BASIS AND DIMENSION. Budi Murtiyasa Muhammadiyah University of Surakarta

Size: px
Start display at page:

Download "BASIS AND DIMENSION. Budi Murtiyasa Muhammadiyah University of Surakarta"

Transcription

1 BASIS AND DIMENSION Budi Murtiyasa Muhammadiyah University of Surakarta

2 Plan Linearly dependence and Linearly Independece Basis and Dimension Sum Space and Intersection Space Row/Column Space

3 Linearly Dependence and Linearly Independence

4 linearly dependence and Linearly Independence A vector space V over field F. Vectors u, u, u,, u n V called linearly dependence (bergantung linear) or simply dependent if there are exist scalars a, a, a,, a n F which not all zero such that : a u + a u + + a n u n =

5 From, a u + a u + + a n u n = if it is only satisfy for all a i = (a = a = = a n = ), then vectors u, u, u,, u n V called linearly independence (bebas linear) or simply independent.

6 vectors u, v, w R, where : u =, v =, and w = 6. evaluate, the vectors dependent or independent?. solution: x u + y v + z w = -x + y + 5z = x y 6z = x + y z = 5 It can obtained : x = -, y =, and z = - thus : - u + v w = There are exist a scalars not zero, so vectors u, v, and w are dependent.

7 solution: (using a matrices) w v u = 6 5 u w u v u 5 = ) ( ) 5 ( u v u w u v u = 4 4 From the row echelon form, the last row can be read as : (w + 5u) (v + u) = or : u v + w = There are exist a scalars not zero, so vectors u, v, and w are dependent. Observe that the echelon form have a zero row.

8 Vectors u, v, w R, where : u =, v =, and w =. evaluate, the vectors dependent or independent?. solution: x u + y v + z w = x + y z = -x + y + z = x y z = Its only obtained: x =, y =, and z = thus: u + v + w = There are only a scalar zero (), So vectors u, v, and w are independent.

9 Solution: (using a matrices) w v u = u w u v u = 6 ) ( 6 ) ( u v u w u v u = 6 From the row echelon form, there aren t zero row. Vectors u, v, and w are independent. Observe that the echelon form haven t a zero row.

10 Theorem The nonzero rows of the echelon form of matrices are independent.

11 Theorem A vectors u, u, u,, u n V called dependent if one of the vectors can be expressed as linear combination of the other vectors.

12 Notes : If u =, then u must be dependent. If u, then u must be independent. A set of vectors that containing zero vector (), is dependent. A set of vector that containing two vectors which is same or multiplicative, it is dependent. Suppose U V. If U dependent, then V dependent. Suppose W V. If V independent, then W independent. Geometrically, two vectors which is dependent lies on the same line ( same plane).

13 two vectors dependence y v kv x

14 Basis and Dimension

15 Basis and Dimension a vector space V have finite dimensional n (denote dim V = n) if there are independent vectors e, e,, e n V. Set of { e, e,, e n } called basis of V; and number of independent vectors is maximum n. // budi murtiyasa ums surakarta 5

16 Suppose V = {u, u, u }, where - u = u = - and u = - observe that set {u, u, u } is dependent; hence, set {u, u, u } is not basis of V. But, set {u, u } is independent. Thus, set {u, u } is basis of V. Hence, dim V =. Besides, set {u, u } is also independent, so set {u, u } is basis of V, and dim V =. From the above discussion, it was shown that basis of a vector space is not unique. // budi murtiyasa ums surakarta 6

17 Because of The nonzero rows of the echelon form of matrices are independent, so algorithm to determine a basis can be done as follow.. Develop a matrix M which rows are the given vectors. Reduce M to echelon form. The non zero rows of the echelon form matrices are the basis.

18 Suppose a vector space V = {u, v, w, s}, where: u =, v =, w =, and s =. find basis and dimension of V! solution: (using matrices) = ~ ~ Basis of V = {(-,, ) T, (, -, ) T }. Dim V =. 5 8 s w v u // 8 budi murtiyasa ums surakarta

19 Find basis and dimension of V = {u, v, w}; if // budi murtiyasa ums surakarta 9

20 For R n ; with e =, e =,, e n = are basis of R n, and dim R n = n. Basis {e, e,, e n } called natural basis or standard basis. So, natural basis of R are e = and e =. Dim R =. : : : // budi murtiyasa ums surakarta

21 Theorem Set {u, u,, u n } which is independent from a n-dimensional vector space V is generating system for the vector space V. // budi murtiyasa ums surakarta

22 Notes : Every generating system which is independent is a basis of a vector space. every set {u, u,, u n } which is independent is a basis of n-dimensional vector space. // budi murtiyasa ums surakarta

23 example: u, v, w R, with u =, v =, w =. it can be verified that {u, v, w} is dependent; it is also generating system for R. but, set {u, v, w} is not basis for R. meanwhile, set {u, w} is independent, it is also generating system for R. thus, set {u, w} is basis of R. // budi murtiyasa ums surakarta

24 Theorem If there is n-dimensional vector space V, then every set that contains n+ (or more) of vectors are dependent. // budi murtiyasa ums surakarta 4

25 example: u, v, w R, with u =, v =, w =. it can be verified that set {u, v, w} is dependent; why?. - V = Dependent or Independent? // budi murtiyasa ums surakarta 5

26 Application on Homogenous System Remember for homogenous system AX = which n variables. Dimension the solution space of AX = is n r, where r is rank of coefficient matrix A.

27 7 Find Basis and dimension for solution space of the system: -x + x x + x 4 = x x + x x 4 = x x + x 8x 4 = Solution: A = 8 ~ 6 ~ r(a) = n = 4 Number free variables= n r = 4 = Free variable are: x and x 4 New equation: - x + x x + x 4 = x x 4 = Suppose x = α, and x 4 = β With α, β are Real number x x 4 = x β = x = - β -x + x x + x 4 = -x + α (-β) + β = x = α + 7β General solution: Basis for the solution space are : x x x x 7 and The dimension = n r = 4 =.

28 Sum Space

29 Sum Space If U and W are subspace of V, sum space of U and W are U + W = {u+w u U and w W}. // budi murtiyasa ums surakarta 9

30 Find basis and dimension of U + W // budi murtiyasa ums surakarta

31 Solution : U + W = To find basis of U + W, it can be done as follow : // budi murtiyasa ums surakarta

32 Basis U+W ={(,-,,)T,(,-,5,4)T,(,,,)T,(,,,)T} Dim U+W = 4. ~ ~ ~ ~ // budi murtiyasa ums surakarta H

33 Theorem If U and W are subspace of V, then U+W are subspace of V. // budi murtiyasa ums surakarta

34 -- // 4 budi murtiyasa ums surakarta Suppose U and W subspace of V = R 4, where : U= { a + b c = ; a, b, c, d ϵ R} W= { a d = ; b + c = ; a,b,c,d ϵ R} Find basis and dimension of : (i) U (ii) W (iii) U W (iv) U + W d c b a d c b a

35 Observe that : dim(u + W) = dim(u) + dim(w) dim (U W)

36 Direct Sum Suppose U and W are subspace of V. The vector space V is direct sum of U and W, denote U W, if every v V can be expressed by one way and the only as v = u + w; where u U and w W. // budi murtiyasa ums surakarta 6

37 Suppose U and W are subspace of V, where a U = b and W = c a V = b is direct sum of U and W, c 4 4 for example : 8 = // budi murtiyasa ums surakarta 7

38 Suppose U and W are subspace of V, where a a U = b and W = b, then V = b c c is not direct sum of U and W, because 4 4 For example : 8 = 5 + or = + 6 etc. 7 7 // budi murtiyasa ums surakarta 8

39 Theorem a vector space V is called direct sum of subspace U and W if and only if () U + W = V, and () U W = { }. // budi murtiyasa ums surakarta 9

40 Row/Column Space

41 Row space and Coloumn Space For a mxn matrix A, then dimension of row space = dimension of coloumn space. // budi murtiyasa ums surakarta 4

42 Dimension of row space: ~ H 6 6 ~ ~ There are two rows that non zero, its mean the dimension =. Dimension of coloumn space: ~ K 6 6 ~ 6 ~ There are two coloumns that non zero, its mean, the dimension =. // 4 budi murtiyasa ums surakarta

AFFINE AND PROJECTIVE GEOMETRY, E. Rosado & S.L. Rueda 4. BASES AND DIMENSION

AFFINE AND PROJECTIVE GEOMETRY, E. Rosado & S.L. Rueda 4. BASES AND DIMENSION 4. BASES AND DIMENSION Definition Let u 1,..., u n be n vectors in V. The vectors u 1,..., u n are linearly independent if the only linear combination of them equal to the zero vector has only zero scalars;

More information

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

The definition of a vector space (V, +, ) The definition of a vector space (V, +, ) 1. For any u and v in V, u + v is also in V. 2. For any u and v in V, u + v = v + u. 3. For any u, v, w in V, u + ( v + w) = ( u + v) + w. 4. There is an element

More information

Linear Independence x

Linear Independence x Linear Independence A consistent system of linear equations with matrix equation Ax = b, where A is an m n matrix, has a solution set whose graph in R n is a linear object, that is, has one of only n +

More information

Lecture 22: Section 4.7

Lecture 22: Section 4.7 Lecture 22: Section 47 Shuanglin Shao December 2, 213 Row Space, Column Space, and Null Space Definition For an m n, a 11 a 12 a 1n a 21 a 22 a 2n A = a m1 a m2 a mn, the vectors r 1 = [ a 11 a 12 a 1n

More information

YORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics MATH M Test #1. July 11, 2013 Solutions

YORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics MATH M Test #1. July 11, 2013 Solutions YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH 222 3. M Test # July, 23 Solutions. For each statement indicate whether it is always TRUE or sometimes FALSE. Note: For

More information

MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix.

MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix. MATH 323 Linear Algebra Lecture 12: Basis of a vector space (continued). Rank and nullity of a matrix. Basis Definition. Let V be a vector space. A linearly independent spanning set for V is called a basis.

More information

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

Chapter 3. Directions: For questions 1-11 mark each statement True or False. Justify each answer. Chapter 3 Directions: For questions 1-11 mark each statement True or False. Justify each answer. 1. (True False) Asking whether the linear system corresponding to an augmented matrix [ a 1 a 2 a 3 b ]

More information

Row Space, Column Space, and Nullspace

Row Space, Column Space, and Nullspace Row Space, Column Space, and Nullspace MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 2015 Introduction Every matrix has associated with it three vector spaces: row space

More information

MAT 242 CHAPTER 4: SUBSPACES OF R n

MAT 242 CHAPTER 4: SUBSPACES OF R n MAT 242 CHAPTER 4: SUBSPACES OF R n JOHN QUIGG 1. Subspaces Recall that R n is the set of n 1 matrices, also called vectors, and satisfies the following properties: x + y = y + x x + (y + z) = (x + y)

More information

Overview. Motivation for the inner product. Question. Definition

Overview. Motivation for the inner product. Question. Definition Overview Last time we studied the evolution of a discrete linear dynamical system, and today we begin the final topic of the course (loosely speaking) Today we ll recall the definition and properties of

More information

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

2. Every linear system with the same number of equations as unknowns has a unique solution. 1. For matrices A, B, C, A + B = A + C if and only if A = B. 2. Every linear system with the same number of equations as unknowns has a unique solution. 3. Every linear system with the same number of equations

More information

SECTION 3.3. PROBLEM 22. The null space of a matrix A is: N(A) = {X : AX = 0}. Here are the calculations of AX for X = a,b,c,d, and e. =

SECTION 3.3. PROBLEM 22. The null space of a matrix A is: N(A) = {X : AX = 0}. Here are the calculations of AX for X = a,b,c,d, and e. = SECTION 3.3. PROBLEM. The null space of a matrix A is: N(A) {X : AX }. Here are the calculations of AX for X a,b,c,d, and e. Aa [ ][ ] 3 3 [ ][ ] Ac 3 3 [ ] 3 3 [ ] 4+4 6+6 Ae [ ], Ab [ ][ ] 3 3 3 [ ]

More information

Math 3C Lecture 25. John Douglas Moore

Math 3C Lecture 25. John Douglas Moore Math 3C Lecture 25 John Douglas Moore June 1, 2009 Let V be a vector space. A basis for V is a collection of vectors {v 1,..., v k } such that 1. V = Span{v 1,..., v k }, and 2. {v 1,..., v k } are linearly

More information

Department of Aerospace Engineering AE602 Mathematics for Aerospace Engineers Assignment No. 4

Department of Aerospace Engineering AE602 Mathematics for Aerospace Engineers Assignment No. 4 Department of Aerospace Engineering AE6 Mathematics for Aerospace Engineers Assignment No.. Decide whether or not the following vectors are linearly independent, by solving c v + c v + c 3 v 3 + c v :

More information

(i) [7 points] Compute the determinant of the following matrix using cofactor expansion.

(i) [7 points] Compute the determinant of the following matrix using cofactor expansion. Question (i) 7 points] Compute the determinant of the following matrix using cofactor expansion 2 4 2 4 2 Solution: Expand down the second column, since it has the most zeros We get 2 4 determinant = +det

More information

Review Notes for Linear Algebra True or False Last Updated: February 22, 2010

Review Notes for Linear Algebra True or False Last Updated: February 22, 2010 Review Notes for Linear Algebra True or False Last Updated: February 22, 2010 Chapter 4 [ Vector Spaces 4.1 If {v 1,v 2,,v n } and {w 1,w 2,,w n } are linearly independent, then {v 1 +w 1,v 2 +w 2,,v n

More information

We showed that adding a vector to a basis produces a linearly dependent set of vectors; more is true.

We showed that adding a vector to a basis produces a linearly dependent set of vectors; more is true. Dimension We showed that adding a vector to a basis produces a linearly dependent set of vectors; more is true. Lemma If a vector space V has a basis B containing n vectors, then any set containing more

More information

Math 369 Exam #2 Practice Problem Solutions

Math 369 Exam #2 Practice Problem Solutions Math 369 Exam #2 Practice Problem Solutions 2 5. Is { 2, 3, 8 } a basis for R 3? Answer: No, it is not. To show that it is not a basis, it suffices to show that this is not a linearly independent set.

More information

Chapter 1. Vectors, Matrices, and Linear Spaces

Chapter 1. Vectors, Matrices, and Linear Spaces 1.6 Homogeneous Systems, Subspaces and Bases 1 Chapter 1. Vectors, Matrices, and Linear Spaces 1.6. Homogeneous Systems, Subspaces and Bases Note. In this section we explore the structure of the solution

More information

Lecture 9: Vector Algebra

Lecture 9: Vector Algebra Lecture 9: Vector Algebra Linear combination of vectors Geometric interpretation Interpreting as Matrix-Vector Multiplication Span of a set of vectors Vector Spaces and Subspaces Linearly Independent/Dependent

More information

Linear independence, span, basis, dimension - and their connection with linear systems

Linear independence, span, basis, dimension - and their connection with linear systems Linear independence span basis dimension - and their connection with linear systems Linear independence of a set of vectors: We say the set of vectors v v..v k is linearly independent provided c v c v..c

More information

Abstract Vector Spaces

Abstract Vector Spaces CHAPTER 1 Abstract Vector Spaces 1.1 Vector Spaces Let K be a field, i.e. a number system where you can add, subtract, multiply and divide. In this course we will take K to be R, C or Q. Definition 1.1.

More information

MATH 304 Linear Algebra Lecture 20: Review for Test 1.

MATH 304 Linear Algebra Lecture 20: Review for Test 1. MATH 304 Linear Algebra Lecture 20: Review for Test 1. Topics for Test 1 Part I: Elementary linear algebra (Leon 1.1 1.4, 2.1 2.2) Systems of linear equations: elementary operations, Gaussian elimination,

More information

Solutions of Linear system, vector and matrix equation

Solutions of Linear system, vector and matrix equation Goals: Solutions of Linear system, vector and matrix equation Solutions of linear system. Vectors, vector equation. Matrix equation. Math 112, Week 2 Suggested Textbook Readings: Sections 1.3, 1.4, 1.5

More information

6.4 BASIS AND DIMENSION (Review) DEF 1 Vectors v 1, v 2,, v k in a vector space V are said to form a basis for V if. (a) v 1,, v k span V and

6.4 BASIS AND DIMENSION (Review) DEF 1 Vectors v 1, v 2,, v k in a vector space V are said to form a basis for V if. (a) v 1,, v k span V and 6.4 BASIS AND DIMENSION (Review) DEF 1 Vectors v 1, v 2,, v k in a vector space V are said to form a basis for V if (a) v 1,, v k span V and (b) v 1,, v k are linearly independent. HMHsueh 1 Natural Basis

More information

1. Let m 1 and n 1 be two natural numbers such that m > n. Which of the following is/are true?

1. Let m 1 and n 1 be two natural numbers such that m > n. Which of the following is/are true? . Let m and n be two natural numbers such that m > n. Which of the following is/are true? (i) A linear system of m equations in n variables is always consistent. (ii) A linear system of n equations in

More information

Study Guide for Linear Algebra Exam 2

Study Guide for Linear Algebra Exam 2 Study Guide for Linear Algebra Exam 2 Term Vector Space Definition A Vector Space is a nonempty set V of objects, on which are defined two operations, called addition and multiplication by scalars (real

More information

Row Space and Column Space of a Matrix

Row Space and Column Space of a Matrix Row Space and Column Space of a Matrix 1/18 Summary: To a m n matrix A = (a ij ), we can naturally associate subspaces of K n and of K m, called the row space of A and the column space of A, respectively.

More information

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

1. TRUE or FALSE. 2. Find the complete solution set to the system: TRUE or FALSE (a A homogenous system with more variables than equations has a nonzero solution True (The number of pivots is going to be less than the number of columns and therefore there is a free variable

More information

CSL361 Problem set 4: Basic linear algebra

CSL361 Problem set 4: Basic linear algebra CSL361 Problem set 4: Basic linear algebra February 21, 2017 [Note:] If the numerical matrix computations turn out to be tedious, you may use the function rref in Matlab. 1 Row-reduced echelon matrices

More information

Test 3, Linear Algebra

Test 3, Linear Algebra Test 3, Linear Algebra Dr. Adam Graham-Squire, Fall 2017 Name: I pledge that I have neither given nor received any unauthorized assistance on this exam. (signature) DIRECTIONS 1. Don t panic. 2. Show all

More information

MATH 2360 REVIEW PROBLEMS

MATH 2360 REVIEW PROBLEMS MATH 2360 REVIEW PROBLEMS Problem 1: In (a) (d) below, either compute the matrix product or indicate why it does not exist: ( )( ) 1 2 2 1 (a) 0 1 1 2 ( ) 0 1 2 (b) 0 3 1 4 3 4 5 2 5 (c) 0 3 ) 1 4 ( 1

More information

Determine whether the following system has a trivial solution or non-trivial solution:

Determine whether the following system has a trivial solution or non-trivial solution: Practice Questions Lecture # 7 and 8 Question # Determine whether the following system has a trivial solution or non-trivial solution: x x + x x x x x The coefficient matrix is / R, R R R+ R The corresponding

More information

A = u + V. u + (0) = u

A = u + V. u + (0) = u Recall: Last time we defined an affine subset of R n to be a subset of the form A = u + V = {u + v u R n,v V } where V is a subspace of R n We said that we would use the notation A = {u,v } to indicate

More information

MTH 362: Advanced Engineering Mathematics

MTH 362: Advanced Engineering Mathematics MTH 362: Advanced Engineering Mathematics Lecture 5 Jonathan A. Chávez Casillas 1 1 University of Rhode Island Department of Mathematics September 26, 2017 1 Linear Independence and Dependence of Vectors

More information

Math 313 Chapter 5 Review

Math 313 Chapter 5 Review Math 313 Chapter 5 Review Howard Anton, 9th Edition May 2010 Do NOT write on me! Contents 1 5.1 Real Vector Spaces 2 2 5.2 Subspaces 3 3 5.3 Linear Independence 4 4 5.4 Basis and Dimension 5 5 5.5 Row

More information

NAME MATH 304 Examination 2 Page 1

NAME MATH 304 Examination 2 Page 1 NAME MATH 4 Examination 2 Page. [8 points (a) Find the following determinant. However, use only properties of determinants, without calculating directly (that is without expanding along a column or row

More information

1. What is the determinant of the following matrix? a 1 a 2 4a 3 2a 2 b 1 b 2 4b 3 2b c 1. = 4, then det

1. What is the determinant of the following matrix? a 1 a 2 4a 3 2a 2 b 1 b 2 4b 3 2b c 1. = 4, then det What is the determinant of the following matrix? 3 4 3 4 3 4 4 3 A 0 B 8 C 55 D 0 E 60 If det a a a 3 b b b 3 c c c 3 = 4, then det a a 4a 3 a b b 4b 3 b c c c 3 c = A 8 B 6 C 4 D E 3 Let A be an n n matrix

More information

Vector Spaces 4.5 Basis and Dimension

Vector Spaces 4.5 Basis and Dimension Vector Spaces 4.5 and Dimension Summer 2017 Vector Spaces 4.5 and Dimension Goals Discuss two related important concepts: Define of a Vectors Space V. Define Dimension dim(v ) of a Vectors Space V. Vector

More information

2018 Fall 2210Q Section 013 Midterm Exam II Solution

2018 Fall 2210Q Section 013 Midterm Exam II Solution 08 Fall 0Q Section 0 Midterm Exam II Solution True or False questions points 0 0 points) ) Let A be an n n matrix. If the equation Ax b has at least one solution for each b R n, then the solution is unique

More information

χ 1 χ 2 and ψ 1 ψ 2 is also in the plane: αχ 1 αχ 2 which has a zero first component and hence is in the plane. is also in the plane: ψ 1

χ 1 χ 2 and ψ 1 ψ 2 is also in the plane: αχ 1 αχ 2 which has a zero first component and hence is in the plane. is also in the plane: ψ 1 58 Chapter 5 Vector Spaces: Theory and Practice 57 s Which of the following subsets of R 3 are actually subspaces? (a The plane of vectors x =(χ,χ, T R 3 such that the first component χ = In other words,

More information

Linear Algebra problems

Linear Algebra problems Linear Algebra problems 1. Show that the set F = ({1, 0}, +,.) is a field where + and. are defined as 1+1=0, 0+0=0, 0+1=1+0=1, 0.0=0.1=1.0=0, 1.1=1.. Let X be a non-empty set and F be any field. Let X

More information

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

Instructions Please answer the five problems on your own paper. These are essay questions: you should write in complete sentences. Instructions Please answer the five problems on your own paper. These are essay questions: you should write in complete sentences.. Recall that P 3 denotes the vector space of polynomials of degree less

More information

MATH 2331 Linear Algebra. Section 1.1 Systems of Linear Equations. Finding the solution to a set of two equations in two variables: Example 1: Solve:

MATH 2331 Linear Algebra. Section 1.1 Systems of Linear Equations. Finding the solution to a set of two equations in two variables: Example 1: Solve: MATH 2331 Linear Algebra Section 1.1 Systems of Linear Equations Finding the solution to a set of two equations in two variables: Example 1: Solve: x x = 3 1 2 2x + 4x = 12 1 2 Geometric meaning: Do these

More information

Chapter 2 Subspaces of R n and Their Dimensions

Chapter 2 Subspaces of R n and Their Dimensions Chapter 2 Subspaces of R n and Their Dimensions Vector Space R n. R n Definition.. The vector space R n is a set of all n-tuples (called vectors) x x 2 x =., where x, x 2,, x n are real numbers, together

More information

Linear Algebra, Spring 2005

Linear Algebra, Spring 2005 Linear Algebra, Spring 2005 Solutions May 4, 2005 Problem 4.89 To check for linear independence write the vectors as rows of a matrix. Reduce the matrix to echelon form and determine the number of non-zero

More information

(b) If a multiple of one row of A is added to another row to produce B then det(b) =det(a).

(b) If a multiple of one row of A is added to another row to produce B then det(b) =det(a). .(5pts) Let B = 5 5. Compute det(b). (a) (b) (c) 6 (d) (e) 6.(5pts) Determine which statement is not always true for n n matrices A and B. (a) If two rows of A are interchanged to produce B, then det(b)

More information

Homework 5 M 373K Mark Lindberg and Travis Schedler

Homework 5 M 373K Mark Lindberg and Travis Schedler Homework 5 M 373K Mark Lindberg and Travis Schedler 1. Artin, Chapter 3, Exercise.1. Prove that the numbers of the form a + b, where a and b are rational numbers, form a subfield of C. Let F be the numbers

More information

What is on this week. 1 Vector spaces (continued) 1.1 Null space and Column Space of a matrix

What is on this week. 1 Vector spaces (continued) 1.1 Null space and Column Space of a matrix Professor Joana Amorim, jamorim@bu.edu What is on this week Vector spaces (continued). Null space and Column Space of a matrix............................. Null Space...........................................2

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. est Review-Linear Algebra Name MULIPLE CHOICE Choose the one alternative that best completes the statement or answers the question Solve the system of equations ) 7x + 7 + x + + 9x + + 9 9 (-,, ) (, -,

More information

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

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

More information

MA 511, Session 10. The Four Fundamental Subspaces of a Matrix

MA 511, Session 10. The Four Fundamental Subspaces of a Matrix MA 5, Session The Four Fundamental Subspaces of a Matrix Let A be a m n matrix. (i) The row space C(A T )ofais the subspace of R n spanned by the rows of A. (ii) The null space N (A) ofa is the subspace

More information

is Use at most six elementary row operations. (Partial

is Use at most six elementary row operations. (Partial MATH 235 SPRING 2 EXAM SOLUTIONS () (6 points) a) Show that the reduced row echelon form of the augmented matrix of the system x + + 2x 4 + x 5 = 3 x x 3 + x 4 + x 5 = 2 2x + 2x 3 2x 4 x 5 = 3 is. Use

More information

Solutions to Homework 5 - Math 3410

Solutions to Homework 5 - Math 3410 Solutions to Homework 5 - Math 34 (Page 57: # 489) Determine whether the following vectors in R 4 are linearly dependent or independent: (a) (, 2, 3, ), (3, 7,, 2), (, 3, 7, 4) Solution From x(, 2, 3,

More information

1 Last time: inverses

1 Last time: inverses MATH Linear algebra (Fall 8) Lecture 8 Last time: inverses The following all mean the same thing for a function f : X Y : f is invertible f is one-to-one and onto 3 For each b Y there is exactly one a

More information

DEF 1 Let V be a vector space and W be a nonempty subset of V. If W is a vector space w.r.t. the operations, in V, then W is called a subspace of V.

DEF 1 Let V be a vector space and W be a nonempty subset of V. If W is a vector space w.r.t. the operations, in V, then W is called a subspace of V. 6.2 SUBSPACES DEF 1 Let V be a vector space and W be a nonempty subset of V. If W is a vector space w.r.t. the operations, in V, then W is called a subspace of V. HMHsueh 1 EX 1 (Ex. 1) Every vector space

More information

1. Determine by inspection which of the following sets of vectors is linearly independent. 3 3.

1. Determine by inspection which of the following sets of vectors is linearly independent. 3 3. 1. Determine by inspection which of the following sets of vectors is linearly independent. (a) (d) 1, 3 4, 1 { [ [,, 1 1] 3]} (b) 1, 4 5, (c) 3 6 (e) 1, 3, 4 4 3 1 4 Solution. The answer is (a): v 1 is

More information

Solutions to Final Exam

Solutions to Final Exam Solutions to Final Exam. Let A be a 3 5 matrix. Let b be a nonzero 5-vector. Assume that the nullity of A is. (a) What is the rank of A? 3 (b) Are the rows of A linearly independent? (c) Are the columns

More information

Vector Spaces. (1) Every vector space V has a zero vector 0 V

Vector Spaces. (1) Every vector space V has a zero vector 0 V Vector Spaces 1. Vector Spaces A (real) vector space V is a set which has two operations: 1. An association of x, y V to an element x+y V. This operation is called vector addition. 2. The association of

More information

Math 1553, Introduction to Linear Algebra

Math 1553, Introduction to Linear Algebra Learning goals articulate what students are expected to be able to do in a course that can be measured. This course has course-level learning goals that pertain to the entire course, and section-level

More information

Chapter 1 Vector Spaces

Chapter 1 Vector Spaces Chapter 1 Vector Spaces Per-Olof Persson persson@berkeley.edu Department of Mathematics University of California, Berkeley Math 110 Linear Algebra Vector Spaces Definition A vector space V over a field

More information

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

MATH 2331 Linear Algebra. Section 2.1 Matrix Operations. Definition: A : m n, B : n p. Example: Compute AB, if possible. MATH 2331 Linear Algebra Section 2.1 Matrix Operations Definition: A : m n, B : n p ( 1 2 p ) ( 1 2 p ) AB = A b b b = Ab Ab Ab Example: Compute AB, if possible. 1 Row-column rule: i-j-th entry of AB:

More information

MATH 225 Summer 2005 Linear Algebra II Solutions to Assignment 1 Due: Wednesday July 13, 2005

MATH 225 Summer 2005 Linear Algebra II Solutions to Assignment 1 Due: Wednesday July 13, 2005 MATH 225 Summer 25 Linear Algebra II Solutions to Assignment 1 Due: Wednesday July 13, 25 Department of Mathematical and Statistical Sciences University of Alberta Question 1. [p 224. #2] The set of all

More information

Linear vector spaces and subspaces.

Linear vector spaces and subspaces. Math 2051 W2008 Margo Kondratieva Week 1 Linear vector spaces and subspaces. Section 1.1 The notion of a linear vector space. For the purpose of these notes we regard (m 1)-matrices as m-dimensional vectors,

More information

MA 242 LINEAR ALGEBRA C1, Solutions to First Midterm Exam

MA 242 LINEAR ALGEBRA C1, Solutions to First Midterm Exam MA 242 LINEAR ALGEBRA C Solutions to First Midterm Exam Prof Nikola Popovic October 2 9:am - :am Problem ( points) Determine h and k such that the solution set of x + = k 4x + h = 8 (a) is empty (b) contains

More information

Linear Algebra 1 Exam 1 Solutions 6/12/3

Linear Algebra 1 Exam 1 Solutions 6/12/3 Linear Algebra 1 Exam 1 Solutions 6/12/3 Question 1 Consider the linear system in the variables (x, y, z, t, u), given by the following matrix, in echelon form: 1 2 1 3 1 2 0 1 1 3 1 4 0 0 0 1 2 3 Reduce

More information

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

Midterm 1 Review. Written by Victoria Kala SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015 Midterm 1 Review Written by Victoria Kala vtkala@math.ucsb.edu SH 6432u Office Hours: R 12:30 1:30 pm Last updated 10/10/2015 Summary This Midterm Review contains notes on sections 1.1 1.5 and 1.7 in your

More information

3.4 Elementary Matrices and Matrix Inverse

3.4 Elementary Matrices and Matrix Inverse Math 220: Summer 2015 3.4 Elementary Matrices and Matrix Inverse A n n elementary matrix is a matrix which is obtained from the n n identity matrix I n n by a single elementary row operation. Elementary

More information

Math 290, Midterm II-key

Math 290, Midterm II-key Math 290, Midterm II-key Name (Print): (first) Signature: (last) The following rules apply: There are a total of 20 points on this 50 minutes exam. This contains 7 pages (including this cover page) and

More information

MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS

MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS MATH 221: SOLUTIONS TO SELECTED HOMEWORK PROBLEMS 1. HW 1: Due September 4 1.1.21. Suppose v, w R n and c is a scalar. Prove that Span(v + cw, w) = Span(v, w). We must prove two things: that every element

More information

Linear Algebra MATH20F Midterm 1

Linear Algebra MATH20F Midterm 1 University of California San Diego NAME TA: Linear Algebra Wednesday, October st, 9 :am - :5am No aids are allowed Be sure to write all row operations used Remember that you can often check your answers

More information

MA 0540 fall 2013, Row operations on matrices

MA 0540 fall 2013, Row operations on matrices MA 0540 fall 2013, Row operations on matrices December 2, 2013 This is all about m by n matrices (of real or complex numbers). If A is such a matrix then A corresponds to a linear map F n F m, which we

More information

Lecture 6: Spanning Set & Linear Independency

Lecture 6: Spanning Set & Linear Independency Lecture 6: Elif Tan Ankara University Elif Tan (Ankara University) Lecture 6 / 0 Definition (Linear Combination) Let v, v 2,..., v k be vectors in (V,, ) a vector space. A vector v V is called a linear

More information

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

Linear algebra I Homework #1 due Thursday, Oct Show that the diagonals of a square are orthogonal to one another. Homework # due Thursday, Oct. 0. Show that the diagonals of a square are orthogonal to one another. Hint: Place the vertices of the square along the axes and then introduce coordinates. 2. Find the equation

More information

Math 221 Midterm Fall 2017 Section 104 Dijana Kreso

Math 221 Midterm Fall 2017 Section 104 Dijana Kreso The University of British Columbia Midterm October 5, 017 Group B Math 1: Matrix Algebra Section 104 (Dijana Kreso) Last Name: Student Number: First Name: Section: Format: 50 min long exam. Total: 5 marks.

More information

R b. x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 1 1, x h. , x p. x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9

R b. x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 1 1, x h. , x p. x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 The full solution of Ax b x x p x h : The general solution is the sum of any particular solution of the system Ax b plus the general solution of the corresponding homogeneous system Ax. ) Reduce A b to

More information

MATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)

MATH 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 information

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

Exam 2 Solutions. (a) Is W closed under addition? Why or why not? W is not closed under addition. For example, Exam 2 Solutions. Let V be the set of pairs of real numbers (x, y). Define the following operations on V : (x, y) (x, y ) = (x + x, xx + yy ) r (x, y) = (rx, y) Check if V together with and satisfy properties

More information

spring, math 204 (mitchell) list of theorems 1 Linear Systems Linear Transformations Matrix Algebra

spring, math 204 (mitchell) list of theorems 1 Linear Systems Linear Transformations Matrix Algebra spring, 2016. math 204 (mitchell) list of theorems 1 Linear Systems THEOREM 1.0.1 (Theorem 1.1). Uniqueness of Reduced Row-Echelon Form THEOREM 1.0.2 (Theorem 1.2). Existence and Uniqueness Theorem THEOREM

More information

Math 2174: Practice Midterm 1

Math 2174: Practice Midterm 1 Math 74: Practice Midterm Show your work and explain your reasoning as appropriate. No calculators. One page of handwritten notes is allowed for the exam, as well as one blank page of scratch paper.. Consider

More information

Chapter 2. General Vector Spaces. 2.1 Real Vector Spaces

Chapter 2. General Vector Spaces. 2.1 Real Vector Spaces Chapter 2 General Vector Spaces Outline : Real vector spaces Subspaces Linear independence Basis and dimension Row Space, Column Space, and Nullspace 2 Real Vector Spaces 2 Example () Let u and v be vectors

More information

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

Math 4A Notes. Written by Victoria Kala Last updated June 11, 2017 Math 4A Notes Written by Victoria Kala vtkala@math.ucsb.edu Last updated June 11, 2017 Systems of Linear Equations A linear equation is an equation that can be written in the form a 1 x 1 + a 2 x 2 +...

More information

Review for Exam 2 Solutions

Review for Exam 2 Solutions Review for Exam 2 Solutions Note: All vector spaces are real vector spaces. Definition 4.4 will be provided on the exam as it appears in the textbook.. Determine if the following sets V together with operations

More information

EXAM. Exam #2. Math 2360 Summer II, 2000 Morning Class. Nov. 15, 2000 ANSWERS

EXAM. Exam #2. Math 2360 Summer II, 2000 Morning Class. Nov. 15, 2000 ANSWERS EXAM Exam # Math 6 Summer II Morning Class Nov 5 ANSWERS i Problem Consider the matrix 6 pts A = 6 4 9 5 7 6 5 5 5 4 The RREF of A is the matrix R = A Find a basis for the nullspace of A Solve the homogeneous

More information

MATH SOLUTIONS TO PRACTICE PROBLEMS - MIDTERM I. 1. We carry out row reduction. We begin with the row operations

MATH SOLUTIONS TO PRACTICE PROBLEMS - MIDTERM I. 1. We carry out row reduction. We begin with the row operations MATH 2 - SOLUTIONS TO PRACTICE PROBLEMS - MIDTERM I. We carry out row reduction. We begin with the row operations yielding the matrix This is already upper triangular hence The lower triangular matrix

More information

GRE Subject test preparation Spring 2016 Topic: Abstract Algebra, Linear Algebra, Number Theory.

GRE Subject test preparation Spring 2016 Topic: Abstract Algebra, Linear Algebra, Number Theory. GRE Subject test preparation Spring 2016 Topic: Abstract Algebra, Linear Algebra, Number Theory. Linear Algebra Standard matrix manipulation to compute the kernel, intersection of subspaces, column spaces,

More information

Math 323 Exam 2 Sample Problems Solution Guide October 31, 2013

Math 323 Exam 2 Sample Problems Solution Guide October 31, 2013 Math Exam Sample Problems Solution Guide October, Note that the following provides a guide to the solutions on the sample problems, but in some cases the complete solution would require more work or justification

More information

Systems of Linear Equations

Systems 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 information

Lecture Summaries for Linear Algebra M51A

Lecture Summaries for Linear Algebra M51A These lecture summaries may also be viewed online by clicking the L icon at the top right of any lecture screen. Lecture Summaries for Linear Algebra M51A refers to the section in the textbook. Lecture

More information

REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW

REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW 20 CHAPTER 1 Systems of Linear Equations REPLACE ONE ROW BY ADDING THE SCALAR MULTIPLE OF ANOTHER ROW The last type of operation is slightly more complicated. Suppose that we want to write down the elementary

More information

EE263: Introduction to Linear Dynamical Systems Review Session 2

EE263: Introduction to Linear Dynamical Systems Review Session 2 EE263: Introduction to Linear Dynamical Systems Review Session 2 Basic concepts from linear algebra nullspace range rank and conservation of dimension EE263 RS2 1 Prerequisites We assume that you are familiar

More information

MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian.

MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian. MATH 304 Linear Algebra Lecture 10: Linear independence. Wronskian. Spanning set Let S be a subset of a vector space V. Definition. The span of the set S is the smallest subspace W V that contains S. If

More information

Announcements Monday, October 29

Announcements Monday, October 29 Announcements Monday, October 29 WeBWorK on determinents due on Wednesday at :59pm. The quiz on Friday covers 5., 5.2, 5.3. My office is Skiles 244 and Rabinoffice hours are: Mondays, 2 pm; Wednesdays,

More information

Chapter 3. Vector spaces

Chapter 3. Vector spaces Chapter 3. Vector spaces Lecture notes for MA1111 P. Karageorgis pete@maths.tcd.ie 1/22 Linear combinations Suppose that v 1,v 2,...,v n and v are vectors in R m. Definition 3.1 Linear combination We say

More information

Algorithms to Compute Bases and the Rank of a Matrix

Algorithms to Compute Bases and the Rank of a Matrix Algorithms to Compute Bases and the Rank of a Matrix Subspaces associated to a matrix Suppose that A is an m n matrix The row space of A is the subspace of R n spanned by the rows of A The column space

More information

1 Last time: determinants

1 Last time: determinants 1 Last time: determinants Let n be a positive integer If A is an n n matrix, then its determinant is the number det A = Π(X, A)( 1) inv(x) X S n where S n is the set of n n permutation matrices Π(X, A)

More information

MathQuest: Linear Algebra

MathQuest: Linear Algebra MathQuest: Linear Algebra Linear Independence. True or False The following vectors are linearly independent: (,0,0), (0,0,2), (3,0,) 2. Which set of vectors is linearly independent? (a) (2,3),(8,2) (b)

More information

Solutions to Math 51 First Exam April 21, 2011

Solutions to Math 51 First Exam April 21, 2011 Solutions to Math 5 First Exam April,. ( points) (a) Give the precise definition of a (linear) subspace V of R n. (4 points) A linear subspace V of R n is a subset V R n which satisfies V. If x, y V then

More information

x y + z = 3 2y z = 1 4x + y = 0

x y + z = 3 2y z = 1 4x + y = 0 MA 253: Practice Exam Solutions You may not use a graphing calculator, computer, textbook, notes, or refer to other people (except the instructor). Show all of your work; your work is your answer. Problem

More information

Unit I Vector Spaces. Outline. definition and examples subspaces and more examples three key concepts. sums and direct sums of vector spaces

Unit I Vector Spaces. Outline. definition and examples subspaces and more examples three key concepts. sums and direct sums of vector spaces Outline Unit I Vector Spaces definition and examples subspaces and more examples three key concepts linear combinations and span linear independence bases and dimension sums and direct sums of vector spaces

More information