Vectors in Rn un. This definition of norm is an extension of the Pythagorean Theorem. Consider the vector u = (5, 8) in R 2

Similar documents
Lecture 3. (2) Last time: 3D space. The dot product. Dan Nichols January 30, 2018

Lesson 81: The Cross Product of Vectors

The Cross Product of Two Vectors in Space DEFINITION. Cross Product. u * v = s ƒ u ƒƒv ƒ sin ud n

1 The space of linear transformations from R n to R m :

CONTENTS. INTRODUCTION MEQ curriculum objectives for vectors (8% of year). page 2 What is a vector? What is a scalar? page 3, 4

6.4 VECTORS AND DOT PRODUCTS

Graphs and Networks Lecture 5. PageRank. Lecturer: Daniel A. Spielman September 20, 2007

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

Lecture 9: 3.4 The Geometry of Linear Systems

The Brauer Manin obstruction

CS 450: COMPUTER GRAPHICS VECTORS SPRING 2016 DR. MICHAEL J. REALE

Math 425 Lecture 1: Vectors in R 3, R n

3.3 Operations With Vectors, Linear Combinations

Change of Variables. (f T) JT. f = U

u 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.

3.4-Miscellaneous Equations

SECTION 6.7. The Dot Product. Preview Exercises. 754 Chapter 6 Additional Topics in Trigonometry. 7 w u 7 2 =?. 7 v 77w7

Math 144 Activity #10 Applications of Vectors

different formulas, depending on whether or not the vector is in two dimensions or three dimensions.

MATH2715: Statistical Methods

Which of these statements are true? A) 1, 2 and 3 only C) 2, 4 and 5 only. B) 1, 2 and 5 only D) 1, 3 and 4 only

The Real Stabilizability Radius of the Multi-Link Inverted Pendulum

8.0 Definition and the concept of a vector:

Mon Apr dot product, length, orthogonality, projection onto the span of a single vector. Announcements: Warm-up Exercise:

Affine Invariant Total Variation Models

We automate the bivariate change-of-variables technique for bivariate continuous random variables with

Lecture Notes: Finite Element Analysis, J.E. Akin, Rice University

MAT389 Fall 2016, Problem Set 6

Vectors. Vectors ( 向量 ) Representation of Vectors. Special Vectors. Equal vectors. Chapter 16

Correction key. Example of an appropriate method. be the wind vector x = 120 and x = y = 160 and y = 10.

Differential Geometry. Peter Petersen

Linear Strain Triangle and other types of 2D elements. By S. Ziaei Rad

Setting The K Value And Polarization Mode Of The Delta Undulator

EE2 Mathematics : Functions of Multiple Variables

CHAPTER 3 : VECTORS. Definition 3.1 A vector is a quantity that has both magnitude and direction.

OPTIMUM EXPRESSION FOR COMPUTATION OF THE GRAVITY FIELD OF A POLYHEDRAL BODY WITH LINEARLY INCREASING DENSITY 1

Concept of Stress at a Point

10.2 Solving Quadratic Equations by Completing the Square

Roberto s Notes on Linear Algebra Chapter 1: Geometric vectors Section 8. The dot product

u P(t) = P(x,y) r v t=0 4/4/2006 Motion ( F.Robilliard) 1

Digital Image Processing. Lecture 8 (Enhancement in the Frequency domain) Bu-Ali Sina University Computer Engineering Dep.

Homework 5 Solutions

ECON3120/4120 Mathematics 2, spring 2009

sin u 5 opp } cos u 5 adj } hyp opposite csc u 5 hyp } sec u 5 hyp } opp Using Inverse Trigonometric Functions

LINEAR COMBINATIONS AND SUBSPACES

1. Solve Problem 1.3-3(c) 2. Solve Problem 2.2-2(b)

Spring, 2008 CIS 610. Advanced Geometric Methods in Computer Science Jean Gallier Homework 1, Corrected Version

Euclidean Vector Spaces

6.1.1 Angle between Two Lines Intersection of Two lines Shortest Distance from a Point to a Line

4.4 Moment of a Force About a Line

FEA Solution Procedure

Section 7.4: Integration of Rational Functions by Partial Fractions

ANOVA INTERPRETING. It might be tempting to just look at the data and wing it

LECTURE 2: CROSS PRODUCTS, MULTILINEARITY, AND AREAS OF PARALLELOGRAMS

Classify by number of ports and examine the possible structures that result. Using only one-port elements, no more than two elements can be assembled.

Formules relatives aux probabilités qui dépendent de très grands nombers

PhysicsAndMathsTutor.com

Momentum Equation. Necessary because body is not made up of a fixed assembly of particles Its volume is the same however Imaginary

Applied Linear Algebra in Geoscience Using MATLAB

ON THE PERFORMANCE OF LOW

MEG 741 Energy and Variational Methods in Mechanics I

m = Average Rate of Change (Secant Slope) Example:

THE DIFFERENTIAL GEOMETRY OF REGULAR CURVES ON A REGULAR TIME-LIKE SURFACE

2-9. The plate is subjected to the forces acting on members A and B as shown. If θ = 60 o, determine the magnitude of the resultant of these forces

5. The Bernoulli Equation

Chapter 4 Linear Models

UNDERSTAND MOTION IN ONE AND TWO DIMENSIONS

FLUCTUATING WIND VELOCITY CHARACTERISTICS OF THE WAKE OF A CONICAL HILL THAT CAUSE LARGE HORIZONTAL RESPONSE OF A CANTILEVER MODEL

The geometry of least squares

Math 116 First Midterm October 14, 2009

Elementary Linear Algebra

THREE AXIS CON TROL OF THE HUB BLE SPACE TELE SCOPE USING TWO RE AC TION WHEELS AND MAG NETIC TORQUER BARS FOR SCI ENCE OB SER VA TIONS

1 Undiscounted Problem (Deterministic)

Xihe Li, Ligong Wang and Shangyuan Zhang

Change of Variables. f(x, y) da = (1) If the transformation T hasn t already been given, come up with the transformation to use.

Success Center Math Tips

Garret Sobczyk s 2x2 Matrix Derivation

Technical Note. ODiSI-B Sensor Strain Gage Factor Uncertainty

A generalized Alon-Boppana bound and weak Ramanujan graphs

PHASE PLANE DIAGRAMS OF DIFFERENCE EQUATIONS. 1. Introduction

Chapter 6 Momentum Transfer in an External Laminar Boundary Layer

Formal Methods for Deriving Element Equations

Reduction of over-determined systems of differential equations

Microscopic Properties of Gases

Fixed points for discrete logarithms

The Dot Product

GEOMETRICAL DESCRIPTION OF ONE SURFACE IN ECONOMY

Math Review. Week 1, Wed Jan 10

On the Total Duration of Negative Surplus of a Risk Process with Two-step Premium Function

LINEAR ALGEBRA. and VECTOR GEOMETRY Volume 2 of 2 September 2014 edition

ECE Notes 4 Functions of a Complex Variable as Mappings. Fall 2017 David R. Jackson. Notes are adapted from D. R. Wilton, Dept.

Determining of temperature field in a L-shaped domain

On relative errors of floating-point operations: optimal bounds and applications

Cubic graphs have bounded slope parameter

Model (In-)Validation from a H and µ perspective

MEAN VALUE ESTIMATES OF z Ω(n) WHEN z 2.

Announcements Wednesday, September 05

Primary dependent variable is fluid velocity vector V = V ( r ); where r is the position vector

Chapter 6. Inverse Circular Functions and Trigonometric Equations. Section 6.1 Inverse Circular Functions y = 0

Linear Algebra. Alvin Lin. August December 2017

Transcription:

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, we can find special geometric interpretations for ectors in R 2 (the x y - plane) and for ectors in R 3. In fact, most of the definitions and theorems for R n are generalizations of reslts that can be seen geometrically in R 2 and R 3. Throghot, let = ( 1,..., n ) and = ( 1,..., n ) be ectors in R n. 0. Scalar Mltiplcation/Sm/Difference: (i) For any scalar c, c = (c 1,..., c n ). (ii) + = ( 1 + 1,..., n + n ) (iii) = ( 1 1,..., n n ), which gies the directional ector from to. I. Norm: The norm (or length or magnitde) of the ector is defined by = 1 2 +... + n 2. This definition of norm is an extension of the Pythagorean Theorem. Consider the ector = (5, 8) in R 2. Then the length of the line segment from (0, 0) to (5, 8) is = 5 2 + 8 2 = 89. In R 3, the norm of the ector (x, y, z) is simply the length of the segment from the origin to the point (x, y, z), and is gien by x 2 + y 2 + z 2. Howeer for n 4, we lose the geometric interpretation of length. So for = (4, 3, 5, 6) in R 4, the norm is simply = 4 2 +( 3) 2 + 5 2 + 6 2 = 86 withot the concept of length attached to it. Some Properties of Norm: (i) = (0, 0,, 0), (iii) 0, (ii) 2 = 2 1 +... + n 2 and (i) = 0 if and only if is the zero ector 0 c = c for any scalar c. II. Distance: The distance d(, ) between ectors and is gien by the length of the ector from to : d(, ) = = ( 1 1 ) 2 +... +( n n ) 2. This definition is an extension of the reslt in R 2. If = (5, 8) and = (10, 6), then the distance between these points is fond by the sal distance formla from algebra: d(, ) = (x 2 x 1 ) 2 +(y 2 y 1 ) 2 = 5 2 + ( 14) 2 = 221. Althogh we do not really hae a distance in R n for n 4, we still se the name distance for this measrement. Some Properties of Distance: (i) d(, ) 0, (ii) d(, ) = 0 and d(, ) > 0 if, and (iii) d(, ) = d(, ).

III. Dot Prodct: We define the dot prodct (also called inner prodct) between ectors and by = 1 1 +... + n n. In terms of matrix mltiplication, is really the matrix prodct T which is a 1 1 matrix. If = (4, 3, 5, 6) and = (8, 4, 2, 10), then = 32 12 10 + 60 = 70. Properties of Dot Prodct (a) = 2 1 +... + n 2 = 2 0. (b) = (c) = (d) For any scalar c, (c ) = c ( ) and (c ) = c ( ). (e) For another ector w, ( + ) w = ( w ) + ( w ). Parallelogram Diagonals: In R 2 and R 3, two non-collinear ectors and form a parallelogram. θ (3, 5) (7, 4) + Vectors = (3, 5) and = (7, 4) The long diagonal of the parallelogram aboe is gien by the ector + = (10, 9). The short diagonal gies the ector from to, which is gien by the difference = (4, 1). In R 2 and R 3, we can find the lengths of these diagonals sing the Law of Cosines. First, we let θ be the measre of the angle between and. Then 2 = 2 + 2 2 and cosθ, + 2 = 2 + 2 2 = 2 + 2 + 2 cos(π θ) cosθ.

These formlas are neer sed directly to find the lengths of the diagonals. For instance with = (3, 5) and = (7, 4), then + = (10, 9). Ths, + = 10 2 + 9 2 = 181. Howeer, the first formla can be sed to find the measre of the angle θ. Angle Between Vectors in R 2 and R 3 : Becase 2 = we can sole for cosθ and simplify the expression: 2 + 2 2 cosθ, cosθ = 2 + 2 ( 2 2 + ( ) ( )) = 2 + + = 2 = 2( ) 2 =. ( ) Ths, cosθ = and θ = cos 1. Note: It is always the case that 0º θ 180º. The angle θ between = (3, 5) and = (7, 4) is gien by θ = cos 1 41 34 65 29.29. We can compte cos 1 for any two ectors and in R n ; howeer, only in R 2 and R 3 does the ale gie the geometric interpretation of the measre of the angle between the ectors. Orthogonal Vectors: In R 2 and R 3, two ectors and are perpendiclar if and only if the angle θ between them is 90 ; that is, if and only if cosθ = 0. cosθ =, then cosθ = 0 if and only if the dot prodct eqals 0. Becase Ths in R 2 and R 3. we can say that and are perpendiclar if and only if = 0. For R n in general, we say that ectors and are orthogonal if and only if = 0. For example, if = ( 2, 0, 3, 0, 6) and = (3, 5, 2, 10, 0), then = 6 + 6 = 0; so and are orthogonal in R 5.

Identities in R n : Only in R 2 and R 3 do we hae the geometric interpretation of angle between ectors. Howeer the parallelogram identities can be generalized to R n as follows: 2 = 2 + 2 2( ) and + 2 = 2 + 2 + 2( ). We qickly obtain these reslts sing the properties of dot prodct: 2 = ( ) ( ) = + = + 2 = ( + ) ( + ) = + + + = 2 + 2 2( ), 2 + 2 + 2( ). Alternately in R 2 and R 3, 2 = 2 + 2 2 cosθ = 2 + 2 2 = 2 + 2 + 2( ) Cachy-Schwarz Ineqality: Let and be ectors in R n. Then Proof. If = assme 0 and 0, then = 0 and. = 0, so the ineqality holds. Ths we may > 0. Now let k be a scalar and consider the ector k +. Then 0 k + 2 = (k + ) (k + ) = k 2 ( ) + k( ) + k( ) + ( ) = 2 k 2 + 2( ) k + 2. This expression defines a qadratic in k which is always non-negatie; so the qadratic has zero roots or jst one root. Ths the discriminant b 2 4ac mst be less than or eqal to 0, where a = 2, b = 2( ), and c = 2. (If the discriminant were positie, then the qadratic wold hae two roots and therefore wold hae to be negatie oer some interal.) Ths, b 2 4ac = 4( ) 2 4 2 2 0, which implies that ( ) 2 2 2. By taking sqare roots we obtain. QED Triangle Ineqality: Let and be ectors in R n. Then + + Proof: We simply expand + 2. The first ineqality below arises from the fact that the nmber is less than or eqal to its absolte ale. The second ineqality is from Cachy-Schwarz..

+ 2 = ( + ) ( + ) = 2 + 2 + 2( ) 2 + 2 + 2 2 + 2 + 2 = + ( ) 2. Becase the sqare root fnction is increasing and + + by taking sqare roots. + 2 + ( ) 2, we obtain QED Note: For ectors and in R 2, the triangle ineqality states that length of the diagonal + can be no more than the sm of the lengths of the two sides and (hence, the name triangle ineqality). The reslt can be generalized to the distance between ectors as shown next. + Shortest Distance Between Points: Let and be ectors in R n. For any other ector w, we hae d(, ) d(, w ) + d( w, ). Proof. We se the definition of distance and apply the triangle ineqality: d(, ) = = ( w ) + ( w w + ) w = d( w, ) + d(, w ) = d(, w ) + d( w, ). QED The reslt states that the planar distance directly from and is less than or eqal to the sm of the distances from to w then from w to. We note that if,, and w are collinear with w between and, then d(, ) will eqal d(, w ) + d( w, ). Some Applications of Determinants to Vectors R 2 and R 3 Area of a Triangle: Three non-collinear points ( x 1, y 1 ), ( x 2, y 2 ), and ( x 3, y 3 ) form a triangle in the x y -plane. The area of the triangle is gien by Area = 1 x 1 y 1 1 2 det x 2 y 2 1. x 3 y 3 1 The aboe determinant eqals 0 if and only if the three points are on the same line.

With the points (10, 9), (7, 4), and (3, 5), 10 9 1 then det 7 4 1 = 23; ths the area of 3 5 1 the triangle is 23/2 = 11.5 sq. nits. Volme of a Tetrahedron: For non-planar points ( x 1, y 1, z 1 ), ( x 2, y 2, z 2 ), ( x 3, y 3, z 3 ), and ( x 4, y 4, z 4 ) form a tetrahedron in R 3. The olme is gien by x 1 y 1 z 1 1 Volme = 1 6 det x 2 y 2 z 2 1 x 3 y 3 z 3 1 x 4 y 4 z 4 1. The aboe determinant eqals 0 if and only if the for points are on the same plane. For the points (3, 0, 0), (0, 4, 0), (0, 0, 6), and (0, 0, 0), 3 0 0 1 0 4 0 1 then det = 72; ths the tetrahedral 0 0 6 1 0 0 0 1 olme is 72/6 = 12 nits cbed. (0, 0, 6) (0, 4, 0) (3, 0, 0) Example. Let = (5, 8, 5) and = ( 4, 6, 12). (a) Plot the points in R 3 and find their norms (i.e., lengths). (b) Find the ector from to, the direct distance from to, and the angle in between and. (c) Verify the Cachy-Schwarz Ineqality and the Triangle Ineqality sing and. (d) Let w = (1, 2, 3). Verify that d(, ) d(, w ) + d( w, ).

Soltion. (a) Below are the points = (5, 8, 5) and = ( 4, 6, 12) and some other points in R 3. z (5, 8, 5) (0, 8, 5) 5 4 ( 4, 6, 0) 8 6 y (5, 8, 0) 5 x ( 4, 6, 12) 12 (0, 6, 12) Below are the ectors = (5, 8, 5) and = ( 4, 6, 12). z (5, 8, 5) 4 8 6 y 5 x ( 4, 6, 12) The length of is = 5 2 + 8 2 + 5 2 = 114 10.677 and the length of is = 4 2 + 6 2 +12 2 = 196 =14.

= (5, 8, 5) and = ( 4, 6, 12) and w = (1, 2, 3). (b) The ector from to is = ( 9, 14, 17). Dr. Neal, WKU The direct distance from to is = 9 2 +14 2 +17 2 = 566 23.79. As before, = 114 and =14. The dot prodct is = 20 48 60 = 128. So the angle in between ectors and is θ = cos 1 z 128 148.9º. 114 14 = (5, 8, 5) 114 θ 14 x 566 = ( 4, 6, 12) (c) Cachy-Schwarz: =128 and ths, =14 114 149.479;. Triangle Ineqality: + = (1, 2, 7) and + = 1+ 4 + 49 = 54 7.35. Howeer, + = + 114 +14 24.677; ths, +. (d) From (b), d(, ) = = 566 23.79. Also, d(, w ) = w = 4 2 +10 2 + 2 2 = 120 d( w, ) = w = 5 2 + 4 2 +15 2 = 266 So d(, w ) + d( w, ) = 120 + 266 27.264 > d(, ). That is, d(, ) d(, w ) + d( w, ).