APPM 3310 Problem Set 4 Solutions

Similar documents
Chapter 2 Notes, Linear Algebra 5e Lay

Midterm 1 Review. Written by Victoria Kala SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015

Math 54 Homework 3 Solutions 9/

APPENDIX: MATHEMATICAL INDUCTION AND OTHER FORMS OF PROOF

Linear Algebra and Matrix Inversion

Math 4A Notes. Written by Victoria Kala Last updated June 11, 2017

Math 369 Exam #2 Practice Problem Solutions

[3] (b) Find a reduced row-echelon matrix row-equivalent to ,1 2 2

Linear Algebra Exam 1 Spring 2007

Math 2174: Practice Midterm 1

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

Math 54 HW 4 solutions

Chapter 3. Vector spaces

MATH 2360 REVIEW PROBLEMS

Chapter 3: Theory Review: Solutions Math 308 F Spring 2015

Linear Equation: a 1 x 1 + a 2 x a n x n = b. x 1, x 2,..., x n : variables or unknowns

Math 60. Rumbos Spring Solutions to Assignment #17

MATH 431: FIRST MIDTERM. Thursday, October 3, 2013.

ENGINEERING MATH 1 Fall 2009 VECTOR SPACES

Jim Lambers MAT 610 Summer Session Lecture 1 Notes

MATH 2050 Assignment 6 Fall 2018 Due: Thursday, November 1. x + y + 2z = 2 x + y + z = c 4x + 2z = 2

MATH 2030: MATRICES. Example 0.2. Q:Define A 1 =, A. 3 4 A: We wish to find c 1, c 2, and c 3 such that. c 1 + c c

Linear Algebra Practice Problems

Linear Algebra. Hoffman & Kunze. 2nd edition. Answers and Solutions to Problems and Exercises Typos, comments and etc...

Math 314H EXAM I. 1. (28 points) The row reduced echelon form of the augmented matrix for the system. is the matrix

Review Let A, B, and C be matrices of the same size, and let r and s be scalars. Then

Chapter 3. Directions: For questions 1-11 mark each statement True or False. Justify each answer.

APPM 2360 Exam 2 Solutions Wednesday, March 9, 2016, 7:00pm 8:30pm

Linear Algebra Quiz 4. Problem 1 (Linear Transformations): 4 POINTS Show all Work! Consider the tranformation T : R 3 R 3 given by:

The matrix will only be consistent if the last entry of row three is 0, meaning 2b 3 + b 2 b 1 = 0.

web: HOMEWORK 1

4.3 - Linear Combinations and Independence of Vectors

Math 302 Test 1 Review

LS.1 Review of Linear Algebra

Family Feud Review. Linear Algebra. October 22, 2013

Chapter 5. Linear Algebra. A linear (algebraic) equation in. unknowns, x 1, x 2,..., x n, is. an equation of the form

Lecture 11: Vector space and subspace

Math 313 Chapter 1 Review

MATH 2331 Linear Algebra. Section 2.1 Matrix Operations. Definition: A : m n, B : n p. Example: Compute AB, if possible.

Homework For each of the following matrices, find the minimal polynomial and determine whether the matrix is diagonalizable.

2.3. VECTOR SPACES 25

Section 9.2: Matrices. Definition: A matrix A consists of a rectangular array of numbers, or elements, arranged in m rows and n columns.

Review Solutions for Exam 1

MATH2210 Notebook 3 Spring 2018

2018 Fall 2210Q Section 013 Midterm Exam I Solution

3 - Vector Spaces Definition vector space linear space u, v,

Finite Mathematics Chapter 2. where a, b, c, d, h, and k are real numbers and neither a and b nor c and d are both zero.

1. TRUE or FALSE. 2. Find the complete solution set to the system:

MTH 362: Advanced Engineering Mathematics

Instructions Please answer the five problems on your own paper. These are essay questions: you should write in complete sentences.

Review of Matrices and Block Structures

Study Guide for Linear Algebra Exam 2

Vector Space Basics. 1 Abstract Vector Spaces. 1. (commutativity of vector addition) u + v = v + u. 2. (associativity of vector addition)

Math Exam 2, October 14, 2008

Equality: Two matrices A and B are equal, i.e., A = B if A and B have the same order and the entries of A and B are the same.

MATH 315 Linear Algebra Homework #1 Assigned: August 20, 2018

REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW

The definition of a vector space (V, +, )

Spring 2014 Math 272 Final Exam Review Sheet

Chapter 2: Matrix Algebra

A 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

Math 250B Midterm II Review Session Spring 2019 SOLUTIONS

Exam 2 Solutions. (a) Is W closed under addition? Why or why not? W is not closed under addition. For example,

A 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

OHSx XM511 Linear Algebra: Solutions to Online True/False Exercises

LECTURES 14/15: LINEAR INDEPENDENCE AND BASES

Solution to Homework 1

MAT 242 CHAPTER 4: SUBSPACES OF R n

Matrices and systems of linear equations

Basic Concepts in Linear Algebra

Digital Workbook for GRA 6035 Mathematics

Linear Algebra. Min Yan

Lecture 22: Section 4.7

Review : Powers of a matrix

MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS

Chapter 1: Systems of Linear Equations

Extra Problems for Math 2050 Linear Algebra I

BASIC NOTIONS. x + y = 1 3, 3x 5y + z = A + 3B,C + 2D, DC are not defined. A + C =

Homework Set #8 Solutions

Matrix operations Linear Algebra with Computer Science Application

Math 3108: Linear Algebra

Section 2.2: The Inverse of a Matrix

Review of Basic Concepts in Linear Algebra

MATH 240 Spring, Chapter 1: Linear Equations and Matrices

MTH 464: Computational Linear Algebra

Vector Spaces, Orthogonality, and Linear Least Squares

Math Linear Algebra Final Exam Review Sheet

Chapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations

Steven J. Leon University of Massachusetts, Dartmouth

Solution to Set 7, Math 2568

Linear Algebra, Summer 2011, pt. 2

Linear Equations in Linear Algebra

Introduction to Determinants

Fundamentals of Linear Algebra. Marcel B. Finan Arkansas Tech University c All Rights Reserved

Linear Independence x

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

Math 3191 Applied Linear Algebra

Chapter 7. Linear Algebra: Matrices, Vectors,

Solving Linear Systems Using Gaussian Elimination

MTH 464: Computational Linear Algebra

Transcription:

APPM 33 Problem Set 4 Solutions. Problem.. Note: Since these are nonstandard definitions of addition and scalar multiplication, be sure to show that they satisfy all of the vector space axioms. Solution: The space Q is defined by Q = {(x, y) x, y > } R with (x, y ) + (x, y ) = (x x, y y ) and c (x, y) = (x c, y c ). We need to show that Q satisfies the two closure conditions and the seven axioms of a vector space outlined in Definition. of the textbook. (i) Closure under addition: Let (x, y ), (x, y ) Q, i.e. x, y, x, y >. Then (x, y ) + (x, y ) = (x x, y, y ) Q because x, x > x x > and y, y > y y >. (ii) Closure under scalar multiplication: Let (x, y) Q. Then c (x, y) = (x c, y c ) Q because a positive number to raised to any exponent is positive. (a) Commutativity of addition: Since multiplication of real numbers is commutative we have (x, y ) + (x, y ) = (x x, y y ) = (x x, y y ) = (x, y ) + (x, y ) (b) Associativity of addition: Since multiplication of real numbers is associative we have (x, y )+[(x, y ) + (x 3, y 3 )] = [x (x x 3 ), y (y y 3 )] = [(x x ) x 3, (y y ) y 3 ] = [(x, y ) + (x, y )]+(x 3, y 3 ) (c) Additive identity: The zero element is the element (, ). Note that since > the zero element is in the space and we have (x, y) + (, ) = (x, y ) = (x, y) = ( x, y) = (, ) + (x, y) (d) Additive Inverse: The additive inverse of (x, y) Q is (/x, /y). Note that since x, y > we have that /x, /y > and so the additive inverse is also in Q. Remembering that the zero element in Q is (, ) we have (x, y) + (/x, /y) = (x/x, y/y) = (, ) = (/x x, /y y) = (/x, /y) + (x, y) (e) Distributivity: Let c, d R and (x, y ), (x, y ) Q. Then (c + d) (x, y ) = ( ) ( ) x c+d, y c+d = x c x d, yy c d = (x c, y)+ ( ) c x d, y d = c (x, y )+d (x, y )

and also c [(x, y ) + (x, y )] = c (x x, y y ) = ((x x ) c, (y y ) c ) = (x c x c, y c y c ) = (x c, y c ) + (x c, y c ) = c (x, y ) + c (x, y ) (f) Associativity of scalar multiplication: We have c [d (x, y)] = c ( x d, y d) = (( x d) c, ( y d ) c) = ( x cd, y cd) = (cd) (x, y) (g) Unit for scalar multiplication We have (x, y) = ( x, y ) = (x, y)

. Problem.. Solution (a) Not a subspace because it does not contain the zero element: + 4 () + = (b) The set of vectors of the form (t, t, ) T for t R is a subspace. To see this we need to show that it contains the zero element, and is closed under vector addition and scalar multiplication. zero element: Taking t = we have (,, ) T = (,, ) T which is in the space. closure: We want to show that since (t, t, ) T and (s, s, ) T are in the subspace, so is a (t, t, ) T + b (s, s, ) T where a, b R. We have a t t + b s s at + bs at bs r r (c) The set of vectors of the form (r s, r + s, s) T is a subspace. where r = at + bs zero element: Let r = s =, then (r s, r + s, s) T = (,, ) T closure: Consider the two vectors (r s, r + s, s ) T and (r s, r s, s ) T from the space. Then we have a r s r + s s +b r s r + s s which has the form r s r + s s ar as + br bs ar + as + br + bs as bs with r = ar + br and s = as + bs. (d) The set of vectors whose first component is is a subspace. zero element: The zero vector (,, ) T is in the space. (ar + br ) (as + bs ) (ar + br ) + (as + bs ) (as + bs ) closure: Consider (, x, y) T and (, u, v) T from the space. We want to show that for scalars b, c R the vector b (, x, y) T + c (, u, v) T is in the space. We have b x y + c u v which has first element so it s in the space. bx + cu by + cv (e) The space of vectors with last element is not a subspace. To see this note that the zero element (,, ) T is not in the space. It s also does not satisfy either of the closure conditions since, for example, the vectors

are not in the space. + and 3 (f) The set of all vectors (x, y, z) T such that x y z is not a subspace because it is not closed under scalar multiplication. Consider that the vector (3,, ) T is in the space but has 3 < <. 3 (g) The set of all vectors (x, y, z) T such that z = x y forms a subspace. It s easier to see this if we note that all vectors from the space have entries that satisfy z x + y =. zero element: The zero vector (,, ) T is in the space since z x+y = + =. closure: Let (x, y, z ) be such that z x + y = and similarly (x, y, z ) be such that z x + y =. Then for any b, c R we have b x y z + c x y z 3 The resulting vector is in the space because bx + cx by + cy bz + cz 3 (bz + cz ) (bx + cx )+(by + cy ) = b (z x + y )+c (z x + y ) = b +c = (h) The set of all solutions to z = xy does not form a subspace because it does not satisfy either of the closure conditions. Note that (,, ) T and (,, ) T are in the space, but the following are not + since () = 4 3 3 since 3 (3) = 6 (i) The set of all solutions of the equation x + y + z = is a subspace because the only elements of the space is the zero element (,, ) T. Recall that the trivial subspace {} is always a subspace.

(j) The set of all solutions to the system xy = yz = xz is a subspace. To see this note that the only solutions to this system are constant vectors of the form (t, t, t) T. zero element: Clearly (,, ) T is a solution to the system. closure: Let b, c R and consider the two constant vectors (r, r, r) T and (s, s, s) T. Then we have b r r r + c s s s br + cs br + cs br + cs t t t if we let t = br + cs

3. Problem..8 The claim that we wish to prove is an if-and-only-if so we need to prove the implication in both directions. In other words we need to prove the following two conditionals: If the set of all solutions x of Ax = b is a subspace then the system is homogeneous. If the system is homogeneous then the set of all solutions x of Ax = b is a subspace. Proof ( ) Assume that the set of all solutions x of Ax = b is a subspace. Since it is a subspace it must contain the zero element. But if is a solution we have A = so the right-hand side must be and the system is homogeneous. ( ) Assume that the system is homogeneous, i.e. Ax =. To prove that the set of all solutions x to this system is a subspace we need to show that it contains the zero element and satisfies the closure conditions. Clearly the zero element is in the space since A =. For the closure conditions we assume x and y satisfy Ax = and Ay =. Then, for c, d R we have A (cx + dy) = cax + day = c + d = showing that (cx + dy) is in the subspace. 4. Problem.. Proof: We wish to prove that the set of all n n traceless matrices form a subspace of M n n. We need to show that the space contains the zero element and satisfies the closure conditions. zero element: The zero element of M n n is the n n zero matrix. Since every entry of the zero matrix is zero it s diagonal entries are all zero. Since the sum of n zeros is zero we have that the zero matrix is traceless. closure: Let A and B be n n traceless matrices. Then we have tr (A) = a + a + a nn = and tr (B) = b + b + b nn = Let c, d R then tr (ca + db) = (ca + db ) + (ca + db ) + + (ca nn + db nn ) = c (a + a + + a nn ) + d (b + b + + b nn ) = c () + d () =

5. Problem.3. Solution: I m going to save time by doing parts (a) and (c) simultaneously since the result of (c) makes part (b) trivial. (a) and (c): To show that a set of vectors is linearly independent we stack the vectors side-by-side in a matrix A and show that Ax = has only the trivial solution by performing Gaussian Elimination and showing that the reduced system has all nonzero pivots. We also augment the system with a general right-hand side vector to determine conditions on the range of A. a 3 b c d a 3 b 3 3 c a 3 3 d a a 3 b c a b d c + a a 3 b c a b d a b Since we ve reduced to the matrix A to a matrix with three nonzero pivots we know that the vectors we started with are linearly independent. By performing Gaussian Elimination with a general right-hand side vector we know a general condition on vectors in the span of the three vectors. A vector b is in the span of the columns of A if there is a solution to the linear system Ax = b. The system will have a solution provided that the right-hand side vector b makes the system compatible, which we can see from the result of Gaussian Elimination will happen if d c + a =. (b) To see if the following vectors are in the span of the given vectors we could solve attempt to solve the system Ax = b for each vector. We conclude that the vector is in the span if the linear system has a solution. Since above we solved the Ax = b with a general right-hand side we need only check that the given vectors satisfy the resulting compatibility condition d c + a = : (i) For [ ] T we have d c + a = + = so the vector is in the span. (ii) For [ ] T we have d c + a = + = so the vector is not in the span. (iii) For [ ] T we have d c + a = + = so the vector is in the span. (iv) For [ ] T we have d c + a = + = so the vector is in the span. Note that the zero vector is in the span of any set of vectors because we the trivial solution is always a solution of Ax =.

6. Problem.3.8 Proof: It is much easier to prove the following equivalent contrapositive biconditional statement: u and v are linearly dependent if and only if ad bc =. Assume that u and v are linearly dependent. Then there exist nonzero scalars α and β such that αu + βv =. Then = αu + βv = α (ax + by) + β (cx + dy) = (αa + βc) x + (αb + βd) y Since x and y are linearly independent by assumption, this equality holds if and only if the coefficients in front of x and y are zero. Since α and β are nonzero this is true if and only if ad bc =. To see this let α = d and β = b, then = (αa + βc) x + (αb + βd) y = (ad bc) x Alternatively we could take α = c and β = a, to get = (αa + βc) x + (αb + βd) y = ( bc + ad) y which confirms the claim.

7. Each of the following statements is either true or false. If the statement is true, please provide a proof or some other sufficient justification. If it is false, explain why it is false or give a counterexample. (a) An interval is a vector space. Solution The statement is False. Consider the interval [a, b]. If is not in the interval we re done because the zero element is not in the space. If is in the interval [a, b] then we note that b + b = b is not in [a, b] so the interval is not closed under addition. Similarly we could note that 3b is not in the interval so [a, b] is not closed under scalar multiplication. (b) The set of all real n n nonsingular matrices is a subspace of M n n. Solution: The statement is False. The set of all nonsingular matrices is not a subspace of M n n for multiple reasons. First note that the zero element of M n n is the zero matrix, which is singular. Alternatively, we could note that the n n identity matrix I is nonsingular, and so is it s negative, I. But I + ( I) = and the zero matrix is singular, so the space is not closed under additon. (c) The set of all real n n symmetric matrices is a subspace of M n n. Solution: The statement is True. Note that the transpose of the n n zero matrix is T = so the space contains the zero element. To show closure we assume that A and B are symmetric and that c, d R. Then (ca + db) T = ca T + db T = ca + db () where here () follows from the assumption that A and B are symmetric. Thus the space of symmetric matrices is closed under addition and scalar multiplication. (d) If v, v,..., v k are elements of a vector space V and do not span V, then v, v,..., v k are linearly independent. Solution The statement is (super duper) False. Consider the following three vectors from R 3 : v = e, v = e, v 3 = 3e, where here e is the first cannoncial basis vector of R 3. Clearly these vectors do not span R 3 but they are linearly dependent since they are each scalar multiples of each other.