Chapter 5 Dot, Inner and Cross Products. 5.1 Length of a vector 5.2 Dot Product 5.3 Inner Product 5.4 Cross Product
|
|
- Jared Cook
- 5 years ago
- Views:
Transcription
1 Chapter 5 Dot Inner and Cross Prodcts 5. Length of a ector 5. Dot Prodct 5.3 Inner Prodct 5.4 Cross Prodct
2 5. Length and Dot Prodct in R n Length: The length of a ector ( n in R n is gien by n Notes: The length of a ector is also called its norm. Notes: Properties of length is called a nit ector. 0 iff 0 c c /40
3 Ex : (a In R 5 the length of ( 0 4 is gien by 0 ( 4 ( 5 5 (b In R 3 the length of ( is gien by ( is a nit ector 3/40
4 A standard nit ector in R n : e e e n Ex: the standard nit ector in R : the standard nit ector in R 3 : i j 0 0 i j k Notes: (Two nonzero ectors are parallel c c c 0 0 and hae the same direction and hae the opposite direction 4/40
5 Thm 5.: (Length of a scalar mltiple Let be a ector in R n and c be a scalar. Then Pf: c ( c ( c c c ( n c c c ( cn ( c c c n ( c n c ( n c c n 5/40
6 Thm 5.: (Unit ector in the direction of If is a nonzero ector in R n then the ector has length and has the same direction as. This ector is called the nit ector in the direction of. Pf: ( has the same direction as is nonzero 0 0 ( has length 6/40
7 Notes: ( The ector is called the nit ector in the direction of. ( The process of finding the nit ector in the direction of is called normalizing the ector. 7/40
8 Ex : (Finding a nit ector Find the nit ector in the direction of ( 3 and erify that this ector has length. Sol: ( (3 3 ( 4 ( is a nit ector /40
9 Distance between two ectors: The distance between two ectors and in R n is d( Notes: (Properties of distance ( ( d( 0 d( 0 if and only if (3 d( d( 9/40
10 Ex 3: (Finding the distance between two ectors The distance between =(0 and =( 0 is d( (0 0 ( 3 0/40
11 Keywords in Section 5.: طول length: معيار norm: متجه الوحدة ector: nit متجه الوحدة األساسي : ector standard nit معايرة normalizing: المسافة distance: زاوية angle: متباينة المثلث ineqality: triangle نظرية فيثاغورس theorem: Pythagorean /40
12 5. Dot Prodct Dot prodct in R n : The dot prodct of ( n and ( n is the scalar qantity n n Ex 4: (Finding the dot prodct of two ectors The dot prodct of =( 0-3 and =(3-4 is ( (3 (( (0(4 ( 3( 7 /40
13 Thm 5.3: (Properties of the dot prodct If and w are ectors in R n and c is a scalar then the following properties are tre. ( ( ( w w (3 c( ( c ( c (4 (5 0 and 0 if and only if 0 3/40
14 Eclidean n-space: R n was defined to be the set of all order n-tples of real nmbers. When R n is combined with the standard operations of ector addition scalar mltiplication ector length and the dot prodct the reslting ector space is called Eclidean n-space. 4/40
15 Ex 5: (Finding dot prodcts ( (5 8 w ( 4 3 (a (b ( w (c ( (d (e Sol: ( a ((5 ( (8 6 ( b ( w 6w 6( 4 3 (4 8 w ( w ( c ( ( ( 6 (d w w w ( 4( 4 (3(3 5 ( e w (5 ( 8 86 (3 ( w ((3 ( ( 6 4 5/40
16 Ex 6: (Using the properties of the dot prodct Sol: Gien Find ( (3 ( (3 (3 (3 (3 ( (3 ( 3( 6( ( 3( 7( ( 3(39 7( 3 ( /40
17 Thm 5.4: (The Cachy - Schwarz ineqality If and are ectors in R n then ( denotes the absolte ale of Ex 7: (An example of the Cachy - Schwarz ineqality Verify the Cachy - Schwarz ineqality for =( - 3 and =( 0 - Sol: /40
18 The angle between two ectors in R n : cos 0 Opposite direction Same direction Note: cos The angle between the zero ector and another ector is not defined. cos 0 cos 0 0 cos 0 0 cos 8/40
19 Ex 8: (Finding the angle between two ectors ( 4 0 ( 0 Sol: ( 4( (0(0 (( ( ( cos and hae opposite directions. ( 9/40
20 Orthogonal ectors: Two ectors and in R n are orthogonal if 0 Note: The ector 0 is said to be orthogonal to eery ector. 0/40
21 Ex 0: (Finding orthogonal ectors Determine all ectors in R n that are orthogonal to =(4. Sol: (4 Let (4 ( 4 0 t t ( t t t R /40
22 Thm 5.5: (The triangle ineqality Pf: If and are ectors in R n then ( ( ( ( ( ( ( Note: Eqality occrs in the triangle ineqality if and only if the ectors and hae the same direction. /40
23 Thm 5.6: (The Pythagorean theorem If and are ectors in R n then and are orthogonal if and only if 3/40
24 Dot prodct and matrix mltiplication: n n ] [ ] [ n n n n T (A ector in R n is represented as an n colmn matrix ( n 4/40
25 Keywords in Section 5.: الضرب النقطي prodct: dot فضاء نوني اقليدي n-space: Eclidean متباينة كوشي-شوارز ineqality: Cachy-Schwarz متباينة المثلث ineqality: triangle نظرية فيثاغورس theorem: Pythagorean 5/40
26 5.3 Inner Prodct Inner prodct: Let and w be ectors in a ector space V and let c be any scalar. An inner prodct on V is a fnction that associates a real nmber < > with each pair of ectors and and satisfies the following axioms. ( ( (3 w w c c (4 0 and 0 if and only if 0 6/40
27 Note: dot prodct (Eclidean inner prodct for R n general inner prodct for ector spacev Note: A ector space V with an inner prodct is called an inner prodct space. Vector space: V Inner prodct space: V 7/40
28 Ex : (The Eclidean inner prodct for R n Show that the dot prodct in R n satisfies the for axioms of an inner prodct. Sol: ( ( n n n n By Theorem 5.3 this dot prodct satisfies the reqired for axioms. Ths it is an inner prodct on R n. 8/40
29 Ex : (A different inner prodct for R n Show that the fnction defines an inner prodct on R where and. ( ( Sol: ( a w w ( ( ( ( w w w w w w ( ( w w b w 9/40
30 Note: (An inner prodct on R n 0 i n n n c c c c ( ( ( ( c c c c c c 0 ( d 0 ( /40
31 Ex 3: (A fnction that is not an inner prodct Show that the following fnction is not an inner prodct on R Sol: Let ( Then (( (( (( 6 0 Axiom 4 is not satisfied. Ths this fnction is not an inner prodct on R 3. 3/40
32 Thm 5.7: (Properties of inner prodcts Let and w be ectors in an inner prodct space V and let c be any real nmber. ( ( w w w (3 c c Norm (length of : Note: 3/40
33 Distance between and : d( Angle between two nonzero ectors and : cos 0 Orthogonal: ( and are orthogonal if 0. 33/40
34 Notes: ( If then is called a nit ector. ( 0 not a nit ector Normalizing (the nit ector in the direction of 34/40
35 Ex 6: (Finding inner prodct Sol: p q a b a b a b is an inner prodct 0 0 n n Let p( x x q( x 4 x x be polynomials in P ( x ( a p q? ( b q? ( c d( p q? ( a p q ((4 (0( ( ( ( b q q q 4 ( ( c p q 3 x 3x d( p q p q p q p q ( 3 ( 3 35/40
36 Properties of norm: ( ( 0 if and only if (3 0 0 c c Properties of distance: ( d( 0 ( d( 0 if and only if (3 d( d( 36/40
37 Thm 5.8: Let and be ectors in an inner prodct space V. ( Cachy-Schwarz ineqality: Theorem 5.4 ( Triangle ineqality: Theorem 5.5 (3 Pythagorean theorem : and are orthogonal if and only if Theorem /40
38 Orthogonal projections in inner prodct spaces: Note: Let and be two ectors in an inner prodct space V sch that 0. Then the orthogonal projection of onto is gien by proj If is a init ector then. The formla for the orthogonal projection of onto takes the following simpler form. proj 38/40
39 Ex 0: (Finding an orthogonal projection in R 3 Use the Eclidean inner prodct in R 3 to find the orthogonal projection of =(6 4 onto =( 0. Sol: (6( (( (4( proj 0 5 ( 0 ( 4 0 Note: proj (6 4 (4 0 (4 4is orthogonal to (0. 39/40
40 Thm 5.9: (Orthogonal projection and distance Let and be two ectors in an inner prodct space V sch that 0. Then d( proj d( c c 40/40
41 Keywords in Section 5.: ضرب داخلي prodct: inner فضاء الضرب الداخلي space: inner prodct معيار norm: مسافة distance: زاوية angle: متعامد orthogonal: متجه وحدة ector: nit معايرة normalizing: متباينة كوشي - شوارز ineqality: Cachy Schwarz متباينة المثلث ineqality: triangle نظرية فيثاغورس theorem: Pythagorean اسقاط عمودي projection: orthogonal 4/40
42 5.4 Cross Prodct Cross prodct in R 3 : The cross prodct of is the ector qantity w ( and ( 3 i j k w Ex : (Finding the cross prodct of two ectors The cross prodct of =( 0 and =(3-4 is w /40
43 Keywords in Section 5.4: ضرب خارجي prodct: Cross فضاء الضرب الداخلي space: inner prodct معيار norm: مسافة distance: زاوية angle: متعامد orthogonal: 43/40
Vectors in Rn un. This definition of norm is an extension of the Pythagorean Theorem. Consider the vector u = (5, 8) in R 2
MATH 307 Vectors in Rn Dr. Neal, WKU Matrices of dimension 1 n can be thoght of as coordinates, or ectors, in n- dimensional space R n. We can perform special calclations on these ectors. In particlar,
More information6.4 VECTORS AND DOT PRODUCTS
458 Chapter 6 Additional Topics in Trigonometry 6.4 VECTORS AND DOT PRODUCTS What yo shold learn ind the dot prodct of two ectors and se the properties of the dot prodct. ind the angle between two ectors
More informationu v u v v 2 v u 5, 12, v 3, 2 3. u v u 3i 4j, v 7i 2j u v u 4i 2j, v i j 6. u v u v u i 2j, v 2i j 9.
Section. Vectors and Dot Prodcts 53 Vocablary Check 1. dot prodct. 3. orthogonal. \ 5. proj PQ F PQ \ ; F PQ \ 1., 1,, 3. 5, 1, 3, 3., 1,, 3 13 9 53 1 9 13 11., 5, 1, 5. i j, i j. 3i j, 7i j 1 5 1 1 37
More informationInner products. Theorem (basic properties): Given vectors u, v, w in an inner product space V, and a scalar k, the following properties hold:
Inner products Definition: An inner product on a real vector space V is an operation (function) that assigns to each pair of vectors ( u, v) in V a scalar u, v satisfying the following axioms: 1. u, v
More informationLecture 9: 3.4 The Geometry of Linear Systems
Lectre 9: 3.4 The Geometry of Linear Systems Wei-Ta Ch 200/0/5 Dot Prodct Form of a Linear System Recall that a linear eqation has the form a x +a 2 x 2 + +a n x n = b (a,a 2,, a n not all zero) The corresponding
More informationA vector in the plane is directed line segment. The directed line segment AB
Vector: A ector is a matrix that has only one row then we call the matrix a row ector or only one column then we call it a column ector. A row ector is of the form: a a a... A column ector is of the form:
More informationLesson 81: The Cross Product of Vectors
Lesson 8: The Cross Prodct of Vectors IBHL - SANTOWSKI In this lesson yo will learn how to find the cross prodct of two ectors how to find an orthogonal ector to a plane defined by two ectors how to find
More informationThe Cross Product of Two Vectors in Space DEFINITION. Cross Product. u * v = s ƒ u ƒƒv ƒ sin ud n
12.4 The Cross Prodct 873 12.4 The Cross Prodct In stdying lines in the plane, when we needed to describe how a line was tilting, we sed the notions of slope and angle of inclination. In space, we want
More informationLinear Algebra. Alvin Lin. August December 2017
Linear Algebra Alvin Lin August 207 - December 207 Linear Algebra The study of linear algebra is about two basic things. We study vector spaces and structure preserving maps between vector spaces. A vector
More information3.3 Operations With Vectors, Linear Combinations
Operations With Vectors, Linear Combinations Performance Criteria: (d) Mltiply ectors by scalars and add ectors, algebraically Find linear combinations of ectors algebraically (e) Illstrate the parallelogram
More informationElementary Linear Algebra
Elemetary Liear Algebra Ato & Rorres th Editio Lectre Set Chapter : Eclidea Vector Spaces Chapter Cotet Vectors i -Space -Space ad -Space Norm Distace i R ad Dot Prodct Orthogoality Geometry of Liear Systems
More informationSECTION 6.7. The Dot Product. Preview Exercises. 754 Chapter 6 Additional Topics in Trigonometry. 7 w u 7 2 =?. 7 v 77w7
754 Chapter 6 Additional Topics in Trigonometry 115. Yo ant to fly yor small plane de north, bt there is a 75-kilometer ind bloing from est to east. a. Find the direction angle for here yo shold head the
More informationUnit 11: Vectors in the Plane
135 Unit 11: Vectors in the Plane Vectors in the Plane The term ector is used to indicate a quantity (such as force or elocity) that has both length and direction. For instance, suppose a particle moes
More informationMon Apr dot product, length, orthogonality, projection onto the span of a single vector. Announcements: Warm-up Exercise:
Math 2270-004 Week 2 notes We will not necessarily finish the material from a gien day's notes on that day. We may also add or subtract some material as the week progresses, but these notes represent an
More informationLecture 3. (2) Last time: 3D space. The dot product. Dan Nichols January 30, 2018
Lectre 3 The dot prodct Dan Nichols nichols@math.mass.ed MATH 33, Spring 018 Uniersity of Massachsetts Janary 30, 018 () Last time: 3D space Right-hand rle, the three coordinate planes 3D coordinate system:
More informationGarret Sobczyk s 2x2 Matrix Derivation
Garret Sobczyk s x Matrix Derivation Krt Nalty May, 05 Abstract Using matrices to represent geometric algebras is known, bt not necessarily the best practice. While I have sed small compter programs to
More informationLinear Algebra Massoud Malek
CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product
More informationCS 450: COMPUTER GRAPHICS VECTORS SPRING 2016 DR. MICHAEL J. REALE
CS 45: COMPUTER GRPHICS VECTORS SPRING 216 DR. MICHEL J. RELE INTRODUCTION In graphics, we are going to represent objects and shapes in some form or other. First, thogh, we need to figre ot how to represent
More informationHomework 5 Solutions
Q Homework Soltions We know that the colmn space is the same as span{a & a ( a * } bt we want the basis Ths we need to make a & a ( a * linearly independent So in each of the following problems we row
More information1 The space of linear transformations from R n to R m :
Math 540 Spring 20 Notes #4 Higher deriaties, Taylor s theorem The space of linear transformations from R n to R m We hae discssed linear transformations mapping R n to R m We can add sch linear transformations
More informationOPTIMUM EXPRESSION FOR COMPUTATION OF THE GRAVITY FIELD OF A POLYHEDRAL BODY WITH LINEARLY INCREASING DENSITY 1
OPTIMUM EXPRESSION FOR COMPUTATION OF THE GRAVITY FIEL OF A POLYHERAL BOY WITH LINEARLY INCREASING ENSITY 1 V. POHÁNKA2 Abstract The formla for the comptation of the gravity field of a polyhedral body
More informationMath 144 Activity #10 Applications of Vectors
144 p 1 Math 144 Actiity #10 Applications of Vectors In the last actiity, yo were introdced to ectors. In this actiity yo will look at some of the applications of ectors. Let the position ector = a, b
More information12.1 Three Dimensional Coordinate Systems (Review) Equation of a sphere
12.2 Vectors 12.1 Three Dimensional Coordinate Systems (Reiew) Equation of a sphere x a 2 + y b 2 + (z c) 2 = r 2 Center (a,b,c) radius r 12.2 Vectors Quantities like displacement, elocity, and force inole
More information3.4-Miscellaneous Equations
.-Miscellaneos Eqations Factoring Higher Degree Polynomials: Many higher degree polynomials can be solved by factoring. Of particlar vale is the method of factoring by groping, however all types of factoring
More informationDot product. The dot product is an inner product on a coordinate vector space (Definition 1, Theorem
Dot product The dot product is an inner product on a coordinate vector space (Definition 1, Theorem 1). Definition 1 Given vectors v and u in n-dimensional space, the dot product is defined as, n v u v
More informationRecall: Dot product on R 2 : u v = (u 1, u 2 ) (v 1, v 2 ) = u 1 v 1 + u 2 v 2, u u = u u 2 2 = u 2. Geometric Meaning:
Recall: Dot product on R 2 : u v = (u 1, u 2 ) (v 1, v 2 ) = u 1 v 1 + u 2 v 2, u u = u 2 1 + u 2 2 = u 2. Geometric Meaning: u v = u v cos θ. u θ v 1 Reason: The opposite side is given by u v. u v 2 =
More informationInner Product Spaces 6.1 Length and Dot Product in R n
Inner Product Spaces 6.1 Length and Dot Product in R n Summer 2017 Goals We imitate the concept of length and angle between two vectors in R 2, R 3 to define the same in the n space R n. Main topics are:
More informationElementary Linear Algebra
Elementry Liner Algebr Anton & Rorres, 9 th Edition Lectre Set Chpter : Vectors in -Spce nd -Spce Chpter Content Introdction to Vectors (Geometric Norm of Vector; Vector Arithmetic Dot Prodct; Projections
More informationCONTENTS. INTRODUCTION MEQ curriculum objectives for vectors (8% of year). page 2 What is a vector? What is a scalar? page 3, 4
CONTENTS INTRODUCTION MEQ crriclm objectives for vectors (8% of year). page 2 What is a vector? What is a scalar? page 3, 4 VECTOR CONCEPTS FROM GEOMETRIC AND ALGEBRAIC PERSPECTIVES page 1 Representation
More informationCHAPTER 3 : VECTORS. Definition 3.1 A vector is a quantity that has both magnitude and direction.
EQT 101-Engineering Mathematics I Teaching Module CHAPTER 3 : VECTORS 3.1 Introduction Definition 3.1 A ector is a quantity that has both magnitude and direction. A ector is often represented by an arrow
More informationMAT 111 Linear Algebra [Aljabar Linear]
UNIVERSITI SINS MLYSI First Semester Eamination 2/22 cademic Session Janary 22 MT Linear lgebra [ljabar Linear] Dration : 3 hors [Masa : 3 jam] Please check that this eamination paper consists of SEVEN
More informationEuclidean Vector Spaces
CHAPTER 3 Eclidean Vector Spaces CHAPTER CONTENTS 3.1 Vectors in 2-Space, 3-Space, and n-space 131 3.2 Norm, Dot Prodct, and Distance in R n 142 3.3 Orthogonalit 155 3.4 The Geometr of Linear Sstems 164
More informationSection 7.5 Inner Product Spaces
Section 7.5 Inner Product Spaces With the dot product defined in Chapter 6, we were able to study the following properties of vectors in R n. ) Length or norm of a vector u. ( u = p u u ) 2) Distance of
More informationEGR2013 Tutorial 8. Linear Algebra. Powers of a Matrix and Matrix Polynomial
EGR1 Tutorial 8 Linear Algebra Outline Powers of a Matrix and Matrix Polynomial Vector Algebra Vector Spaces Powers of a Matrix and Matrix Polynomial If A is a square matrix, then we define the nonnegative
More information6.1.1 Angle between Two Lines Intersection of Two lines Shortest Distance from a Point to a Line
CHAPTER 6 : VECTORS 6. Lines in Space 6.. Angle between Two Lines 6.. Intersection of Two lines 6..3 Shortest Distance from a Point to a Line 6. Planes in Space 6.. Intersection of Two Planes 6.. Angle
More informationChapter 4 Linear Models
Chapter 4 Linear Models General Linear Model Recall signal + WG case: x[n] s[n;] + w[n] x s( + w Here, dependence on is general ow we consider a special case: Linear Observations : s( H + b known observation
More informationMATH 19520/51 Class 2
MATH 19520/51 Class 2 Minh-Tam Trinh University of Chicago 2017-09-27 1 Review dot product. 2 Angles between vectors and orthogonality. 3 Projection of one vector onto another. 4 Cross product and its
More informationInner Product Spaces
Inner Product Spaces Linear Algebra Josh Engwer TTU 28 October 2015 Josh Engwer (TTU) Inner Product Spaces 28 October 2015 1 / 15 Inner Product Space (Definition) An inner product is the notion of a dot
More informationLast week we presented the following expression for the angles between two vectors u and v in R n ( ) θ = cos 1 u v
Orthogonality (I) Last week we presented the following expression for the angles between two vectors u and v in R n ( ) θ = cos 1 u v u v which brings us to the fact that θ = π/2 u v = 0. Definition (Orthogonality).
More informationSection 7.4: Integration of Rational Functions by Partial Fractions
Section 7.4: Integration of Rational Fnctions by Partial Fractions This is abot as complicated as it gets. The Method of Partial Fractions Ecept for a few very special cases, crrently we have no way to
More informationVectors and the Geometry of Space
Vectors and the Geometry of Space Many quantities in geometry and physics, such as area, volume, temperature, mass, and time, can be characterized by a single real number scaled to appropriate units of
More informationv = v 1 2 +v 2 2. Two successive applications of this idea give the length of the vector v R 3 :
Length, Angle and the Inner Product The length (or norm) of a vector v R 2 (viewed as connecting the origin to a point (v 1,v 2 )) is easily determined by the Pythagorean Theorem and is denoted v : v =
More informationVectors in R n. P. Danziger
1 Vectors in R n P. Danziger 1 Vectors The standard geometric definition of ector is as something which has direction and magnitude but not position. Since ectors hae no position we may place them whereer
More informationGeometry of Span (continued) The Plane Spanned by u and v
Geometric Description of Span Geometr of Span (contined) 2 Geometr of Span (contined) 2 Span {} Span {, } 2 Span {} 2 Geometr of Span (contined) 2 b + 2 The Plane Spanned b and If a plane is spanned b
More informationThe Real Stabilizability Radius of the Multi-Link Inverted Pendulum
Proceedings of the 26 American Control Conference Minneapolis, Minnesota, USA, Jne 14-16, 26 WeC123 The Real Stabilizability Radis of the Mlti-Link Inerted Pendlm Simon Lam and Edward J Daison Abstract
More informationThe Heat Equation and the Li-Yau Harnack Inequality
The Heat Eqation and the Li-Ya Harnack Ineqality Blake Hartley VIGRE Research Paper Abstract In this paper, we develop the necessary mathematics for nderstanding the Li-Ya Harnack ineqality. We begin with
More informationINNER PRODUCT SPACE. Definition 1
INNER PRODUCT SPACE Definition 1 Suppose u, v and w are all vectors in vector space V and c is any scalar. An inner product space on the vectors space V is a function that associates with each pair of
More informationVECTORS IN 2-SPACE AND 3-SPACE GEOMETRIC VECTORS VECTORS OPERATIONS DOT PRODUCT; PROJECTIONS CROSS PRODUCT
VECTORS IN -SPACE AND 3-SPACE GEOMETRIC VECTORS VECTORS OPERATIONS DOT PRODUCT; PROJECTIONS CROSS PRODUCT GEOMETRIC VECTORS Vectors can represented geometrically as directed line segments or arrows in
More informationOn the circuit complexity of the standard and the Karatsuba methods of multiplying integers
On the circit complexity of the standard and the Karatsba methods of mltiplying integers arxiv:1602.02362v1 [cs.ds] 7 Feb 2016 Igor S. Sergeev The goal of the present paper is to obtain accrate estimates
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More informationProjection Theorem 1
Projection Theorem 1 Cauchy-Schwarz Inequality Lemma. (Cauchy-Schwarz Inequality) For all x, y in an inner product space, [ xy, ] x y. Equality holds if and only if x y or y θ. Proof. If y θ, the inequality
More informationGraphs and Networks Lecture 5. PageRank. Lecturer: Daniel A. Spielman September 20, 2007
Graphs and Networks Lectre 5 PageRank Lectrer: Daniel A. Spielman September 20, 2007 5.1 Intro to PageRank PageRank, the algorithm reportedly sed by Google, assigns a nmerical rank to eery web page. More
More informationMarch 27 Math 3260 sec. 56 Spring 2018
March 27 Math 3260 sec. 56 Spring 2018 Section 4.6: Rank Definition: The row space, denoted Row A, of an m n matrix A is the subspace of R n spanned by the rows of A. We now have three vector spaces associated
More informationChange of Variables. (f T) JT. f = U
Change of Variables 4-5-8 The change of ariables formla for mltiple integrals is like -sbstittion for single-ariable integrals. I ll gie the general change of ariables formla first, and consider specific
More information6.1. Inner Product, Length and Orthogonality
These are brief notes for the lecture on Friday November 13, and Monday November 1, 2009: they are not complete, but they are a guide to what I want to say on those days. They are guaranteed to be incorrect..1.
More informationTHE DIFFERENTIAL GEOMETRY OF REGULAR CURVES ON A REGULAR TIME-LIKE SURFACE
Dynamic Systems and Applications 24 2015 349-360 TH DIFFRNTIAL OMTRY OF RULAR CURVS ON A RULAR TIM-LIK SURFAC MIN OZYILMAZ AND YUSUF YAYLI Department of Mathematics ge Uniersity Bornoa Izmir 35100 Trkey
More informationMATH 12 CLASS 2 NOTES, SEP Contents. 2. Dot product: determining the angle between two vectors 2
MATH 12 CLASS 2 NOTES, SEP 23 2011 Contents 1. Dot product: definition, basic properties 1 2. Dot product: determining the angle between two vectors 2 Quick links to definitions/theorems Dot product definition
More informationLecture Note on Linear Algebra 17. Standard Inner Product
Lecture Note on Linear Algebra 17. Standard Inner Product Wei-Shi Zheng, wszheng@ieee.org, 2011 November 21, 2011 1 What Do You Learn from This Note Up to this point, the theory we have established can
More informationQuadratic and Rational Inequalities
Chapter Qadratic Eqations and Ineqalities. Gidelines for solving word problems: (a) Write a verbal model that will describe what yo need to know. (b) Assign labels to each part of the verbal model nmbers
More informationorthogonal relations between vectors and subspaces Then we study some applications in vector spaces and linear systems, including Orthonormal Basis,
5 Orthogonality Goals: We use scalar products to find the length of a vector, the angle between 2 vectors, projections, orthogonal relations between vectors and subspaces Then we study some applications
More informationMATH2715: Statistical Methods
MATH275: Statistical Methods Exercises III (based on lectres 5-6, work week 4, hand in lectre Mon 23 Oct) ALL qestions cont towards the continos assessment for this modle. Q. If X has a niform distribtion
More informationDefinition 1. A set V is a vector space over the scalar field F {R, C} iff. there are two operations defined on V, called vector addition
6 Vector Spaces with Inned Product Basis and Dimension Section Objective(s): Vector Spaces and Subspaces Linear (In)dependence Basis and Dimension Inner Product 6 Vector Spaces and Subspaces Definition
More informationMTH 2032 SemesterII
MTH 202 SemesterII 2010-11 Linear Algebra Worked Examples Dr. Tony Yee Department of Mathematics and Information Technology The Hong Kong Institute of Education December 28, 2011 ii Contents Table of Contents
More informationVectors. Vectors ( 向量 ) Representation of Vectors. Special Vectors. Equal vectors. Chapter 16
Vectors ( 向量 ) Chapter 16 2D Vectors A vector is a line which has both magnitde and direction. For example, in a weather report yo may hear a statement like the wind is blowing at 25 knots ( 海浬 ) in the
More informationVector Spaces. distributive law u,v. Associative Law. 1 v v. Let 1 be the unit element in F, then
1 Def: V be a set of elements with a binary operation + is defined. F be a field. A multiplication operator between a F and v V is also defined. The V is called a vector space over the field F if: V is
More informationMTH 2310, FALL Introduction
MTH 2310, FALL 2011 SECTION 6.2: ORTHOGONAL SETS Homework Problems: 1, 5, 9, 13, 17, 21, 23 1, 27, 29, 35 1. Introduction We have discussed previously the benefits of having a set of vectors that is linearly
More informationMath 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 informationFourier and Wavelet Signal Processing
Ecole Polytechnique Federale de Lausanne (EPFL) Audio-Visual Communications Laboratory (LCAV) Fourier and Wavelet Signal Processing Martin Vetterli Amina Chebira, Ali Hormati Spring 2011 2/25/2011 1 Outline
More information4.4 Moment of a Force About a Line
4.4 Moment of a orce bot a Line 4.4 Moment of a orce bot a Line Eample 1, page 1 of 3 1. orce is applied to the end of gearshift lever DE. Determine the moment of abot shaft. State which wa the lever will
More informationExercise Solutions for Introduction to 3D Game Programming with DirectX 10
Exercise Solutions for Introduction to 3D Game Programming with DirectX 10 Frank Luna, September 6, 009 Solutions to Part I Chapter 1 1. Let u = 1, and v = 3, 4. Perform the following computations and
More informationFEA Solution Procedure
EA Soltion Procedre (demonstrated with a -D bar element problem) EA Procedre for Static Analysis. Prepare the E model a. discretize (mesh) the strctre b. prescribe loads c. prescribe spports. Perform calclations
More informationLinear Algebra: Homework 3
Linear Algebra: Homework 3 Alvin Lin August 206 - December 206 Section.2 Exercise 48 Find all values of the scalar k for which the two vectors are orthogonal. [ ] [ ] 2 k + u v 3 k u v 0 2(k + ) + 3(k
More informationInner Product Spaces
Inner Product Spaces Introduction Recall in the lecture on vector spaces that geometric vectors (i.e. vectors in two and three-dimensional Cartesian space have the properties of addition, subtraction,
More informationMath 3191 Applied Linear Algebra
Math 191 Applied Linear Algebra Lecture 1: Inner Products, Length, Orthogonality Stephen Billups University of Colorado at Denver Math 191Applied Linear Algebra p.1/ Motivation Not all linear systems have
More informationReview of Linear Algebra
Review of Linear Algebra Definitions An m n (read "m by n") matrix, is a rectangular array of entries, where m is the number of rows and n the number of columns. 2 Definitions (Con t) A is square if m=
More informationAffine Invariant Total Variation Models
Affine Invariant Total Variation Models Helen Balinsky, Alexander Balinsky Media Technologies aboratory HP aboratories Bristol HP-7-94 Jne 6, 7* Total Variation, affine restoration, Sobolev ineqality,
More information6.4 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 informationLecture # 3 Orthogonal Matrices and Matrix Norms. We repeat the definition an orthogonal set and orthornormal set.
Lecture # 3 Orthogonal Matrices and Matrix Norms We repeat the definition an orthogonal set and orthornormal set. Definition A set of k vectors {u, u 2,..., u k }, where each u i R n, is said to be an
More informationWorksheet for Lecture 23 (due December 4) Section 6.1 Inner product, length, and orthogonality
Worksheet for Lecture (due December 4) Name: Section 6 Inner product, length, and orthogonality u Definition Let u = u n product or dot product to be and v = v v n be vectors in R n We define their inner
More informationA Geometric Review of Linear Algebra
A Geometric Reiew of Linear Algebra The following is a compact reiew of the primary concepts of linear algebra. The order of presentation is unconentional, with emphasis on geometric intuition rather than
More informationOn relative errors of floating-point operations: optimal bounds and applications
On relative errors of floating-point operations: optimal bonds and applications Clade-Pierre Jeannerod, Siegfried M. Rmp To cite this version: Clade-Pierre Jeannerod, Siegfried M. Rmp. On relative errors
More informationINPUT-OUTPUT APPROACH NUMERICAL EXAMPLES
INPUT-OUTPUT APPROACH NUMERICAL EXAMPLES EXERCISE s consider the linear dnamical sstem of order 2 with transfer fnction with Determine the gain 2 (H) of the inpt-otpt operator H associated with this sstem.
More informationSetting The K Value And Polarization Mode Of The Delta Undulator
LCLS-TN-4- Setting The Vale And Polarization Mode Of The Delta Undlator Zachary Wolf, Heinz-Dieter Nhn SLAC September 4, 04 Abstract This note provides the details for setting the longitdinal positions
More informationStudy on the Mathematic Model of Product Modular System Orienting the Modular Design
Natre and Science, 2(, 2004, Zhong, et al, Stdy on the Mathematic Model Stdy on the Mathematic Model of Prodct Modlar Orienting the Modlar Design Shisheng Zhong 1, Jiang Li 1, Jin Li 2, Lin Lin 1 (1. College
More informationDot Products. K. Behrend. April 3, Abstract A short review of some basic facts on the dot product. Projections. The spectral theorem.
Dot Products K. Behrend April 3, 008 Abstract A short review of some basic facts on the dot product. Projections. The spectral theorem. Contents The dot product 3. Length of a vector........................
More informationMath Linear Algebra
Math 220 - Linear Algebra (Summer 208) Solutions to Homework #7 Exercise 6..20 (a) TRUE. u v v u = 0 is equivalent to u v = v u. The latter identity is true due to the commutative property of the inner
More informationClassify by number of ports and examine the possible structures that result. Using only one-port elements, no more than two elements can be assembled.
Jnction elements in network models. Classify by nmber of ports and examine the possible strctres that reslt. Using only one-port elements, no more than two elements can be assembled. Combining two two-ports
More informationMath Review. Week 1, Wed Jan 10
Uniersity of British Colbia CPSC 4 Coter Grahics Jan-Ar 007 Taara Mnzner Math Reiew Week, Wed Jan 0 htt://www.grad.cs.bc.ca/~cs4/vjan007 News sign sheet with nae, eail, rogra Reiew: Coter Grahics Defined
More informationInner Product Spaces 5.2 Inner product spaces
Inner Product Spaces 5.2 Inner product spaces November 15 Goals Concept of length, distance, and angle in R 2 or R n is extended to abstract vector spaces V. Sucn a vector space will be called an Inner
More informationReview of Matrices and Vectors 1/45
Reiew of Matrices and Vectors /45 /45 Definition of Vector: A collection of comple or real numbers, generally put in a column [ ] T "! Transpose + + + b a b a b b a a " " " b a b a Definition of Vector
More informationMath 261 Lecture Notes: Sections 6.1, 6.2, 6.3 and 6.4 Orthogonal Sets and Projections
Math 6 Lecture Notes: Sections 6., 6., 6. and 6. Orthogonal Sets and Projections We will not cover general inner product spaces. We will, however, focus on a particular inner product space the inner product
More informationwhich has a check digit of 9. This is consistent with the first nine digits of the ISBN, since
vector Then the check digit c is computed using the following procedure: 1. Form the dot product. 2. Divide by 11, thereby producing a remainder c that is an integer between 0 and 10, inclusive. The check
More informationWhen are Two Numerical Polynomials Relatively Prime?
J Symbolic Comptation (1998) 26, 677 689 Article No sy980234 When are Two Nmerical Polynomials Relatively Prime? BERNHARD BECKERMANN AND GEORGE LABAHN Laboratoire d Analyse Nmériqe et d Optimisation, Université
More informationQuantities which have only magnitude are called scalars. Quantities which have magnitude and direction are called vectors.
Vectors summary Quantities which have only magnitude are called scalars. Quantities which have magnitude and direction are called vectors. AB is the position vector of B relative to A and is the vector
More informationCovariance and Dot Product
Covariance and Dot Product 1 Introduction. As you learned in Calculus III and Linear Algebra, the dot product of two vectors x = (x 1,..., x n ) and y = (y 1,..., y n ) in R n is the number n := x i y
More informationThe Dot Product
The Dot Product 1-9-017 If = ( 1,, 3 ) and = ( 1,, 3 ) are ectors, the dot product of and is defined algebraically as = 1 1 + + 3 3. Example. (a) Compute the dot product (,3, 7) ( 3,,0). (b) Compute the
More informationImage and Multidimensional Signal Processing
Image and Mltidimensional Signal Processing Professor William Hoff Dept of Electrical Engineering &Compter Science http://inside.mines.ed/~whoff/ Forier Transform Part : D discrete transforms 2 Overview
More informationOrthonormal Bases; Gram-Schmidt Process; QR-Decomposition
Orthonormal Bases; Gram-Schmidt Process; QR-Decomposition MATH 322, Linear Algebra I J. Robert Buchanan Department of Mathematics Spring 205 Motivation When working with an inner product space, the most
More informationThe Transpose of a Vector
8 CHAPTER Vectors The Transpose of a Vector We now consider the transpose of a vector in R n, which is a row vector. For a vector u 1 u. u n the transpose is denoted by u T = [ u 1 u u n ] EXAMPLE -5 Find
More informationSpring, 2008 CIS 610. Advanced Geometric Methods in Computer Science Jean Gallier Homework 1, Corrected Version
Spring, 008 CIS 610 Adanced Geometric Methods in Compter Science Jean Gallier Homework 1, Corrected Version Febrary 18, 008; De March 5, 008 A problems are for practice only, and shold not be trned in.
More information