Chapter Vectors

Size: px
Start display at page:

Download "Chapter Vectors"

Transcription

1 Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it meas to have a liearly idepedet set of vectors ad 5. fid the ra of a set of vectors. What is a vector? vector is a collectio of umbers i a defiite order. If it is a collectio of umbers it is called a -dimesioal vector. So the vector give by a a a is a -dimesioal colum vector with compoets a a... a. The above is a colum vector. row vector [B] is of the form B [ b b... b ] where B is a - dimesioal row vector with compoets b b... b. Example Give a example of a -dimesioal colum vector. ssume a poit i space is give by its ( x y z) coordiates. The if the value of x y z 5 the colum vector correspodig to the locatio of the poits is x y. z 5 4..

2 4.. Chapter 4. Whe are two vectors equal? Two vectors ad B are equal if they are of the same dimesio ad if their correspodig compoets are equal. Give a a ad the a b b B b B if a i b i.... i Example What are the values of the uow compoets i B if 4 ad b B 4 b4 ad B. b b 4 How do you add two vectors? Two vectors ca be added oly if they are of the same dimesio ad the additio is give by

3 Vectors 4.. b b b a a a B ] [ ] [ a b b a b a Example dd the two vectors 4 ad 7 5 B B

4 4..4 Chapter 4. Example 4 store sells three brads of tires: Tirestoe Michiga ad Copper. I quarter the sales are give by the colum vector where the rows represet the three brads of tires sold Tirestoe Michiga ad Copper respectively. I quarter the sales are give by 6 What is the total sale of each brad of tire i the first half of the year? The total sales would be give by C So the umber of Tirestoe tires sold is 45 Michiga is 5 ad Copper is i the first half of the year. What is a ull vector? ull vector (also called zero vector) is where all the compoets of the vector are zero. Example 5 Give a example of a ull vector or zero vector. The vector

5 Vectors 4..5 is a example of a zero or ull vector. What is a uit vector? uit vector U is defied as u u u U where u u u u Example 6 Give examples of -dimesioal uit colum vectors. Examples iclude etc. How do you multiply a vector by a scalar? If is a scalar ad is a -dimesioal vector the a a a a a a

6 4..6 Chapter 4. Example 7 What is if Example 8 store sells three brads of tires: Tirestoe Michiga ad Copper. I quarter the sales are give by the colum vector If the goal is to icrease the sales of all tires by at least 5% i the ext quarter how may of each brad should be sold? Sice the goal is to icrease the sales by 5% oe would multiply the vector by.5 5 B Sice the umber of tires must be a iteger we ca say that the goal of sales is

7 Vectors B What do you mea by a liear combiatio of vectors? Give m... as m vectors of same dimesio ad if m... are scalars the m m... is a liear combiatio of the m vectors. Example 9 Fid the liear combiatios a) B ad b) C B where 6 C B a) 6 B 6 4 b) 6 C B 6 6

8 4..8 Chapter 4. 7 What do you mea by vectors beig liearly idepedet? set of vectors m are cosidered to be liearly idepedet if... m m has oly oe solutio of... m Example re the three vectors liearly idepedet? Writig the liear combiatio of the three vectors gives The above equatios have oly oe solutio. However how do we show that this is the oly solutio? This is show below. The above equatios are 5 5 () 64 8 () 44 () Subtractig Eq () from Eq () gives 9 (4) Multiplyig Eq () by 8 ad subtractig it from Eq () that is first multiplied by 5 gives

9 Vectors (5) Remember we foud Eq (4) ad Eq (5) just from Eqs () ad (). Substitutio of Eqs (4) ad (5) i Eq () for ad gives 44 ( ) 4 8 This meas that has to be zero ad coupled with (4) ad (5) ad are also zero. So the oly solutio is. The three vectors hece are liearly idepedet. Example re the three vectors liearly idepedet? By ispectio or So the liear combiatio has a o-zero solutio Hece the set of vectors is liearly depedet. What if I caot prove by ispectio what do I do? Put the liear combiatio of three vectors equal to the zero vector to give 6 () 5 4 () () Multiplyig Eq () by ad subtractig from Eq () gives

10 4.. Chapter 4. (4) Multiplyig Eq () by.5 ad subtractig from Eq () gives.5 (5) Remember we foud Eq (4) ad Eq (5) just from Eqs () ad (). Substitute Eq (4) ad (5) i Eq () for ad gives 5( ) 7( ) This meas ay values satisfyig Eqs (4) ad (5) will satisfy Eqs () () ad () simultaeously. For example chose 6 the from Eq (4) ad from Eq (5). Hece we have a otrivial solutio of [ ] [ 6]. This implies the three give vectors are liearly depedet. Ca you fid aother otrivial solutio? What about the followig three vectors? re they liearly depedet or liearly idepedet? Note that the oly differece betwee this set of vectors ad the previous oe is the third etry i the third vector. Hece equatios (4) ad (5) are still valid. What coclusio do you draw whe you plug i equatios (4) ad (5) i the third equatio: 5 7 5? What has chaged? Example re the three vectors liearly idepedet? Writig the liear combiatio of the three vectors ad equatig to zero vector

11 Vectors gives I additio to oe ca fid other solutios for which are ot equal to zero. For example 4 is also a solutio as Hece are liearly depedet. What do you mea by the ra of a set of vectors? From a set of -dimesioal vectors the maximum umber of liearly idepedet vectors i the set is called the ra of the set of vectors. Note that the ra of the vectors ca ever be greater tha the vectors dimesio. Example What is the ra of ? 44 Sice we foud i Example. that are liearly idepedet the ra of the set of vectors is. If we were give aother vector 4 the ra of the set of the vectors 4 would still be as the ra of a set of vectors is always less tha or equal to the dimesio of the vectors ad that at least are liearly idepedet. Example 4 What is the ra of ?

12 4.. Chapter 4. I Example. we foud that are liearly depedet the ra of is hece ot ad is less tha. Is it? Let us choose two of the three vectors Liear combiatio of ad equal to zero has oly oe solutio the trivial solutio. Therefore the ra is. Example 5 What is the ra of? 4 5 From ispectio that implies. Hece. has a otrivial solutio. So are liearly depedet ad hece the ra of the three vectors is ot. Sice ad are liearly depedet but. has trivial solutio as the oly solutio. So ad are liearly idepedet. The ra of the above three vectors is. Prove that if a set of vectors cotais the ull vector the set of vectors is liearly depedet. Let... m be a set of -dimesioal vectors the m m is a liear combiatio of the m vectors. The assumig if is the zero or ull vector ay value of coupled with will satisfy the above equatio. Hece the m

13 Vectors 4.. set of vectors is liearly depedet as more tha oe solutio exists. Prove that if a set of m vectors is liearly idepedet the a subset of the m vectors also has to be liearly idepedet. Let this subset of vectors be a a ap where p < m. The if this subset of vectors is liearly depedet the liear combiatio a a p ap has a o-trivial solutio. So a a p ap a ( p )... am also has a o-trivial solutio too where a ( p ) am are the rest of the ( m p) vectors. However this is a cotradictio. Therefore a subset of liearly idepedet vectors caot be liearly depedet. Prove that if a set of vectors is liearly depedet the at least oe vector ca be writte as a liear combiatio of others. Let m be liearly depedet set of vectors the there exists a set of scalars m ot all of which are zero for the liear combiatio equatio m m. Let p be oe of the o-zero values of i i m that is p the p p m p p p m. p p p p ad that proves the theorem. Prove that if the dimesio of a set of vectors is less tha the umber of vectors i the set the the set of vectors is liearly depedet. Ca you prove it? How ca vectors be used to write simultaeous liear equatios? If a set of m simultaeous liear equatios with uows is writte as ax a x c ax a x c a x a x c m m

14 4..4 Chapter 4. where where where x x x x x are the uows the i the vector otatio they ca be writte as a a m a a m a a m a a m c C c m x C The problem ow becomes whether you ca fid the scalars combiatio x... x is equal to the C that is x... x C x x... x such that the liear Example 6 Write 5x 5x x x 8x x x x x 79. as a liear combiatio of set of vectors equal to aother vector.

15 Vectors x 5x x x 8x x x 79. x x x 64 x 8 x What is the defiitio of the dot product of two vectors? a a B b b be two -dimesioal vectors. The the dot Let [ ] ad [ ] a b product of the two vectors ad B is defied as B a b a b a b a b i dot product is also called a ier product. Example 7 Fid the dot product of the two vectors [4 ] ad B [ 7 ]. B [4].[7] (4)()()()()(7)()() Example 8 product lie eeds three types of rubber as give i the table below. Rubber Type Weight (lbs) Cost per poud ($) B C Use the defiitio of a dot product to fid the total price of the rubber eeded. The weight vector is give by W [5] ad the cost vector is give by C [..569.]. The total cost of the rubber would be the dot product of W ad C. W C [ 5] [..569.] i i

16 4..6 Chapter 4. ( )(.) (5)(.56) ()(9.) $7. Key Terms: Vector dditio of vectors Ra Dot Product Subtractio of vectors Uit vector Scalar multiplicatio of vectors Null vector Liear combiatio of vectors Liearly idepedet vectors

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

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

More information

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

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

More information

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

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

More information

Chapter Unary Matrix Operations

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

More information

Algebra of Least Squares

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

More information

M 340L CS Homew ork Set 6 Solutions

M 340L CS Homew ork Set 6 Solutions 1. Suppose P is ivertible ad M 34L CS Homew ork Set 6 Solutios A PBP 1. Solve for B i terms of P ad A. Sice A PBP 1, w e have 1 1 1 B P PBP P P AP ( ).. Suppose ( B C) D, w here B ad C are m matrices ad

More information

M 340L CS Homew ork Set 6 Solutions

M 340L CS Homew ork Set 6 Solutions . Suppose P is ivertible ad M 4L CS Homew ork Set 6 Solutios A PBP. Solve for B i terms of P ad A. Sice A PBP, w e have B P PBP P P AP ( ).. Suppose ( B C) D, w here B ad C are m matrices ad D is ivertible.

More information

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

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

More information

Eigenvalues and Eigenvectors

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

More information

MATH10212 Linear Algebra B Proof Problems

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

More information

LESSON 2: SIMPLIFYING RADICALS

LESSON 2: SIMPLIFYING RADICALS High School: Workig with Epressios LESSON : SIMPLIFYING RADICALS N.RN.. C N.RN.. B 5 5 C t t t t t E a b a a b N.RN.. 4 6 N.RN. 4. N.RN. 5. N.RN. 6. 7 8 N.RN. 7. A 7 N.RN. 8. 6 80 448 4 5 6 48 00 6 6 6

More information

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

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

More information

CHAPTER 5. Theory and Solution Using Matrix Techniques

CHAPTER 5. Theory and Solution Using Matrix Techniques A SERIES OF CLASS NOTES FOR 2005-2006 TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS DE CLASS NOTES 3 A COLLECTION OF HANDOUTS ON SYSTEMS OF ORDINARY DIFFERENTIAL

More information

September 2012 C1 Note. C1 Notes (Edexcel) Copyright - For AS, A2 notes and IGCSE / GCSE worksheets 1

September 2012 C1 Note. C1 Notes (Edexcel) Copyright   - For AS, A2 notes and IGCSE / GCSE worksheets 1 September 0 s (Edecel) Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

PROBLEM SET I (Suggested Solutions)

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

More information

CHAPTER I: Vector Spaces

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

More information

M A T H F A L L CORRECTION. Algebra I 1 4 / 1 0 / U N I V E R S I T Y O F T O R O N T O

M A T H F A L L CORRECTION. Algebra I 1 4 / 1 0 / U N I V E R S I T Y O F T O R O N T O M A T H 2 4 0 F A L L 2 0 1 4 HOMEWORK ASSIGNMENT #4 CORRECTION Algebra I 1 4 / 1 0 / 2 0 1 4 U N I V E R S I T Y O F T O R O N T O P r o f e s s o r : D r o r B a r - N a t a Correctio Homework Assigmet

More information

Machine Learning for Data Science (CS 4786)

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

More information

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

(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row: Math 50-004 Tue Feb 4 Cotiue with sectio 36 Determiats The effective way to compute determiats for larger-sized matrices without lots of zeroes is to ot use the defiitio, but rather to use the followig

More information

Definitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients.

Definitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients. Defiitios ad Theorems Remember the scalar form of the liear programmig problem, Miimize, Subject to, f(x) = c i x i a 1i x i = b 1 a mi x i = b m x i 0 i = 1,2,, where x are the decisio variables. c, b,

More information

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

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

More information

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

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

More information

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

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

More information

U8L1: Sec Equations of Lines in R 2

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

More information

Mathematical Foundations -1- Sets and Sequences. Sets and Sequences

Mathematical Foundations -1- Sets and Sequences. Sets and Sequences Mathematical Foudatios -1- Sets ad Sequeces Sets ad Sequeces Methods of proof 2 Sets ad vectors 13 Plaes ad hyperplaes 18 Liearly idepedet vectors, vector spaces 2 Covex combiatios of vectors 21 eighborhoods,

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

is also known as the general term of the sequence

is also known as the general term of the sequence Lesso : Sequeces ad Series Outlie Objectives: I ca determie whether a sequece has a patter. I ca determie whether a sequece ca be geeralized to fid a formula for the geeral term i the sequece. I ca determie

More information

Lesson 10: Limits and Continuity

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

More information

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

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

More information

Name Date PRECALCULUS SUMMER PACKET

Name Date PRECALCULUS SUMMER PACKET Name Date PRECALCULUS SUMMER PACKET This packet covers some of the cocepts that you eed to e familiar with i order to e successful i Precalculus. This summer packet is due o the first day of school! Make

More information

Zeros of Polynomials

Zeros of Polynomials Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree

More information

Stochastic Matrices in a Finite Field

Stochastic Matrices in a Finite Field Stochastic Matrices i a Fiite Field Abstract: I this project we will explore the properties of stochastic matrices i both the real ad the fiite fields. We first explore what properties 2 2 stochastic matrices

More information

Recurrence Relations

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

More information

Example 1.1 Use an augmented matrix to mimic the elimination method for solving the following linear system of equations.

Example 1.1 Use an augmented matrix to mimic the elimination method for solving the following linear system of equations. MTH 261 Mr Simods class Example 11 Use a augmeted matrix to mimic the elimiatio method for solvig the followig liear system of equatios 2x1 3x2 8 6x1 x2 36 Example 12 Use the method of Gaussia elimiatio

More information

CALCULATION OF FIBONACCI VECTORS

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

More information

TEACHER CERTIFICATION STUDY GUIDE

TEACHER CERTIFICATION STUDY GUIDE COMPETENCY 1. ALGEBRA SKILL 1.1 1.1a. ALGEBRAIC STRUCTURES Kow why the real ad complex umbers are each a field, ad that particular rigs are ot fields (e.g., itegers, polyomial rigs, matrix rigs) Algebra

More information

RADICAL EXPRESSION. If a and x are real numbers and n is a positive integer, then x is an. n th root theorems: Example 1 Simplify

RADICAL EXPRESSION. If a and x are real numbers and n is a positive integer, then x is an. n th root theorems: Example 1 Simplify Example 1 Simplify 1.2A Radical Operatios a) 4 2 b) 16 1 2 c) 16 d) 2 e) 8 1 f) 8 What is the relatioship betwee a, b, c? What is the relatioship betwee d, e, f? If x = a, the x = = th root theorems: RADICAL

More information

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function. MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied

More information

Machine Learning for Data Science (CS 4786)

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

More information

1. By using truth tables prove that, for all statements P and Q, the statement

1. By using truth tables prove that, for all statements P and Q, the statement Author: Satiago Salazar Problems I: Mathematical Statemets ad Proofs. By usig truth tables prove that, for all statemets P ad Q, the statemet P Q ad its cotrapositive ot Q (ot P) are equivalet. I example.2.3

More information

8. Applications To Linear Differential Equations

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

More information

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2.

multiplies all measures of center and the standard deviation and range by k, while the variance is multiplied by k 2. Lesso 3- Lesso 3- Scale Chages of Data Vocabulary scale chage of a data set scale factor scale image BIG IDEA Multiplyig every umber i a data set by k multiplies all measures of ceter ad the stadard deviatio

More information

The multiplicative structure of finite field and a construction of LRC

The multiplicative structure of finite field and a construction of LRC IERG6120 Codig for Distributed Storage Systems Lecture 8-06/10/2016 The multiplicative structure of fiite field ad a costructio of LRC Lecturer: Keeth Shum Scribe: Zhouyi Hu Notatios: We use the otatio

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES 9 SEQUENCES AND SERIES INTRODUCTION Sequeces have may importat applicatios i several spheres of huma activities Whe a collectio of objects is arraged i a defiite order such that it has a idetified first

More information

Linear Regression Demystified

Linear Regression Demystified Liear Regressio Demystified Liear regressio is a importat subject i statistics. I elemetary statistics courses, formulae related to liear regressio are ofte stated without derivatio. This ote iteds to

More information

REVISION SHEET FP1 (MEI) ALGEBRA. Identities In mathematics, an identity is a statement which is true for all values of the variables it contains.

REVISION SHEET FP1 (MEI) ALGEBRA. Identities In mathematics, an identity is a statement which is true for all values of the variables it contains. The mai ideas are: Idetities REVISION SHEET FP (MEI) ALGEBRA Before the exam you should kow: If a expressio is a idetity the it is true for all values of the variable it cotais The relatioships betwee

More information

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

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

More information

B = B is a 3 4 matrix; b 32 = 3 and b 2 4 = 3. Scalar Multiplication

B = B is a 3 4 matrix; b 32 = 3 and b 2 4 = 3. Scalar Multiplication MATH 37 Matrices Dr. Neal, WKU A m matrix A = (a i j ) is a array of m umbers arraged ito m rows ad colums, where a i j is the etry i the ith row, jth colum. The values m are called the dimesios (or size)

More information

End-of-Year Contest. ERHS Math Club. May 5, 2009

End-of-Year Contest. ERHS Math Club. May 5, 2009 Ed-of-Year Cotest ERHS Math Club May 5, 009 Problem 1: There are 9 cois. Oe is fake ad weighs a little less tha the others. Fid the fake coi by weighigs. Solutio: Separate the 9 cois ito 3 groups (A, B,

More information

Vector Spaces and Vector Subspaces. Remarks. Euclidean Space

Vector Spaces and Vector Subspaces. Remarks. Euclidean Space Vector Spaces ad Vector Subspaces Remarks Let be a iteger. A -dimesioal vector is a colum of umbers eclosed i brackets. The umbers are called the compoets of the vector. u u u u Euclidea Space I Euclidea

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Sequeces ad 6 Sequeces Ad SEQUENCES AND SERIES Successio of umbers of which oe umber is desigated as the first, other as the secod, aother as the third ad so o gives rise to what is called a sequece. Sequeces

More information

( ) ( ) ( ) notation: [ ]

( ) ( ) ( ) notation: [ ] Liear Algebra Vectors ad Matrices Fudametal Operatios with Vectors Vector: a directed lie segmets that has both magitude ad directio =,,,..., =,,,..., = where 1, 2,, are the otatio: [ ] 1 2 3 1 2 3 compoets

More information

Some examples of vector spaces

Some examples of vector spaces Roberto s Notes o Liear Algebra Chapter 11: Vector spaces Sectio 2 Some examples of vector spaces What you eed to kow already: The te axioms eeded to idetify a vector space. What you ca lear here: Some

More information

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

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

More information

n m CHAPTER 3 RATIONAL EXPONENTS AND RADICAL FUNCTIONS 3-1 Evaluate n th Roots and Use Rational Exponents Real nth Roots of a n th Root of a

n m CHAPTER 3 RATIONAL EXPONENTS AND RADICAL FUNCTIONS 3-1 Evaluate n th Roots and Use Rational Exponents Real nth Roots of a n th Root of a CHAPTER RATIONAL EXPONENTS AND RADICAL FUNCTIONS Big IDEAS: 1) Usig ratioal expoets ) Performig fuctio operatios ad fidig iverse fuctios ) Graphig radical fuctios ad solvig radical equatios Sectio: Essetial

More information

P.3 Polynomials and Special products

P.3 Polynomials and Special products Precalc Fall 2016 Sectios P.3, 1.2, 1.3, P.4, 1.4, P.2 (radicals/ratioal expoets), 1.5, 1.6, 1.7, 1.8, 1.1, 2.1, 2.2 I Polyomial defiitio (p. 28) a x + a x +... + a x + a x 1 1 0 1 1 0 a x + a x +... +

More information

Chimica Inorganica 3

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

More information

1 Last time: similar and diagonalizable matrices

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

More information

REVISION SHEET FP1 (MEI) ALGEBRA. Identities In mathematics, an identity is a statement which is true for all values of the variables it contains.

REVISION SHEET FP1 (MEI) ALGEBRA. Identities In mathematics, an identity is a statement which is true for all values of the variables it contains. the Further Mathematics etwork wwwfmetworkorguk V 07 The mai ideas are: Idetities REVISION SHEET FP (MEI) ALGEBRA Before the exam you should kow: If a expressio is a idetity the it is true for all values

More information

CS276A Practice Problem Set 1 Solutions

CS276A Practice Problem Set 1 Solutions CS76A Practice Problem Set Solutios Problem. (i) (ii) 8 (iii) 6 Compute the gamma-codes for the followig itegers: (i) (ii) 8 (iii) 6 Problem. For this problem, we will be dealig with a collectio of millio

More information

Math 61CM - Solutions to homework 3

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

More information

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

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

More information

Properties and Tests of Zeros of Polynomial Functions

Properties and Tests of Zeros of Polynomial Functions Properties ad Tests of Zeros of Polyomial Fuctios The Remaider ad Factor Theorems: Sythetic divisio ca be used to fid the values of polyomials i a sometimes easier way tha substitutio. This is show by

More information

Sail into Summer with Math!

Sail into Summer with Math! Sail ito Summer with Math! For Studets Eterig Hoors Geometry This summer math booklet was developed to provide studets i kidergarte through the eighth grade a opportuity to review grade level math objectives

More information

Optimization Methods: Linear Programming Applications Assignment Problem 1. Module 4 Lecture Notes 3. Assignment Problem

Optimization Methods: Linear Programming Applications Assignment Problem 1. Module 4 Lecture Notes 3. Assignment Problem Optimizatio Methods: Liear Programmig Applicatios Assigmet Problem Itroductio Module 4 Lecture Notes 3 Assigmet Problem I the previous lecture, we discussed about oe of the bech mark problems called trasportatio

More information

APPENDIX F Complex Numbers

APPENDIX F Complex Numbers APPENDIX F Complex Numbers Operatios with Complex Numbers Complex Solutios of Quadratic Equatios Polar Form of a Complex Number Powers ad Roots of Complex Numbers Operatios with Complex Numbers Some equatios

More information

Axioms of Measure Theory

Axioms of Measure Theory MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that

More information

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3 MATH 337 Sequeces Dr. Neal, WKU Let X be a metric space with distace fuctio d. We shall defie the geeral cocept of sequece ad limit i a metric space, the apply the results i particular to some special

More information

Complex Numbers Solutions

Complex Numbers Solutions Complex Numbers Solutios Joseph Zoller February 7, 06 Solutios. (009 AIME I Problem ) There is a complex umber with imagiary part 64 ad a positive iteger such that Fid. [Solutio: 697] 4i + + 4i. 4i 4i

More information

Math 4707 Spring 2018 (Darij Grinberg): homework set 4 page 1

Math 4707 Spring 2018 (Darij Grinberg): homework set 4 page 1 Math 4707 Sprig 2018 Darij Griberg): homewor set 4 page 1 Math 4707 Sprig 2018 Darij Griberg): homewor set 4 due date: Wedesday 11 April 2018 at the begiig of class, or before that by email or moodle Please

More information

Chapter 1 Simple Linear Regression (part 6: matrix version)

Chapter 1 Simple Linear Regression (part 6: matrix version) Chapter Simple Liear Regressio (part 6: matrix versio) Overview Simple liear regressio model: respose variable Y, a sigle idepedet variable X Y β 0 + β X + ε Multiple liear regressio model: respose Y,

More information

INTEGRATION BY PARTS (TABLE METHOD)

INTEGRATION BY PARTS (TABLE METHOD) INTEGRATION BY PARTS (TABLE METHOD) Suppose you wat to evaluate cos d usig itegratio by parts. Usig the u dv otatio, we get So, u dv d cos du d v si cos d si si d or si si d We see that it is ecessary

More information

R is a scalar defined as follows:

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

More information

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

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

More information

2 Geometric interpretation of complex numbers

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

More information

BHW #13 1/ Cooper. ENGR 323 Probabilistic Analysis Beautiful Homework # 13

BHW #13 1/ Cooper. ENGR 323 Probabilistic Analysis Beautiful Homework # 13 BHW # /5 ENGR Probabilistic Aalysis Beautiful Homework # Three differet roads feed ito a particular freeway etrace. Suppose that durig a fixed time period, the umber of cars comig from each road oto the

More information

MATH 304: MIDTERM EXAM SOLUTIONS

MATH 304: MIDTERM EXAM SOLUTIONS MATH 304: MIDTERM EXAM SOLUTIONS [The problems are each worth five poits, except for problem 8, which is worth 8 poits. Thus there are 43 possible poits.] 1. Use the Euclidea algorithm to fid the greatest

More information

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

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

More information

1 Approximating Integrals using Taylor Polynomials

1 Approximating Integrals using Taylor Polynomials Seughee Ye Ma 8: Week 7 Nov Week 7 Summary This week, we will lear how we ca approximate itegrals usig Taylor series ad umerical methods. Topics Page Approximatig Itegrals usig Taylor Polyomials. Defiitios................................................

More information

6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer.

6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer. 6 Itegers Modulo I Example 2.3(e), we have defied the cogruece of two itegers a,b with respect to a modulus. Let us recall that a b (mod ) meas a b. We have proved that cogruece is a equivalece relatio

More information

UNIVERSITY OF NORTHERN COLORADO MATHEMATICS CONTEST. First Round For all Colorado Students Grades 7-12 November 3, 2007

UNIVERSITY OF NORTHERN COLORADO MATHEMATICS CONTEST. First Round For all Colorado Students Grades 7-12 November 3, 2007 UNIVERSITY OF NORTHERN COLORADO MATHEMATICS CONTEST First Roud For all Colorado Studets Grades 7- November, 7 The positive itegers are,,, 4, 5, 6, 7, 8, 9,,,,. The Pythagorea Theorem says that a + b =

More information

Hoggatt and King [lo] defined a complete sequence of natural numbers

Hoggatt and King [lo] defined a complete sequence of natural numbers REPRESENTATIONS OF N AS A SUM OF DISTINCT ELEMENTS FROM SPECIAL SEQUENCES DAVID A. KLARNER, Uiversity of Alberta, Edmoto, Caada 1. INTRODUCTION Let a, I deote a sequece of atural umbers which satisfies

More information

Long-term Memory Review CRT PRACTICE 8 th Grade: MONDAY REVIEW STATE STANDARDS AND 2.8.2

Long-term Memory Review CRT PRACTICE 8 th Grade: MONDAY REVIEW STATE STANDARDS AND 2.8.2 CRT PRACTICE 8 th Grade: MONDAY REVIEW Word Bak: use these words to fill i the blaks for Questio 1. Words may be used oce, more tha oce, or ot at all. sequece series factor term equatio expressio 1) A()

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

A 2nTH ORDER LINEAR DIFFERENCE EQUATION

A 2nTH ORDER LINEAR DIFFERENCE EQUATION A 2TH ORDER LINEAR DIFFERENCE EQUATION Doug Aderso Departmet of Mathematics ad Computer Sciece, Cocordia College Moorhead, MN 56562, USA ABSTRACT: We give a formulatio of geeralized zeros ad (, )-discojugacy

More information

The Structure of Z p when p is Prime

The Structure of Z p when p is Prime LECTURE 13 The Structure of Z p whe p is Prime Theorem 131 If p > 1 is a iteger, the the followig properties are equivalet (1) p is prime (2) For ay [0] p i Z p, the equatio X = [1] p has a solutio i Z

More information

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK)

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

More information

Notes The Incremental Motion Model:

Notes The Incremental Motion Model: The Icremetal Motio Model: The Jacobia Matrix I the forward kiematics model, we saw that it was possible to relate joit agles θ, to the cofiguratio of the robot ed effector T I this sectio, we will see

More information

Math 312 Lecture Notes One Dimensional Maps

Math 312 Lecture Notes One Dimensional Maps Math 312 Lecture Notes Oe Dimesioal Maps Warre Weckesser Departmet of Mathematics Colgate Uiversity 21-23 February 25 A Example We begi with the simplest model of populatio growth. Suppose, for example,

More information

LinearAlgebra DMTH502

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

More information

TMA4205 Numerical Linear Algebra. The Poisson problem in R 2 : diagonalization methods

TMA4205 Numerical Linear Algebra. The Poisson problem in R 2 : diagonalization methods TMA4205 Numerical Liear Algebra The Poisso problem i R 2 : diagoalizatio methods September 3, 2007 c Eiar M Røquist Departmet of Mathematical Scieces NTNU, N-749 Trodheim, Norway All rights reserved A

More information

Proof of Fermat s Last Theorem by Algebra Identities and Linear Algebra

Proof of Fermat s Last Theorem by Algebra Identities and Linear Algebra Proof of Fermat s Last Theorem by Algebra Idetities ad Liear Algebra Javad Babaee Ragai Youg Researchers ad Elite Club, Qaemshahr Brach, Islamic Azad Uiversity, Qaemshahr, Ira Departmet of Civil Egieerig,

More information

11. FINITE FIELDS. Example 1: The following tables define addition and multiplication for a field of order 4.

11. FINITE FIELDS. Example 1: The following tables define addition and multiplication for a field of order 4. 11. FINITE FIELDS 11.1. A Field With 4 Elemets Probably the oly fiite fields which you ll kow about at this stage are the fields of itegers modulo a prime p, deoted by Z p. But there are others. Now although

More information

Mathematics Review for MS Finance Students Lecture Notes

Mathematics Review for MS Finance Students Lecture Notes Mathematics Review for MS Fiace Studets Lecture Notes Athoy M. Mario Departmet of Fiace ad Busiess Ecoomics Marshall School of Busiess Uiversity of Souther Califoria Los Ageles, CA 1 Lecture 1.1: Basics

More information

Principle Of Superposition

Principle Of Superposition ecture 5: PREIMINRY CONCEP O RUCUR NYI Priciple Of uperpositio Mathematically, the priciple of superpositio is stated as ( a ) G( a ) G( ) G a a or for a liear structural system, the respose at a give

More information

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions Statistical ad Mathematical Methods DS-GA 00 December 8, 05. Short questios Sample Fial Problems Solutios a. Ax b has a solutio if b is i the rage of A. The dimesio of the rage of A is because A has liearly-idepedet

More information

XT - MATHS Grade 12. Date: 2010/06/29. Subject: Series and Sequences 1: Arithmetic Total Marks: 84 = 2 = 2 1. FALSE 10.

XT - MATHS Grade 12. Date: 2010/06/29. Subject: Series and Sequences 1: Arithmetic Total Marks: 84 = 2 = 2 1. FALSE 10. ubject: eries ad equeces 1: Arithmetic otal Mars: 8 X - MAH Grade 1 Date: 010/0/ 1. FALE 10 Explaatio: his series is arithmetic as d 1 ad d 15 1 he sum of a arithmetic series is give by [ a ( ] a represets

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

WORKING WITH NUMBERS

WORKING WITH NUMBERS 1 WORKING WITH NUMBERS WHAT YOU NEED TO KNOW The defiitio of the differet umber sets: is the set of atural umbers {0, 1,, 3, }. is the set of itegers {, 3,, 1, 0, 1,, 3, }; + is the set of positive itegers;

More information