The Graphs of Polynomial Functions
|
|
- Alexia Russell
- 5 years ago
- Views:
Transcription
1 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 is a oegative iteger. The umbers a0, a1, a2,, a are called the coefficiets of the polyomial fuctio. The umber a is called the leadig coefficiet ad a0 is called the costat coefficiet , 2, ad 4 Determie if the give fuctio is a polyomial fuctio. If it is, the idetify the degree, the leadig coefficiet, ad the costat coefficiet. a) f(x) = b) f(x) = Degree Leadig Coefficiet Costat Coefficiet Degree Leadig Coefficiet Costat Coefficiet Objective 2: Sketchig the Graphs of Power Fuctios (a) x (b) 2 x (c) 3 x (d) 4 x (e) x ad 15 Use the associated power fuctio ad trasformatios to sketch the followig fuctios. a) f(x) = b) f(x) =
2 Objective 3: Determiig the Ed Behavior of Polyomial Fuctios Process for Determiig the Ed Behavior of a Polyomial Fuctio 1 2 ax a 1x a 2x a1x a0. If the degree is odd, the graph has opposite left-had ad right-had ed behavior, that is, the graph starts ad fiishes i opposite directios. Odd degree polyomials have opposite left-had ad right-had ed behavior. a 0, odd degree a 0, odd degree If the degree is eve, the graph has the same left-had ad right-had ed behavior, that is, the graph starts ad fiishes i the same directio. Eve degree polyomials have the same left-had ad right-had ed behavior. a 0, eve degree a 0, eve degree ad 21 Use the ed behavior of the graph of the give polyomial fuctio to aswer the followig: 16 a)the degree is (eve or odd). b) The leadig coefficiet is (positive or egative). 21 a)the degree is (eve or odd). b) The leadig coefficiet is (positive or egative).
3 Objective 4: Determiig the Itercepts of a Polyomial Fuctio The umber x c is called a zero of a fuctio f if f( c) 0. If c is a real umber, the c is a x- itercept. Therefore, to fid the x-itercepts of a polyomial fuctio y, we must fid the real solutios of the equatio f( x) Fid the itercepts of the polyomial fuctio f(x) =. The y-itercept is y =. The x-itercept(s) is/are x = Fid the itercepts of the polyomial fuctio f(x) =. The y-itercept is y =. The x-itercept(s) is/are x =.
4 Objective 5: Determiig the Real Zeros of Polyomial Fuctios ad Their Multiplicities The Shape of the Graph of a Polyomial Fuctio Near a Zero of Multiplicity k. Suppose c is a real zero of a polyomial fuctio f of multiplicity k, that is, x c is a factor of f. The the shape of the graph of f ear x = c is as follows: If k 1 is eve, the the graph touches the x-axis at x = c. k OR If k 1 is odd, the the graph crosses the x-axis at x = c. OR Determie the real zeros ad their multiplicities of f(x) =. a) The real zeros of the polyomial are x =. (Use a comma to separate aswers as eeded. Type a exact aswer, usig radicals as eeded.) b) The multiplicity of the zero located farthest left o the x-axis is. The multiplicity of the zero located farthest right o the x-axis is. The graph the x-axis at the leftmost zero. (touches or crosses) The graph the x-axis at the rightmost zero. (touches or crosses)
5 Objective 6: Sketchig the Graph of a Polyomial Fuctio Four-Step Process for Sketchig the Graph of a Polyomial Fuctio 1. Determie the ed behavior. 2. Plot the y-itercept f(0) a0. 3. Completely factor f to fid all real zeros ad their multiplicities*. 4. Choose a test value betwee each real zero ad sketch the graph. * This is the most difficult step ad will be discussed i further detail i the subsequet sectios of this chapter Sketch the polyomial fuctio f(x) = usig the four-step process. The left-had behavior starts ad the right-had behavior eds. The y-itercept is. The real zeros of the polyomial are x =. The multiplicity of the zero located farthest left o the x-axis is. The multiplicity of the zero located betwee the leftmost ad rightmost zeros is. The multiplicity of the zero located farthest right o the x-axis is. What is the value of the test poit at x =? y = Sketch the graph.
6 Objective 7: Determiig a Possible Equatio of a Polyomial Fuctio Give its Graph Aalyze the graph to address the followig about the polyomial fuctio it represets. a) Is the degree eve or odd? b) Is the leadig coefficiet positive or egative? c) The value of the costat coefficiet is. d) The leftmost real zero is x =, which has a multiplicity. The secod real zero from the left is x =, which has a multiplicity. The secod real zero from the right is x =, which has a multiplicity. The rightmost real zero is x =, which has a multiplicity. e) Select a possible fuctio that could be represeted by this graph.
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 information2) 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 informationPolynomial 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 information9 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
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:
More informationTest 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 informationSection 7.2: Direction Fields and Euler s Methods
Sectio 7.: Directio ields ad Euler s Methods Practice HW from Stewart Tetbook ot to had i p. 5 # -3 9-3 odd or a give differetial equatio we wat to look at was to fid its solutio. I this chapter we will
More informationArea 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 informationName 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 informationSouth 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 informationCreating 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 informationMath 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 informationPattern 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 informationParabolic 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 informationCivil 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 informationWebAssign 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 informationArithmetic 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 informationAn (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 informationEVALUATION 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 informationBezier 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 information9.1. 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 informationCONTINUI 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 informationBig-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 informationIntro 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 informationName 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 informationCh 9.3 Geometric Sequences and Series Lessons
Ch 9.3 Geometric Sequeces ad Series Lessos SKILLS OBJECTIVES Recogize a geometric sequece. Fid the geeral, th term of a geometric sequece. Evaluate a fiite geometric series. Evaluate a ifiite geometric
More informationAlpha 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 informationNumerical 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 informationBig-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 informationThe 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 informationChapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3
More informationA 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 informationIntegration: Reduction Formulas Any positive integer power of sin x can be integrated by using a reduction formula.
Itegratio: Reductio Formulas Ay positive iteger power of si x ca be itegrated by usig a reductio formula. Prove that for ay iteger 2, si xdx = 1 si 1 x cos x + 1 si Solutio. Weuseitegratiobyparts. Let
More information1. 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 informationTaylor Series and Applications - (8.7)(8.8) b n!x # c" n for x # c " R.!x # c" # f %%!c" 2! T!x"! 1 # x # x2 2! # x3. 3! n!
Taylor Series ad Applicatios - (8.7)(8.8). Taylor ad Maclauri Series: For a give fuctio f!x, how ca we fid its power series represetatio? If f has a power series represetatio at a umber a, that is, if
More informationPseudocode ( 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 informationProject 2.5 Improved Euler Implementation
Project 2.5 Improved Euler Implemetatio Figure 2.5.10 i the text lists TI-85 ad BASIC programs implemetig the improved Euler method to approximate the solutio of the iitial value problem dy dx = x+ y,
More informationPLEASURE 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 informationUniversity 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 informationExact 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 informationMajor CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70
NOTE:. Attempt all seve questios. Major CSL 02 2. Write your ame ad etry o o every sheet of the aswer script. Time 2 Hrs Max Marks 70 Q No Q Q 2 Q 3 Q 4 Q 5 Q 6 Q 7 Total MM 6 2 4 0 8 4 6 70 Q. Write a
More informationV.T. Chow, Open Channel Hydraulics, 1959 problem 9-8. for each reach computed in file below and placed here. = 5.436' yc = 2.688'
V.T. Chow, Ope Chael Hydraulics, 959 problem 9-8 y c ad y for each reach computed i file below ad placed here WSE =47.0' 7.0' 70.0' y =.86' yc =.688' So =.0 y = 5.46' yc =.688' So =.0004 y =.70' yc =.688'
More informationHomework 1 Solutions MA 522 Fall 2017
Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear
More informationThe 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 informationRecursive 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 information3n
Prctice Set 6 Sequeces d Series Clcultor Required Objectives Alyze ptters i sequeces to determie subsequet terms. Fid the first four terms of sequece give equtio for. Fid expressio for give sequece. Expd
More informationOCR 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 informationChapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.
Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4
More informationOnes 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 information1.8 What Comes Next? What Comes Later?
35 1.8 What Comes Next? What Comes Later? A Practice Uderstadig Task For each of the followig tables, CC BY Hiroaki Maeda https://flic.kr/p/6r8odk describe how to fid the ext term i the sequece, write
More informationcondition 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 information3D 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 informationCOMP 558 lecture 6 Sept. 27, 2010
Radiometry We have discussed how light travels i straight lies through space. We would like to be able to talk about how bright differet light rays are. Imagie a thi cylidrical tube ad cosider the amout
More informationConsider 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 informationThe 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 informationNTH, GEOMETRIC, AND TELESCOPING TEST
NTH, GEOMETRIC, AND TELESCOPING TEST Sectio 9. Calculus BC AP/Dual, Revised 08 viet.dag@humbleisd.et /4/08 0:0 PM 9.: th, Geometric, ad Telescopig Test SUMMARY OF TESTS FOR SERIES Lookig at the first few
More informationThe following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels:
DBE Memo#1 MARK 5 MEMO #18 MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS 1886 November 19, 24 Telephoe: 978-692-4764 Fax: 781-981-59 To: From: Mark 5 Developmet Group
More informationModule 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 informationAnalysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis
Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems
More informationComputer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria.
Computer Sciece Foudatio Exam August, 005 Computer Sciece Sectio A No Calculators! Name: SSN: KEY Solutios ad Gradig Criteria Score: 50 I this sectio of the exam, there are four (4) problems. You must
More informationNew Results on Energy of Graphs of Small Order
Global Joural of Pure ad Applied Mathematics. ISSN 0973-1768 Volume 13, Number 7 (2017), pp. 2837-2848 Research Idia Publicatios http://www.ripublicatio.com New Results o Eergy of Graphs of Small Order
More informationUNIT 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 informationSolution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions:
CS 604 Data Structures Midterm Sprig, 00 VIRG INIA POLYTECHNIC INSTITUTE AND STATE U T PROSI M UNI VERSI TY Istructios: Prit your ame i the space provided below. This examiatio is closed book ad closed
More informationChapter 3 Classification of FFT Processor Algorithms
Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As
More informationNormals. In OpenGL the normal vector is part of the state Set by glnormal*()
Ray Tracig 1 Normals OpeG the ormal vector is part of the state Set by glnormal*() -glnormal3f(x, y, z); -glnormal3fv(p); Usually we wat to set the ormal to have uit legth so cosie calculatios are correct
More informationExamples and Applications of Binary Search
Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We
More information1.2 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 informationDiego 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 informationLecture 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 informationRecursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle:
Recursio Recursio Jordi Cortadella Departmet of Computer Sciece Priciple: Reduce a complex problem ito a simpler istace of the same problem Recursio Itroductio to Programmig Dept. CS, UPC 2 Mathematical
More informationOutline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis
Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis
More informationConvex 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 informationEE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering
EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors
More information5.3 Recursive definitions and structural induction
/8/05 5.3 Recursive defiitios ad structural iductio CSE03 Discrete Computatioal Structures Lecture 6 A recursively defied picture Recursive defiitios e sequece of powers of is give by a = for =0,,, Ca
More informationCIS 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 informationOptimal 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 informationA Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions
Proceedigs of the 10th WSEAS Iteratioal Coferece o APPLIED MATHEMATICS, Dallas, Texas, USA, November 1-3, 2006 316 A Geeralized Set Theoretic Approach for Time ad Space Complexity Aalysis of Algorithms
More information6.854J / J Advanced Algorithms Fall 2008
MIT OpeCourseWare http://ocw.mit.edu 6.854J / 18.415J Advaced Algorithms Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.415/6.854 Advaced Algorithms
More information1. (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,
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 informationUNIT 1 RECURRENCE RELATIONS
UNIT RECURRENCE RELATIONS Structure Page No.. Itroductio 7. Objectives 7. Three Recurret Problems 8.3 More Recurreces.4 Defiitios 4.5 Divide ad Coquer 7.6 Summary 9.7 Solutios/Aswers. INTRODUCTION I the
More informationLU Decomposition Method
SOLUTION OF SIMULTANEOUS LINEAR EQUATIONS LU Decompositio Method Jamie Traha, Autar Kaw, Kevi Marti Uiversity of South Florida Uited States of America kaw@eg.usf.edu http://umericalmethods.eg.usf.edu Itroductio
More information( 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 informationLecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming
Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis
More informationCHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs
CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics
More informationData Structures and Algorithms. Analysis of Algorithms
Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output
More informationDescriptive 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 informationRunning Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The
More informationIt just came to me that I 8.2 GRAPHS AND CONVERGENCE
44 Chapter 8 Discrete Mathematics: Fuctios o the Set of Natural Numbers (a) Take several odd, positive itegers for a ad write out eough terms of the 3N sequece to reach a repeatig loop (b) Show that ot
More informationSecond-Order Domain Decomposition Method for Three-Dimensional Hyperbolic Problems
Iteratioal Mathematical Forum, Vol. 8, 013, o. 7, 311-317 Secod-Order Domai Decompositio Method for Three-Dimesioal Hyperbolic Problems Youbae Ju Departmet of Applied Mathematics Kumoh Natioal Istitute
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationAnalysis of Algorithms
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The
More informationLighting and Shading. Outline. Raytracing Example. Global Illumination. Local Illumination. Radiosity Example
CSCI 480 Computer Graphics Lecture 9 Lightig ad Shadig Light Sources Phog Illumiatio Model Normal Vectors [Agel Ch. 6.1-6.4] February 13, 2013 Jerej Barbic Uiversity of Souther Califoria http://www-bcf.usc.edu/~jbarbic/cs480-s13/
More informationAlgorithms 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 informationHere are the coefficients of the terms listed above: 3,5,2,1,1 respectively.
*. Operatios with Poloials: Let s start b defiig soe words. Ter: A ter is a uber, variable or the product of a uber ad variable(s). For eaple:,, z, a Coefficiet: A coefficiet is the ueric factor of the
More informationChapter 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 informationMath 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 informationCS 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 informationLecturers: 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 informationLecture 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 informationA graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n))
ca see that time required to search/sort grows with size of We How do space/time eeds of program grow with iput size? iput. time: cout umber of operatios as fuctio of iput Executio size operatio Assigmet:
More information