a + B) 1 0. 5

4. Let B(R) be the set of all bounded functions on R (A function f is bounded if there exists M such that f(x) M for all x. Thus sin(x) is bounded on R but e x is not). Prove that B(R) is a subspace of F(R, R), the set of all functions from R to R. As F(R, R) is a vector space and B(R) is its subset, we just need to check the following three properties: the function z 0 is clearly bounded (as z(x) 0 < 1 for all x) so z R. let f, g B(R). Then there exist M, N such that f(x) M and g(x) N for all x R. Note then that, by the triangle inequality (f + g)(x) f(x) + g(x) f(x) + g(x) M + N for all x R; thus, (f + g) is bounded and hence in B(R). let f B(R) and a R. Observe then that for all x R so af B(R). (af)(x) af(x) a f(x) a M We conclude that B(R) is a subspace of F(R, R). 5. Let W 1 and W be subspaces of a vector space V. Prove that W 1 + W W if and only if W 1 is a subspace of W. Suppose first that W 1 is a subspace of W. Let t W 1 + W ; there then exist w 1 W 1 and w W such that t w 1 + w. As W 1 is a subspace of W, it follows that w 1 W as well and hence t w 1 + w W so that W 1 + W W. As we also have W W 1 + W, we conclude that W 1 + W W. Conversely, suppose that W 1 + W W ; we want to show that W 1 is a subspace of W. Let w 1 W 1 and w W ; then, w 1 + w W 1 + W. As W 1 + W W, there exists some t W such that w 1 + w t w 1 t + ( w ) W. We conclude that W 1 W and, in particular, that W 1 is a subspace of W. 6. Let v 1 (0, 1) and v (1, 1) and define W 1 {tv 1 : t R} and W {tv : t R}. Also, let V R over R with standard operations. (a) Show that W 1 and W are subspaces of V. As W 1 and W are subsets of V which itself is a vector space, we just need to check the following three properties: (we treat both the spaces at the same time) 0 W i by setting t 0 in the definitions. let x, y W i. There then exist t, s R such that x tv i and y sv i so that x + y tv i + sv i (t + s)v i W i. 6

let x W i and a R. Note then that ax a(tv i ) (at)v i W i. We conclude that both W 1 and W are subspaces of V. (b) Show that V W 1 W. We need to show that (i) W 1 W {0} and (ii) W 1 + W V. For (i), note that if u W 1 W, then for some t, s R, we have u tv 1 and u sv so that tv 1 sv (0, t) (s, s). It follows that s 0 t 0 so u must be the zero vector. For (ii), let x (a, b) R. We want show that x w 1 + w for some w 1 W 1 and w W. Note that setting w 1 (0, b a) and w (a, a) accomplishes this as w i W i for i 1, and w 1 + w (0, b a) + (a, a) (a, b) v. As both (i) and (ii) hold, we conclude that V W 1 W. 7. Let E and O denote respectively the subsets consisting of all the even and odd functions in V : F(R, R). In the homework, you are supposed to show that both E and O are subspaces of V. Assuming that, prove that V E O. As in the previous problem, we just need to show that (i) E O {0} and (ii) E + O V. For (i), let f E O. Then, for any x R, we have f( x) f(x) and f( x) f(x) so that f(x) f(x) f(x) 0 f 0. For (ii), let f V. We need to show that f g + h where g E and h O. Define for all x R f(x) + f( x) f(x) f( x) g(x), h(x). Note then that g(x) + h(x) f(x) for all x. Furthermore, for any x R, we have g( x) f( x) + f(x) g(x) f( x) f(x) h( x) h(x). This shows that g E and h O and hence establishes (ii). V E O. We conclude that 7