9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4

Size: px
Start display at page:

Download "9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4"

Transcription

1 1 3.6 I. Combiig Fuctios A. From Equatios Example: Let f(x) = 9 x ad g(x) = 4 f x. Fid (x) g ad its domai. 4 Example: Let f(x) = ad g(x) = x x 4. Fid (f-g)(x) B. From Graphs: Graphical Additio. Example: Graph (f+g)(x) usig the graph below.

2 II. Compositio of Fuctios ( f g)( x) = f(g(x)) Example: Let f(x) = 3x-5 ad g(x) = a) Fid f(g(x)). b) Evaluate ( f g)( ). 3 x. c) State the domai of ( f g)( x). 1 Example: Let f(x) =, g(x) = 3 x, ad h(x) = x. x Fid ( f g h)( x). Example. Express H ( x) 1 x i the form ( f g)( x).

3 3 Example: A stoe is dropped i a lake, creatig a circular ripple that travels outward at 60 cetimeters per secod. a) Fid a fuctio g that models the radius as a fuctio of time. b) Fid a fuctio f that models the area of the circle as a fuctio of time. c) Fid f g. What does this fuctio represet?

4 4 Example: A prit shop makes bumper stickers for electio campaigs. If x stickers are ordered (x < 10,000), the the price per bumper sticker is $( x ), ad the total cost of producig the order is $( 0.095x x ). a) Reveue = (Price per item) X (Number of items sold) Express R(x), the reveue from a order of x stickers, as a product of two fuctios of x. b) Profit = Reveue Cost. Express the profit P(x) o a order of x stickers as a differece of two fuctios of x.

5 5 3.7 I. Combiig Fuctios Fuctio f Iverse fuctio f -1 Domai Domai Rage Rage f(x) = y f 1 ( y) x poit (a,b) poit (b, a) A fuctio must be oe-to-oe to be ivertable. Oe-to-oe: If f ( x1 ) f ( x), the x 1 x. Pass HLT Every icreasig fuctio is 1-1 Every decreasig fuctio is 1-1 Example: Sketch f ( x) x x 6 usig your calculator to determie whether f(x) is 1-1.

6 6 Example: G(x) = (x 1) is graphed below. a) Restrict the domai so that the fuctio is 1-1. b) Graph the iverse fuctio. Example: Sketch f(x) = x 1. Use this to sketch f 1 ( x ). 3 Example: Determie whether h(x) = x 8 is 1-1.

7 7 Steps to fid a iverse fuctio. 1. Replace f(x) by y.. Switch x ad y. 3. Solve for y. 4. Replace y by f 1 ( x ) Example: Fid the iverse of f ( x) 4 x, x 0.

8 8 To verify that two fuctios are iverses, show f(g(x)) = g(f(x)) = x, for all x. Example: Verify that x 5 f ( x) ad 3x 4 5 4x g( x) are iverses. 1 3x Example: If f(5) = 18, f 1 (18) Example: If f 1 (4), f() =

9 9 4.1 Name Equatio Vertex at Quadratic Form f(x) = ax bx c b, f a b a Stadard form f ( x) a( x h) k (h,k) Example: Fid the vertex of s 1.s 16. To Graph 1. Express the equatio i stadard form.. Fid the vertex. 3. Fid the x-itercept(s). 4. Fid the y-itercept. 5. If a > 0, the graph is CCU If a < 0, the graph is CCD 6. Coect the poits.

10 10 Example: Graph f ( x) x 4x 4. State the domai ad rage.

11 11 Example: Fid a fuctio whose graph is a parabola with a vertex at (3, 4) ad passes through the poit (1, -8). Example: Fid the maximum or miimum of f ( x) 1 x x. a) O the graphig calculator. b) By had. c) Compare the results.

12 1 Example: A ball is throw across a playig field from a height of 5 feet above the groud at a 0 45 agle to the horizotal at a speed of 0 feet per secod. The ball s path is modeled by: y 3 x 0 x 5, x = horizotal distace traveled. a) Fid the maximum height of the ball. b) Fid how log it takes the ball to hit the groud.

13 13 Example: A corral has,400 feet of fecig to fece i a rectagular horse coral. a) Fid a fuctio that models the area i terms of width. b) Fid the dimesio of the rectagle that maximizes the area of the corral.

14 14 4. ad 4.3 Form I: Polyomial of degree P( x) a x... a 0 A. Write i descedig powers Polyomials are cotiuous (o breaks or holes) Smooth curve (o cusps) Number of max/mis = degree 1 Degree = umber max/mis + 1 At most At least Ex: Graph each polyomial ad the determie the umber of maximums ad miimums a) Y 1.x 3.75x 7x 15x 18x b) Y (x ) 3 B. Ed tail behavior. Eve Degree Odd Degree a 0 a 0

15 15 C. To graph a polyomial of degree : 1. Graph x. Apply trasformatios 3. Label the ed tail behavior. Example: Graph P ( x) x 4 Type II: Factored Form 1 P ( x) a( x z ) ( x z )...( x z k 1 k ) To graph: 1. Degree = Sum(expoets) ; apply ed tail behavior. Fid the zeros of the polyomial 3. If r is a zero of eve multiplicity, the graph touches the x-axis at root, r If r is a zero of odd multiplicity, the graph crosses the x-axis at root, r. 4. Fid the y-itercept 5. Graph Example: Graph each polyomial. 3 a) P ( x) ( x 1) ( x ). b) P ( x) (x 1)( x 1)( x 3) 4 3 c) P ( x) x 3x x

16 16 Itermediate Value Theorem. If P is a polyomial ad P(a) ad P(b) have opposite sigs, the there exits at least oe value c betwee a ad b such that P(c) = 0. Example: A ope box is to be costructed from a 0cm by 40cm piece of cardboard by cuttig squares of equal legth x from each corer. a) Express the volume (V) of the box as a fuctio of x. b) What is the domai, kowig that legth ad volume are positive? c) Draw a graph of V(x). Estimate the maximum value.

17 17 II. Log Divisio. Divisio Algorithm Divided = Divisor * Quotiet + Remaider P(x) = D(x)*Q(x) + R(x) 4 3 Example: P ( x) x x 4x D ( x) x 3

18 18 III. Sythetic Divisio Use whe divisor is of the form D(x) = x c or D(x) = x (-c) = x + c. Steps: 1. c coefficiets of P(x) writte i descedig powers -c coefficiets of P(x) writte i descedig powers 3 Ex) P ( x) x 4x 6x 1 D ( x) x 1. Write i the coefficiets. Use zero as a place holder for missig terms. 3. Carry dow the first coefficiet below the divisio symbol. 4. Multiply the divisor by the first coefficiet. 5. Write the product above the lie ad i the first colum to the right. 6. Add the ext coefficiet to the product. Record the sum below the lie. 7. Repeat the multiply ad add steps util the last row is complete. A. Factor Theorem: P(x) = (x c) Q(x) + R(x), If R(x) = 0, the the polyomial is factored ad x = c is a zero of the polyomial. Example: x 5 x x

19 19 B. Remaider Theorem Evaluate P(x) at x = c meas Example: Evaluate P(c) at c = -3 whe P ( x) x 7x 40x 7x 10x 11 C. Factor Theorem (Revisited) x c is a factor of P(x) if ad oly if P(c) = 0. Steps: 1. Check to see if P(c) = 0. If so, the R(x) = 0 i sythetic divisio ad x c is a factor of P(x). Why?. Perform sythetic divisio. 3. P(x) = (x c)q(x) Factor Q(x)

20 0 Example: Show 1 3 x is a factor of P ( x) x 7x 6x 5. Example Show c = - ad c are zeros of 3x x 1x 11x 6. The fid all other zeros. 3

21 1 To fid a polyomial with specific zeros: Let z 1 = root 1, z = root,, z = root. The 1 P ( x) a( x z1 ) ( x z)...( x zk ) k Example: Write a polyomial of degree 5 with zeros at -, -1, 0, 1, ad.

22 4.4 Goal: Factor P( x) a x... a0 Use the factored form to fid solutios to P(x) = P( x) a x... a0 = 0 This will result i the zeros/x-itercepts/factors of the polyomial. Cocept: The complex zeros ca be: 1. Real ad Ratioal 0 = 4x 9. Real ad Irratioal 0 = x 3. Imagiary 0 = x 4 Steps to fid the zeros: 1. Total umber of complex zeros = Degree of the polyomial.. Descartes Theorem Example: Fid the umber of real zeros for each polyomial below. 4 3 a) P ( x) x x x x 1 3 b) P ( x) 4x 8x 11x 15

23 3 3. Ratioal Zeros Theorem If P( x) a x... a0 has degree 1ad iteger coefficiets p factors of a 0 p The for x, x is a potetial zero of P(x). q factors of a q 3 Example: List the possible zeros of P ( x) 4x 8x 11x Choose which of the potetial zeros are ideed zeros. Example: Choose the zeros for P ( x) 3 4x 8x 11x 15. Method I: Graph. Method II: Sythetic Divisio Method III: Use the table o your calculator.

24 4 5. Use sythetic divisio to write the polyomial i fully factored form. 3 Example: Factor P ( x) 4x 8x 11x 15.

25 5 Useful Commets: 1. P(x) ca ot have more real zeros tha its degree.. The total umber of complex zeros = degree of the polyomial. 3. A polyomial fuctio with real coefficiets ad odd degree has at least oe zero. 4. Upper Boud/Lower Boud Theorem Divide P(x) by p q usig sythetic divisio. Example: Show that a = -3 ad b = 5 are upper ad lower bouds for P( x) 4 x 3 x 9x x 8.

26 6 4.5 Complex Numbers z = a + bi If b = 0 : If a = 0: z = 3 + 4i Complex cojugate of z = a + bi is z =a bi. Ex: z = 3 + 4i For 1 k P ( x) a( x z1 ) ( x z)...( x zk ) z i are the complex zeros, i = 1 k (degree of polyomial) (x - z i ) are the factors I. Fudametal Theorem of Algebra *If a polyomial P( x) a x... a0 has positive degree ( 1), a 0, ad complex coefficiets (a + bi) * The P(x) has at least oe complex zero. 4 Ex: x 4 II. This theorem allows every polyomial of positive degree to be expressed as a product of polyomials of degree oe. Complete Factorizatio Theorem For all * P( x) a x... a0 *, there exist complex umbers 1 k a, z 1, z k such that P ( x) a( x z1 ) ( x z)...( x zk ). Commet: k = = degree of the polyomial.

27 7 III. Zero s Theorem Every polyomial * P( x) a x... a0 * has exactly complex zeros. Zeros ca be: o Pure Real o Pure Imagiary o A combiatio of real ad imagiary Zeros that occur with multiplicity m are couted m times. If m = odd, the graph flattes ad crosses the x-axis. If m = eve, the graph bouces off the x-axis. Cojugate Zeros Theorem If z = a + bi is a complex zero of P(x), the z =a bi is also a complex zero. Ex: P(x) is of degree three ad has oe imagiary zero. What do you kow about the other zeros of this polyomial? Example: Fid all solutios to ix x i 0. Example: Fid a polyomial with iteger coefficiets of degree three with zeros at -3 ad 1 + i. Example: Factor each zero. 5 3 P( x) x 6x 9x ad the fid all complex zeros. State the multiplicity of

28 8 6 Example: Factor P ( x) x 64 a) ito liear ad irreducible quadratic factors with real coefficiets. b) Ito liear factors with complex coefficiets. 4 3 Example: Fid all zeros of P ( x) 4x x x 3x 1.

29 9 I. Notatio ad defiitios Vertical Asymptotes x c meas x approaches c from the left. Ex) x c meas x approaches c from the right. Ex) Defiitio: If, as x approaches some umber c, the values y approach ifiity, the the lie x = c is a vertical asymptote. (Rus parallel to the y-axis).. Horizotal Asymptotes x meas x icreases without boud. x meas x decreases without boud. Defitio: If, as x approaches positive or egative ifiity, the values y approach a fixed umber L, the the lie y = L is a horizotal asymptote. (Rus parallel to the x-axis).

30 30 3. Oblique Asymptotes If the asymptote is ot parallel to the x-axis or the y-axis, the the asymptote is called oblique. x, y mx b x, y mx b For this graph: x 0, y x 0, y Notice that if m = 0, the f(x) = mx+b becomes f(x) = b. So the slat asymptote becomes a horizotal asymptote.

31 31 II. Ratioal Fuctios r ( x) P( x) Q( x) ax b x d d a b 0 0 mx b R( x) Q( x) ( x ( x z1)( x z )( x 1 z)...( x z )...( x z) z ) d A. Vertical Asymptote: Zeros of the deomiator. If zi is a zero of the deomiator of r(x) i lowest terms, the x = z i is a vertical asymptote. Example: 5 x has a vertical asymptote at x =. B. Horizotal Asymptote: For r( x) P( x) Q( x) a x b x d d a b Proper Form ( < d) y = 0 is the horizotal asymptote (the x-axis). Example: 5 x has a horiztoal asymptote at y = 0.. Improper Form. Divide P( x) R( x) r ( x) mx b. Q( x) Q( x) Case I: If = d, the f(x) = 0x+b = b, ad y a b d is the horizotal asymptote. Example: x x 4 ( x). x x S CaseII: If > d, the f(x) = mx+b ad mx+b is a oblique asymptote. Example: 3 x x 3x 4

32 3 Commet: A graph ca cross a horizotal asymptote. (Does ot happe ear ifiity). C. To graph 1. Factor. Fid ay horizotal or oblique asymptotes (compare degrees) 3. Fid the x-itercept (zeros of the umerator). 4. Fid the vertical asymptotes (zeros of the deomiator) 5. Graph WARNING: If there is a commo factor i the umerator ad deomiator, the this poit forms a hole o the graph. Ex: r ( x) (x (x 5)(4x 5)( x 3) 3)

33 33 Some Geeral Shapes of Ratioal Fuctios

34 34 Example: Graph the equatio below. a) 3 x r ( x) x x 1

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0 Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity

More information

Math Section 2.2 Polynomial Functions

Math Section 2.2 Polynomial Functions Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably

More information

The Graphs of Polynomial Functions

The Graphs of Polynomial Functions Sectio 4.3 The Graphs of Polyomial Fuctios Objective 1: Uderstadig the Defiitio of a Polyomial Fuctio Defiitio Polyomial Fuctio 1 2 The fuctio ax a 1x a 2x a1x a0 is a polyomial fuctio of degree where

More information

2) Give an example of a polynomial function of degree 4 with leading coefficient of -6

2) Give an example of a polynomial function of degree 4 with leading coefficient of -6 Math 165 Read ahead some cocepts from sectios 4.1 Read the book or the power poit presetatios for this sectio to complete pages 1 ad 2 Please, do ot complete the other pages of the hadout If you wat to

More information

Math 10C Long Range Plans

Math 10C Long Range Plans Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will

More information

Arithmetic Sequences

Arithmetic Sequences . Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered

More information

EVALUATION OF TRIGONOMETRIC FUNCTIONS

EVALUATION OF TRIGONOMETRIC FUNCTIONS EVALUATION OF TRIGONOMETRIC FUNCTIONS Whe first exposed to trigoometric fuctios i high school studets are expected to memorize the values of the trigoometric fuctios of sie cosie taget for the special

More information

South Slave Divisional Education Council. Math 10C

South Slave Divisional Education Council. Math 10C South Slave Divisioal Educatio Coucil Math 10C Curriculum Package February 2012 12 Strad: Measuremet Geeral Outcome: Develop spatial sese ad proportioal reasoig It is expected that studets will: 1. Solve

More information

Intro to Scientific Computing: Solutions

Intro to Scientific Computing: Solutions Itro to Scietific Computig: Solutios Dr. David M. Goulet. How may steps does it take to separate 3 objects ito groups of 4? We start with 5 objects ad apply 3 steps of the algorithm to reduce the pile

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

Parabolic Path to a Best Best-Fit Line:

Parabolic Path to a Best Best-Fit Line: Studet Activity : Fidig the Least Squares Regressio Lie By Explorig the Relatioship betwee Slope ad Residuals Objective: How does oe determie a best best-fit lie for a set of data? Eyeballig it may be

More information

Name Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #2

Name Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #2 Name Date Hr. ALGEBRA - SPRING FINAL MULTIPLE CHOICE REVIEW # 5. Which measure of ceter is most appropriate for the followig data set? {7, 7, 75, 77,, 9, 9, 90} Mea Media Stadard Deviatio Rage 5. The umber

More information

Alpha Individual Solutions MAΘ National Convention 2013

Alpha Individual Solutions MAΘ National Convention 2013 Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5

More information

Numerical Methods Lecture 6 - Curve Fitting Techniques

Numerical Methods Lecture 6 - Curve Fitting Techniques Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio

More information

Area As A Limit & Sigma Notation

Area As A Limit & Sigma Notation Area As A Limit & Sigma Notatio SUGGESTED REFERENCE MATERIAL: As you work through the problems listed below, you should referece Chapter 5.4 of the recommeded textbook (or the equivalet chapter i your

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

PLEASURE TEST SERIES (XI) - 04 By O.P. Gupta (For stuffs on Math, click at theopgupta.com)

PLEASURE TEST SERIES (XI) - 04 By O.P. Gupta (For stuffs on Math, click at theopgupta.com) wwwtheopguptacom wwwimathematiciacom For all the Math-Gya Buy books by OP Gupta A Compilatio By : OP Gupta (WhatsApp @ +9-9650 350 0) For more stuffs o Maths, please visit : wwwtheopguptacom Time Allowed

More information

Module 8-7: Pascal s Triangle and the Binomial Theorem

Module 8-7: Pascal s Triangle and the Binomial Theorem Module 8-7: Pascal s Triagle ad the Biomial Theorem Gregory V. Bard April 5, 017 A Note about Notatio Just to recall, all of the followig mea the same thig: ( 7 7C 4 C4 7 7C4 5 4 ad they are (all proouced

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb

( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb Chapter 3 Descriptive Measures Measures of Ceter (Cetral Tedecy) These measures will tell us where is the ceter of our data or where most typical value of a data set lies Mode the value that occurs most

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

Math 3201 Notes Chapter 4: Rational Expressions & Equations

Math 3201 Notes Chapter 4: Rational Expressions & Equations Learig Goals: See p. tet.. Equivalet Ratioal Epressios ( classes) Read Goal p. 6 tet. Math 0 Notes Chapter : Ratioal Epressios & Equatios. Defie ad give a eample of a ratioal epressio. p. 6. Defie o-permissible

More information

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by

More information

WebAssign Lesson 6-1b Geometric Series (Homework)

WebAssign Lesson 6-1b Geometric Series (Homework) WebAssig Lesso 6-b Geometric Series (Homework) Curret Score : / 49 Due : Wedesday, July 30 204 :0 AM MDT Jaimos Skriletz Math 75, sectio 3, Summer 2 204 Istructor: Jaimos Skriletz. /2 poitsrogac alcet2

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

Civil Engineering Computation

Civil Engineering Computation Civil Egieerig Computatio Fidig Roots of No-Liear Equatios March 14, 1945 World War II The R.A.F. first operatioal use of the Grad Slam bomb, Bielefeld, Germay. Cotets 2 Root basics Excel solver Newto-Raphso

More information

Name Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #1

Name Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #1 Name Date Hr. ALGEBRA - SPRING FINAL MULTIPLE CHOICE REVIEW #. The high temperatures for Phoeix i October of 009 are listed below. Which measure of ceter will provide the most accurate estimatio of the

More information

CS Polygon Scan Conversion. Slide 1

CS Polygon Scan Conversion. Slide 1 CS 112 - Polygo Sca Coversio Slide 1 Polygo Classificatio Covex All iterior agles are less tha 180 degrees Cocave Iterior agles ca be greater tha 180 degrees Degeerate polygos If all vertices are colliear

More information

Test 4 Review. dy du 9 5. sin5 zdz. dt. 5 Ê. x 2 È 1, 3. 2cos( x) dx is less than using Simpson's. ,1 t 5 t 2. ft () t2 4.

Test 4 Review. dy du 9 5. sin5 zdz. dt. 5 Ê. x 2 È 1, 3. 2cos( x) dx is less than using Simpson's. ,1 t 5 t 2. ft () t2 4. Name: Class: Date: ID: A Test Review Short Aswer. Fid the geeral solutio of the differetial equatio below ad check the result by differetiatio. dy du 9 u. Use the error formula to estimate the error i

More information

Consider the following population data for the state of California. Year Population

Consider the following population data for the state of California. Year Population Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB

More information

Cubic Polynomial Curves with a Shape Parameter

Cubic Polynomial Curves with a Shape Parameter roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad

More information

The Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana

The Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana The Closest Lie to a Data Set i the Plae David Gurey Southeaster Louisiaa Uiversity Hammod, Louisiaa ABSTRACT This paper looks at three differet measures of distace betwee a lie ad a data set i the plae:

More information

UNIT 3 EXPRESSIONS AND EQUATIONS Lesson 3: Creating Quadratic Equations in Two or More Variables

UNIT 3 EXPRESSIONS AND EQUATIONS Lesson 3: Creating Quadratic Equations in Two or More Variables Guided Practice Example 1 Find the y-intercept and vertex of the function f(x) = 2x 2 + x + 3. Determine whether the vertex is a minimum or maximum point on the graph. 1. Determine the y-intercept. The

More information

Pattern Recognition Systems Lab 1 Least Mean Squares

Pattern Recognition Systems Lab 1 Least Mean Squares Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig

More information

EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS

EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS I this uit of the course we ivestigate fittig a straight lie to measured (x, y) data pairs. The equatio we wat to fit

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

SAMPLE VERSUS POPULATION. Population - consists of all possible measurements that can be made on a particular item or procedure.

SAMPLE VERSUS POPULATION. Population - consists of all possible measurements that can be made on a particular item or procedure. SAMPLE VERSUS POPULATION Populatio - cosists of all possible measuremets that ca be made o a particular item or procedure. Ofte a populatio has a ifiite umber of data elemets Geerally expese to determie

More information

Algorithms Chapter 3 Growth of Functions

Algorithms Chapter 3 Growth of Functions Algorithms Chapter 3 Growth of Fuctios Istructor: Chig Chi Li 林清池助理教授 chigchi.li@gmail.com Departmet of Computer Sciece ad Egieerig Natioal Taiwa Ocea Uiversity Outlie Asymptotic otatio Stadard otatios

More information

Optimal Mapped Mesh on the Circle

Optimal Mapped Mesh on the Circle Koferece ANSYS 009 Optimal Mapped Mesh o the Circle doc. Ig. Jaroslav Štigler, Ph.D. Bro Uiversity of Techology, aculty of Mechaical gieerig, ergy Istitut, Abstract: This paper brigs out some ideas ad

More information

Our starting point is the following sketch of part of one of these polygons having n vertexes and side-length s-

Our starting point is the following sketch of part of one of these polygons having n vertexes and side-length s- PROPERTIES OF REGULAR POLYGONS The simplest D close figures which ca be costructe by the cocateatio of equal legth straight lies are the regular polygos icluig the equilateral triagle, the petago, a the

More information

Mathematical Stat I: solutions of homework 1

Mathematical Stat I: solutions of homework 1 Mathematical Stat I: solutios of homework Name: Studet Id N:. Suppose we tur over cards simultaeously from two well shuffled decks of ordiary playig cards. We say we obtai a exact match o a particular

More information

Learning to Shoot a Goal Lecture 8: Learning Models and Skills

Learning to Shoot a Goal Lecture 8: Learning Models and Skills Learig to Shoot a Goal Lecture 8: Learig Models ad Skills How do we acquire skill at shootig goals? CS 344R/393R: Robotics Bejami Kuipers Learig to Shoot a Goal The robot eeds to shoot the ball i the goal.

More information

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

CIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8)

CIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8) CIS 11 Data Structures ad Algorithms with Java Fall 017 Big-Oh Notatio Tuesday, September 5 (Make-up Friday, September 8) Learig Goals Review Big-Oh ad lear big/small omega/theta otatios Practice solvig

More information

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig

More information

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 )

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 ) EE26: Digital Desig, Sprig 28 3/6/8 EE 26: Itroductio to Digital Desig Combiatioal Datapath Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Combiatioal Logic Blocks Multiplexer Ecoders/Decoders

More information

1. The lines intersect. There is one solution, the point where they intersect. The system is called a consistent system.

1. The lines intersect. There is one solution, the point where they intersect. The system is called a consistent system. Commo Core Math 3 Notes Uit Day Systems I. Systems of Liear Equatios A system of two liear equatios i two variables is two equatios cosidered together. To solve a system is to fid all the ordered pairs

More information

1. (a) Write a C program to display the texts Hello, World! on the screen. (2 points)

1. (a) Write a C program to display the texts Hello, World! on the screen. (2 points) 1. (a) Write a C program to display the texts Hello, World! o the scree. (2 poits) Solutio 1: pritf("hello, World!\"); Solutio 2: void mai() { pritf("hello, World!\"); (b) Write a C program to output a

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein 068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig

More information

On (K t e)-saturated Graphs

On (K t e)-saturated Graphs Noame mauscript No. (will be iserted by the editor O (K t e-saturated Graphs Jessica Fuller Roald J. Gould the date of receipt ad acceptace should be iserted later Abstract Give a graph H, we say a graph

More information

CONTINUI TY. JEE-Mathematics. Illustration 1 : Solution : Illustration 2 : 1. CONTINUOUS FUNCTIONS :

CONTINUI TY. JEE-Mathematics. Illustration 1 : Solution : Illustration 2 : 1. CONTINUOUS FUNCTIONS : J-Mathematics. CONTINUOUS FUNCTIONS : CONTINUI TY A fuctio for which a small chage i the idepedet variable causes oly a small chage ad ot a sudde jump i the depedet variable are called cotiuous fuctios.

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

OCR Statistics 1. Working with data. Section 3: Measures of spread

OCR Statistics 1. Working with data. Section 3: Measures of spread Notes ad Eamples OCR Statistics 1 Workig with data Sectio 3: Measures of spread Just as there are several differet measures of cetral tedec (averages), there are a variet of statistical measures of spread.

More information

Assignment 5; Due Friday, February 10

Assignment 5; Due Friday, February 10 Assigmet 5; Due Friday, February 10 17.9b The set X is just two circles joied at a poit, ad the set X is a grid i the plae, without the iteriors of the small squares. The picture below shows that the iteriors

More information

Counting Regions in the Plane and More 1

Counting Regions in the Plane and More 1 Coutig Regios i the Plae ad More 1 by Zvezdelia Stakova Berkeley Math Circle Itermediate I Group September 016 1. Overarchig Problem Problem 1 Regios i a Circle. The vertices of a polygos are arraged o

More information

Chapter Algebra 1 Copyright Big Ideas Learning, LLC Worked-Out Solutions. Maintaining Mathematical Proficiency.

Chapter Algebra 1 Copyright Big Ideas Learning, LLC Worked-Out Solutions. Maintaining Mathematical Proficiency. Chapter Maintaining Mathematical Proficienc. The function q is of the form = f(x h), where h =. So, the graph of q is a horizontal translation units left of the. The function h is of the form = af(x),

More information

Hash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015.

Hash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015. Presetatio for use with the textbook Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Hash Tables xkcd. http://xkcd.com/221/. Radom Number. Used with permissio uder Creative

More information

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed.

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed. MASSACHUSETTS INSTITUTE of TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.161 Moder Optics Project Laboratory 6.637 Optical Sigals, Devices & Systems Problem Set No. 1 Geometric optics

More information

( )! 1! 3 = x + 1. ( ) =! x + 2

( )! 1! 3 = x + 1. ( ) =! x + 2 7.5 Graphing Parabolas 1. First complete the square: y = x 2 + 2x! 3 = x 2 + 2x + 1 ( )! 1! 3 = x + 1 ( ) 2! 4 The x-intercepts are 3,1 and the vertex is ( 1, 4). Graphing the parabola: 3. First complete

More information

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le Fudametals of Media Processig Shi'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dih Le Today's topics Noparametric Methods Parze Widow k-nearest Neighbor Estimatio Clusterig Techiques k-meas Agglomerative Hierarchical

More information

Thompson s Group F (p + 1) is not Minimally Almost Convex

Thompson s Group F (p + 1) is not Minimally Almost Convex Thompso s Group F (p + ) is ot Miimally Almost Covex Claire Wladis Thompso s Group F (p + ). A Descriptio of F (p + ) Thompso s group F (p + ) ca be defied as the group of piecewiseliear orietatio-preservig

More information

FURTHER INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

FURTHER INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS) Mathematics Revisio Guides More Trigoometric ad Log Itegrals Page of 7 MK HOME TUITION Mathematics Revisio Guides Level: AS / A Level AQA : C Edexcel: C OCR: C OCR MEI: C FURTHER INTEGRATION TECHNIQUES

More information

3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS

3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS 3.1 INTRODUCTION TO THE FAMILY OF QUADRATIC FUNCTIONS Finding the Zeros of a Quadratic Function Examples 1 and and more Find the zeros of f(x) = x x 6. Solution by Factoring f(x) = x x 6 = (x 3)(x + )

More information

Computational Geometry

Computational Geometry Computatioal Geometry Chapter 4 Liear programmig Duality Smallest eclosig disk O the Ageda Liear Programmig Slides courtesy of Craig Gotsma 4. 4. Liear Programmig - Example Defie: (amout amout cosumed

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

Wavelet Transform. CSE 490 G Introduction to Data Compression Winter Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual)

Wavelet Transform. CSE 490 G Introduction to Data Compression Winter Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual) Wavelet Trasform CSE 49 G Itroductio to Data Compressio Witer 6 Wavelet Trasform Codig PACW Wavelet Trasform A family of atios that filters the data ito low resolutio data plus detail data high pass filter

More information

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets

More information

Final Exam Review Algebra Semester 1

Final Exam Review Algebra Semester 1 Final Exam Review Algebra 015-016 Semester 1 Name: Module 1 Find the inverse of each function. 1. f x 10 4x. g x 15x 10 Use compositions to check if the two functions are inverses. 3. s x 7 x and t(x)

More information

Convex hull ( 凸殻 ) property

Convex hull ( 凸殻 ) property Covex hull ( 凸殻 ) property The covex hull of a set of poits S i dimesios is the itersectio of all covex sets cotaiig S. For N poits P,..., P N, the covex hull C is the give by the expressio The covex hull

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

Descriptive Statistics Summary Lists

Descriptive Statistics Summary Lists Chapter 209 Descriptive Statistics Summary Lists Itroductio This procedure is used to summarize cotiuous data. Large volumes of such data may be easily summarized i statistical lists of meas, couts, stadard

More information

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract O Ifiite Groups that are Isomorphic to its Proper Ifiite Subgroup Jaymar Talledo Baliho Abstract Two groups are isomorphic if there exists a isomorphism betwee them Lagrage Theorem states that the order

More information

Administrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today

Administrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today Admiistrative Fial project No office hours today UNSUPERVISED LEARNING David Kauchak CS 451 Fall 2013 Supervised learig Usupervised learig label label 1 label 3 model/ predictor label 4 label 5 Supervised

More information

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure

More information

6.081 March Lecture 6

6.081 March Lecture 6 Review exampe : Aut Zeda s ba accout growig from some iitia baace y [ ] y[.05y[ y[ ].05y[ Natura frequecy is.05 soutio grows as.05 Review exampe : Your robot from ab d d right d eft ] δtθ[ θ[ ] θ[ δtk

More information

The golden search method: Question 1

The golden search method: Question 1 1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())

More information

A Resource for Free-standing Mathematics Qualifications

A Resource for Free-standing Mathematics Qualifications Ope.ls The first sheet is show elow. It is set up to show graphs with equatios of the form = m + c At preset the values of m ad c are oth zero. You ca chage these values usig the scroll ars. Leave the

More information

Assignment Assignment for Lesson 9.1

Assignment Assignment for Lesson 9.1 Assignment Assignment for Lesson.1 Name Date Shifting Away Vertical and Horizontal Translations 1. Graph each cubic function on the grid. a. y x 3 b. y x 3 3 c. y x 3 3 2. Graph each square root function

More information

MATHEMATICAL METHODS OF ANALYSIS AND EXPERIMENTAL DATA PROCESSING (Or Methods of Curve Fitting)

MATHEMATICAL METHODS OF ANALYSIS AND EXPERIMENTAL DATA PROCESSING (Or Methods of Curve Fitting) MATHEMATICAL METHODS OF ANALYSIS AND EXPERIMENTAL DATA PROCESSING (Or Methods of Curve Fittig) I this chapter, we will eamie some methods of aalysis ad data processig; data obtaied as a result of a give

More information

Characterizing graphs of maximum principal ratio

Characterizing graphs of maximum principal ratio Characterizig graphs of maximum pricipal ratio Michael Tait ad Josh Tobi November 9, 05 Abstract The pricipal ratio of a coected graph, deoted γg, is the ratio of the maximum ad miimum etries of its first

More information

Apparent Depth. B' l'

Apparent Depth. B' l' REFRACTION by PLANE SURFACES Apparet Depth Suppose we have a object B i a medium of idex which is viewed from a medium of idex '. If '

More information

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence?

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence? 6. Recursive Procedures I Sectio 6.1, you used fuctio otatio to write a explicit formula to determie the value of ay term i a Sometimes it is easier to calculate oe term i a sequece usig the previous terms.

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

Remember to SHOW ALL STEPS. You must be able to solve analytically. Answers are shown after each problem under A, B, C, or D.

Remember to SHOW ALL STEPS. You must be able to solve analytically. Answers are shown after each problem under A, B, C, or D. Math 165 - Review Chapters 3 and 4 Name Remember to SHOW ALL STEPS. You must be able to solve analytically. Answers are shown after each problem under A, B, C, or D. Find the quadratic function satisfying

More information

EE123 Digital Signal Processing

EE123 Digital Signal Processing Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add

More information

Texture Mapping. Jian Huang. This set of slides references the ones used at Ohio State for instruction.

Texture Mapping. Jian Huang. This set of slides references the ones used at Ohio State for instruction. Texture Mappig Jia Huag This set of slides refereces the oes used at Ohio State for istructio. Ca you do this What Dreams May Come Texture Mappig Of course, oe ca model the exact micro-geometry + material

More information

University of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures

University of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures Uiversity of Waterloo Departmet of Electrical ad Computer Egieerig ECE 250 Algorithms ad Data Structures Midterm Examiatio ( pages) Istructor: Douglas Harder February 7, 2004 7:30-9:00 Name (last, first)

More information

3D Model Retrieval Method Based on Sample Prediction

3D Model Retrieval Method Based on Sample Prediction 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer

More information

Fast Fourier Transform (FFT) Algorithms

Fast Fourier Transform (FFT) Algorithms Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform

More information

1. Sketch a concave polygon and explain why it is both concave and a polygon. A polygon is a simple closed curve that is the union of line segments.

1. Sketch a concave polygon and explain why it is both concave and a polygon. A polygon is a simple closed curve that is the union of line segments. SOLUTIONS MATH / Fial Review Questios, F5. Sketch a cocave polygo ad explai why it is both cocave ad a polygo. A polygo is a simple closed curve that is the uio of lie segmets. A polygo is cocave if it

More information

An (or ) is a sequence in which each term after the first differs from the preceding term by a fixed constant, called the.

An (or ) is a sequence in which each term after the first differs from the preceding term by a fixed constant, called the. Sectio.2 Arithmetic Sequeces ad Series -.2 Arithmetic Sequeces ad Series Arithmetic Sequeces Arithmetic Series Key Terms: arithmetic sequece (arithmetic progressio), commo differece, arithmetic series

More information

UNIT 4 Section 8 Estimating Population Parameters using Confidence Intervals

UNIT 4 Section 8 Estimating Population Parameters using Confidence Intervals UNIT 4 Sectio 8 Estimatig Populatio Parameters usig Cofidece Itervals To make ifereces about a populatio that caot be surveyed etirely, sample statistics ca be take from a SRS of the populatio ad used

More information

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup   Note: Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values

More information

Diego Nehab. n A Transformation For Extracting New Descriptors of Shape. n Locus of points equidistant from contour

Diego Nehab. n A Transformation For Extracting New Descriptors of Shape. n Locus of points equidistant from contour Diego Nehab A Trasformatio For Extractig New Descriptors of Shape Locus of poits equidistat from cotour Medial Axis Symmetric Axis Skeleto Shock Graph Shaked 96 1 Shape matchig Aimatio Dimesio reductio

More information

Eigenimages. Digital Image Processing: Bernd Girod, Stanford University -- Eigenimages 1

Eigenimages. Digital Image Processing: Bernd Girod, Stanford University -- Eigenimages 1 Eigeimages Uitary trasforms Karhue-Loève trasform ad eigeimages Sirovich ad Kirby method Eigefaces for geder recogitio Fisher liear discrimat aalysis Fisherimages ad varyig illumiatio Fisherfaces vs. eigefaces

More information

The number n of subintervals times the length h of subintervals gives length of interval (b-a).

The number n of subintervals times the length h of subintervals gives length of interval (b-a). Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's

More information