Chapter Five. More Dimensions. is simply the set of all ordered n-tuples of real numbers x = ( x 1

Similar documents
PURE MATHEMATICS A-LEVEL PAPER 1

1985 AP Calculus BC: Section I

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series

07 - SEQUENCES AND SERIES Page 1 ( Answers at he end of all questions ) b, z = n

Hadamard Exponential Hankel Matrix, Its Eigenvalues and Some Norms

H2 Mathematics Arithmetic & Geometric Series ( )

A Simple Proof that e is Irrational

Narayana IIT Academy

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

z 1+ 3 z = Π n =1 z f() z = n e - z = ( 1-z) e z e n z z 1- n = ( 1-z/2) 1+ 2n z e 2n e n -1 ( 1-z )/2 e 2n-1 1-2n -1 1 () z

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple

SCHUR S THEOREM REU SUMMER 2005

DTFT Properties. Example - Determine the DTFT Y ( e ) of n. Let. We can therefore write. From Table 3.1, the DTFT of x[n] is given by 1

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120

The Matrix Exponential

Continuity, Derivatives, and All That

Digital Signal Processing, Fall 2006

The Matrix Exponential

Discrete Fourier Transform (DFT)

A Review of Complex Arithmetic

National Quali cations

Further Results on Pair Sum Graphs

Solution to 1223 The Evil Warden.

DFT: Discrete Fourier Transform

Calculus & analytic geometry

UNIT 2: MATHEMATICAL ENVIRONMENT

Linear Algebra Existence of the determinant. Expansion according to a row.

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling

Review Exercises. 1. Evaluate using the definition of the definite integral as a Riemann Sum. Does the answer represent an area? 2

امتحانات الشهادة الثانوية العامة فرع: العلوم العامة

(Reference: sections in Silberberg 5 th ed.)

Restricted Factorial And A Remark On The Reduced Residue Classes

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges.

1973 AP Calculus BC: Section I

Introduction to Quantum Information Processing. Overview. A classical randomised algorithm. q 3,3 00 0,0. p 0,0. Lecture 10.

How many neutrino species?

ω (argument or phase)

LECTURE 13 Filling the bands. Occupancy of Available Energy Levels

SOLVED EXAMPLES. Ex.1 If f(x) = , then. is equal to- Ex.5. f(x) equals - (A) 2 (B) 1/2 (C) 0 (D) 1 (A) 1 (B) 2. (D) Does not exist = [2(1 h)+1]= 3

Introduction to Arithmetic Geometry Fall 2013 Lecture #20 11/14/2013

Statistics 3858 : Likelihood Ratio for Exponential Distribution

Independent Domination in Line Graphs

Chapter 3 Linear Equations of Higher Order (Page # 144)

Probability & Statistics,

Discrete Fourier Transform. Nuno Vasconcelos UCSD

Chapter Taylor Theorem Revisited

On the approximation of the constant of Napier

The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function. The Transfer Function

On a problem of J. de Graaf connected with algebras of unbounded operators de Bruijn, N.G.

WBJEEM MATHEMATICS. Q.No. μ β γ δ 56 C A C B

ln x = n e = 20 (nearest integer)

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Solid State Device Fundamentals

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei.

Folding of Hyperbolic Manifolds

THERMAL STATES IN THE k-generalized HYPERGEOMETRIC COHERENT STATES REPRESENTATION

MONTGOMERY COLLEGE Department of Mathematics Rockville Campus. 6x dx a. b. cos 2x dx ( ) 7. arctan x dx e. cos 2x dx. 2 cos3x dx

Lectures 9 IIR Systems: First Order System

Equation Sheet Please tear off this page and keep it with you

Response of LTI Systems to Complex Exponentials

ENGG 1203 Tutorial. Difference Equations. Find the Pole(s) Finding Equations and Poles

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia

INCOMPLETE KLOOSTERMAN SUMS AND MULTIPLICATIVE INVERSES IN SHORT INTERVALS. xy 1 (mod p), (x, y) I (j)

NET/JRF, GATE, IIT JAM, JEST, TIFR

Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform

International Journal of Advanced and Applied Sciences

An Introduction to Asymptotic Expansions

Problem Value Score Earned No/Wrong Rec -3 Total

Class #24 Monday, April 16, φ φ φ

Classification of DT signals

Law of large numbers

STIRLING'S 1 FORMULA AND ITS APPLICATION

10. Joint Moments and Joint Characteristic Functions

Asymptotic Behaviors for Critical Branching Processes with Immigration

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0

Background: We have discussed the PIB, HO, and the energy of the RR model. In this chapter, the H-atom, and atomic orbitals.

National Quali cations

Derivation of a Predictor of Combination #1 and the MSE for a Predictor of a Position in Two Stage Sampling with Response Error.

Ordinary Differential Equations

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation.

How many neutrons does this aluminium atom contain? A 13 B 14 C 27 D 40

SOME IDENTITIES FOR THE GENERALIZED POLY-GENOCCHI POLYNOMIALS WITH THE PARAMETERS A, B AND C

FORBIDDING RAINBOW-COLORED STARS

COLLECTION OF SUPPLEMENTARY PROBLEMS CALCULUS II

NEW VERSION OF SZEGED INDEX AND ITS COMPUTATION FOR SOME NANOTUBES

Time Dependent Solutions: Propagators and Representations

Discrete Mathematics and Probability Theory Fall 2014 Anant Sahai Homework 11. This homework is due November 17, 2014, at 12:00 noon.

BOUNDS FOR THE COMPONENTWISE DISTANCE TO THE NEAREST SINGULAR MATRIX

Chapter 4 - The Fourier Series

International Journal of Mathematical Archive-6(5), 2015, Available online through ISSN

3.4 Properties of the Stress Tensor

Ordinary Differential Equations

HILBERT SPACE GEOMETRY

Poisson Arrival Process

APPENDIX: STATISTICAL TOOLS

Sums, Approximations, and Asymptotics II

CDS 101: Lecture 5.1 Reachability and State Space Feedback

ANOVA- Analyisis of Variance

Poisson Arrival Process

Cross-Sections for p-adically Closed Fields

Transcription:

Chatr Fiv Mor Dimsios 51 Th Sac R W ar ow rard to mov o to sacs of dimsio gratr tha thr Ths sacs ar a straightforward gralizatio of our Euclida sac of thr dimsios Lt b a ositiv itgr Th -dimsioal Euclida sac R is simly th st of all ordrd -tuls of ral umbrs x = ( x 1, K, x ) Thus R 1 is simly th ral umbrs, R is th la, ad R 3 is Euclida thr-sac Ths ordrd -tuls ar calld oits, or vctors This dfiitio dos ot cotradict our rvious dfiitio of a vctor i cas =3 i that w idtifid ach vctor with a ordrd tril ( x1, x, x3 ) ad sok of th tril as big a vctor W ow dfi various arithmtic oratios o R i th obvious way If w hav vctors x = ( x 1, K, x ) ad y = ( y 1, y, K, y ) i R, th sum x + y is dfid by x + y = ( x + y, x + y, K 1 1, x + y ), ad multilicatio of th vctor x by a scalar a is dfid by ax = ( ax, ax, K 1, ax ) It is asy to vrify that a( x + y) = ax + ay ad ( a + b) x = ax + bx Agai w s that ths dfiitios ar tirly cosistt with what w hav do i dimsios 1,, ad 3-thr is othig to ular Cotiuig, w dfi th lgth, or orm of a vctor x i th obvious mar x = x 1 + x + K + x Th scalar roduct of x ad y is 51

x y = x y + x y + K + x y = x y 1 1 i i i = 1 It is agai asy to vrify th ic rortis: x = x x 0, ax = a x, x y = y x, x ( y + z) = x y + x z, ad ( ax) y = a( x y) Th gomtric laguag of th thr dimsioal sttig is rtaid i highr dimsios; thus w sak of th lgth of a -tul of umbrs I fact, w also sak of d( x, y) = x y as th distac btw x ad y W ca, of cours, o logr rly o our vast kowldg of Euclida gomtry i our rasoig about R wh > 3 Thus for 3, th fact that x + y x + y for ay vctors x ad y was a siml cosquc of th fact that th sum of th lgths of two sids of a triagl is at last as big as th lgth of th third sid This iquality rmais tru i highr dimsios, ad, i fact, is calld th triagl iquality, but rquirs a sstially algbraic roof Lt s s if w ca rov it Lt x = ( x 1, K, x ) ad y = ( y 1, y, K, y ) Th if a is a scalar, w hav Thus, ax + y = ( ax + y) ( ax + y) 0 ( ax + y) ( ax + y) = a x x + ax y + y y 0 This is a quadratic fuctio i a ad is vr gativ; it must thrfor b tru that 4( x y) 4( x x)( y y) 0, or x y x y This last iquality is th clbratd Cauchy-Schwarz-Buiakowsky iquality It is xactly th igrdit w d to rov th triagl iquality 5

x + y = ( x + y) ( x + y) = x x + x y + y y Alyig th C-S-B iquality, w hav x + y x + x y + y = ( x + y ), or x + y x + y Corrsodig to th coordiat vctors i, j, ad k,,, K, ar dfid i R by 1 M 1 3 = ( 10,, 00,, K, 0) = ( 0100,,,, K, 0) = ( 0010,,,, K, 0), = ( 000,,, K, 01, ) th coordiat vctors Thus ach vctor x = ( x 1, K, x ) may b writt i trms of ths coordiat vctors: x = x ii i= 1 Exrciss 1 Lt x ad y b two vctors i R Prov that x + y = x + y x y if ad oly if = 0 (Adotig mor gomtric laguag from thr sac, w say that x ad y ar rdicular or orthogoal if x y = 0) Lt x ad y b two vctors i R Prov a) x + y x y = 4 x y b) x + y + x y = [ x + y ] 53

3 Lt x ad y b two vctors i R Prov that x y x + y 4 Lt P R 4 b th st of all vctors x = ( x1, x, x3, x4 ) such that 3x + 5x x + x = 15 1 3 4 Fid vctors ad a such that P = { x R 4 : ( x a) = 0 } 5 Lt ad a b vctors i R, ad lt P = { x R : ( x a) = 0 a)fid a quatio i x, x, K, ad x such that x = ( x, x, K, x ) P if ad oly if 1 1 th coordiats of x satisfy th quatio b)dscrib th st P b i cas = 3 Dscrib it i cas = [Th st P is calld a hyrla through a] 5 Fuctios W ow cosidr fuctios F: R R Not that wh = = 1, w hav th usual grammar school calculus fuctios, ad wh = 1 ad = or 3, w hav th vctor valud fuctios of th rvious chatr Not also that xct for vry scial circumstacs, grahs of fuctios will ot lay a big rol i our udrstadig Th st of oits ( x, F ( x)) rsids i R + sic x R ad F ( x) R ; this is difficult to s ulss + 3 fuctio F: R W bgi with a vry scial kid of fuctios, th so-calld liar fuctios A R is said to b a liar fuctio if i) F ( x + y) = F( x) + F( y) for all x, y R, ad ii)f ( ax) = af ( x) for all scalars a ad x R Examl Lt = = 1, ad dfi F by F ( x) = 3 x Th F ( x + y) = 3( x + y) = 3x + 3y = F ( x) + F ( y) ad F ( ax) = 3( ax) = a3 x = af ( x) This F is a liar fuctio 54

Aothr Examl Lt F: R R 3 b dfid by F( t) = ti + tj 7tk = ( t, t, 7 t ) Th F( t + s) = ( t + s) i + ( t + s) j 7( t + s) k = [ ti + tj 7tk] + [ si + sj 7sk] = F ( t) + F( s) Also, F( at ) = ati + atj 7at k = a[ ti + tj 7tk] = af( t ) W s yt aothr liar fuctio O Mor Examl Lt F: R R b dfid by 3 4 F (( x, x, x )) = ( x x + 3x, x + 4x 5x, x + x + x, x + x ) 1 3 1 3 1 3 1 3 1 3 It is asy to vrify that F is idd a liar fuctio A traslatio is a fuctio T:R R such that T( x) = a + x, whr a is a fixd vctor i R A fuctio that is th comositio of a liar fuctio followd by a traslatio is calld a affi fuctio A affi fuctio F thus has th form F ( x) = a + L( x), whr L is a liar fuctio Examl Lt F: R R 3 b dfid by F( t) = ( + t, 4t 3, t) Th F is affi Lt a = (, 4, 0 ) ad L( t) = ( t, 4 t, t ) Clarly F ( t) = a + L( t) Exrciss 6 Which of th followig fuctios ar liar? Exlai your aswrs a) f ( x) = 7 x b) g( x) = x 5 c) F ( x1, x) = ( x1 + x, x1 x, 3x1, 5x1 x, x 1 ) d) G( x, x, x ) = x x + x ) F ( t) = ( t, t, 0, t ) 1 3 1 3 f) h( x1, x, x3, x4) = ( 1, 0, 0) g) f ( x) = si x 55

7 a)dscrib th grah of a liar fuctio from R to R b)dscrib th grah of a affi fuctio from R to R 56