Ch 9.3 Geometric Sequences and Series Lessons

Size: px
Start display at page:

Download "Ch 9.3 Geometric Sequences and Series Lessons"

Transcription

1 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 series, if it exists. Use geometric sequeces ad series to model real-world problems. CONCEPTUAL OBJECTIVES Uderstad the differece betwee a geometric sequece ad a geometric series. Distiguish betwee a arithmetic sequece ad a geometric sequece. Uderstad why it is ot possible to evaluate all ifiite geometric series. Geometric Sequeces I Sectio 9.2, we discussed arithmetic sequeces, where successive terms had a commo differece. I other words, each term was foud by addig the same costat to the previous term. I this sectio we discuss geometric sequeces, where successive terms have a commo ratio. I other words, each term is foud by multiplyig the previous term by the same costat. The sequece 4, 12, 36, is geometric because each successive term is foud by multiplyig the previous term by 3.

2 DEFINITION Geometric Sequeces A sequece is geometric if each term i the sequece is foud by multiplyig the previous term by a umber r, so that 1 a 1 r a = + r a. Because = a+, the umber r is called the commo ratio. EXAMPLE 1 Idetifyig the Commo Ratio i Geometric Sequeces Fid the commo ratio for each of the geometric sequeces. a. 5, 20, 80, 320,...

3 Fid the commo ratio for each of the geometric sequeces b. 1,,,,

4 c. $5.000, $5,500, $6,050, $6, 655,... YOUR TURN Fid the commo ratio of each geometric series. a. 1, a. solutio: -3 b. 320, 80, 20, 5,... b. solutio 1 or

5 The Geeral (th) Term of a Geometric Sequece To fid a formula for the geeral, or th, term of a geometric sequece, write out the first several terms ad look for a patter. THE TH TERM OF A GEOMETRIC SEQUENCE The th term of a geometric sequece with commo ratio r is give by 1 a = a r or by 1 for 1 a = 1 a + 1 r for 0

6 EXAMPLE 2 Fidig the th Term of a Geometric Sequece Fid the 7th term of the sequece 2, 10, 50, 250

7 YOUR TURN Fid the 8th term of the sequece 3, 12, 48, 192 Aswer: 49,152 EXAMPLE 3 Fidig the Geometric Sequece Fid the geometric sequece whose 5th term is 0.01 ad whose commo ratio is 0.1. YOUR TURN Fid the geometric sequece whose 4th term is 3 ad whose commo ratio is 1 3. Aswer: 81, 27, 9, 3, 1...

8 Geometric Series The sum of the terms of a geometric sequece is called a geometric series. If we oly sum the first terms of a geometric sequece, the result is a fiite geometric series give by To develop a formula for the th partial sum, we multiply the above equatio by r: Subtractig the secod equatio from the first equatio, we fid that all of the terms o the right side drop out except the first term i the first equatio ad the last term i the secod equatio: Cotiued o ext page.

9 Factor the S, out of the left side ad the a 1, out of the right side: Divide both sides by (1- r), assumig r 1. The result is a geeral formula for the sum of a fiite geometric series: 1 ( 1 r ) S = a r 1 k = 1 ( 1 r) S = a r = a + a r+ a r + a r + + a r k Study Tip The uderscript k = 1 applies oly whe the summatio starts at the a 1 term. It is importat to ote which term is the startig term.

10 EXAMPLE 4 Evaluatig a Fiite Geometric Series Evaluate the fiite geometric series.

11 b. The first ie terms of the series

12 The sum of a ifiite geometric sequece is called a ifiite geometric series. Some ifiite geometric series coverge (yield a fiite sum) ad some diverge (do ot have a fiite sum). For example, For ifiite geometric series that coverge, the partial sum S, approaches a sigle umber as gets large. The formula used to evaluate a fiite geometric series ca be exteded to a ifiite geometric series for certai values of,: If r < 1, the whe r is raised to a power, it cotiues to get smaller, approachig 0. For those values of r, ifiite geometric series coverges to a fiite sum.

13 EVALUATING AN INFINITE GEOMETRIC SERIES The sum of a ifiite geometric series is give by the formula 1 a1 r = a1 r < 1 1 r = 0 ( ) Study Tip The formula used to evaluate a ifiite geometric series is: First term 1 Ratio EXAMPLE 5 Determiig Whether the Sum of a Ifiite Series Exists Determie whether the sum exists for each of the geometric series. a

14 Determie whether the sum exists for each of the geometric series. YOUR TURN Determie whether the sum exists for the followig geometric series. Aswer: a. yes b. o Do you expect ad + + to sum to the same umber? The aswer is o, because the secod series is a alteratig series ad terms are both added ad subtracted. Hece, we would expect the secod series to sum to a smaller umber tha the first series sums to.

15 EXAMPLE 6 Evaluatig a Ifiite Geometric Series Evaluate each ifiite geometric series.

16 Notice that the alteratig series summed to 3 4, whereas the positive series summed to 3 2. YOUR TURN Fid the sum of each ifiite geometric series.

17 It is importat to ote the restrictio o the commo ratio r. The absolute value of the commo ratio has to be strictly less tha 1 for a ifiite geometric series to coverge. Otherwise the ifiite geometric series diverges.

18 EXAMPLE 7 Evaluatig a Ifiite Geometric Series Evaluate the ifiite geometric series, if possible.

19 Applicatios Suppose you are give a job offer with a guarateed percetage raise per year. What will your aual salary be 10 years from ow? That aswer ca be obtaied usig a geometric sequece. Suppose you wat to make volutary cotributios to a retiremet accout directly debited from your paycheck every moth. Suppose the accout ears a fixed percetage rate: 1. How much will you have i 30 years if you deposit $50 a moth? 2. What is the differece i the total you will have i 30 years if you deposit $100 a moth istead? These importat questios about your persoal fiaces ca be aswered usig geometric sequeces ad series. EXAMPLE B Future Salary: Geometric Sequece Suppose you are offered a job as a evet plaer for the PGA Tour. The startig salary is $45,000, ad employees are give a 5% raise per year. What will your aual salary be durig the 10th year with the PGA Tour?

20 YOUR TURN Suppose you are offered a job with AT&T at $37,000 per year with a guarateed raise of 4% after every year. What will your aual salary be after 15 years with the compay? Aswer: $64,072.03

21 EXAMPLE 9 Savigs Growth: Geometric Series Kare has maitaied acrylic ails by payig for them with moey eared from a part-time job. After hearig a lecture from her ecoomics professor o the importace of ivestig early i life, she decides to remove the acrylic ails, which cost $50 per moth, ad do her ow maicures. She has that S50 automatically debited from her checkig accout o the first of every moth ad put ito a moey market accout that receives 3% iterest compouded mothly. What will the balace be i the moey market accout exactly 2 years from the day of her iitial $50 deposit?

22 YOUR TURN Repeat Example 9 with Kare puttig $100 (istead of $50) i the same moey market. Assume she does this for 4 years (istead of 2 years). Aswer: $

23 Sectio 9.3 Summary I this sectio, we discussed geometric sequeces, i which each successive term is foud by multiplyig the previous term by a costat, so that a = 1 r + a. That costat, r, is called the commo ratio. The th term of a geometric sequece is give by 1 a = a r or by 1 for 1 a = 1 a + 1 r for 0 The sum of the terms of a geometric sequece is called a geometric series. Fiite geometric series coverge to a umber. Ifiite geometric series coverge to a umber if the absolute value of the commo ratio is less tha 1. If the absolute value of the commo ratio is greater tha or equal to 1, the ifiite geometric series diverges ad the sum does ot exist. May real-world applicatios ivolve geometric sequeces ad series, such as growth of salaries ad auities through percetage icreases.

24 Sectio 9.3 Summary cotiued Fiite geometric series: 1 ( 1 r ) S = a r 1 k = 1 ( 1 r) S = a r = a + a r+ a r + a r + + a r k EVALUATING AN INFINITE GEOMETRIC SERIES The sum of a ifiite geometric series is give by the formula 1 a1 r = a1 r < 1 1 r = 0 ( ) Next sectio is 9.3 Exercises

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

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

NTH, GEOMETRIC, AND TELESCOPING TEST

NTH, 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 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

SEQUENCES AND SERIES

SEQUENCES AND SERIES SEQUENCES AND SERIES U N I The umber of gifts set i the popular Christmas Carol days of Christmas form a sequece. A part of the sog goes this way O the th day of Christmas my true love gave to me drummers

More information

4.3 Modeling with Arithmetic Sequences

4.3 Modeling with Arithmetic Sequences Name Class Date 4.3 Modelig with Arithmetic Sequeces Essetial Questio: How ca you solve real-world problems usig arithmetic sequeces? Resource Locker Explore Iterpretig Models of Arithmetic Sequeces You

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

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

1.8 What Comes Next? What Comes Later?

1.8 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 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

Investigation Monitoring Inventory

Investigation Monitoring Inventory Ivestigatio Moitorig Ivetory Name Period Date Art Smith has bee providig the prits of a egravig to FieArt Gallery. He plas to make just 2000 more prits. FieArt has already received 70 of Art s prits. The

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

Examples and Applications of Binary Search

Examples 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 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

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

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

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

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

CS 683: Advanced Design and Analysis of Algorithms

CS 683: Advanced Design and Analysis of Algorithms CS 683: Advaced Desig ad Aalysis of Algorithms Lecture 6, February 1, 2008 Lecturer: Joh Hopcroft Scribes: Shaomei Wu, Etha Feldma February 7, 2008 1 Threshold for k CNF Satisfiability I the previous lecture,

More information

UNIT 1 RECURRENCE RELATIONS

UNIT 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 information

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1 CS200: Hash Tables Prichard Ch. 13.2 CS200 - Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted array-based Usorted array-based Balaced Search Trees O(log ) O() O() O() O(1) O()

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

Section 7.2: Direction Fields and Euler s Methods

Section 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 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

LU Decomposition Method

LU 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

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria.

Computer 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 information

5.3 Recursive definitions and structural induction

5.3 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 information

MATHEMATICS IN EVERYDAY LIFE 8

MATHEMATICS IN EVERYDAY LIFE 8 MAHEMAICS IN EVEYDAY LIFE 8 Chapter 0 : Compoud Iterest ANSWE KEYS EXECISE 0.. Pricipal for st year `7600 ate of iterest 5% per aum Iterest for st year ` 7600 5 00 `80 Amout at the ed of st year `7600

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

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

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

Octahedral Graph Scaling

Octahedral Graph Scaling Octahedral Graph Scalig Peter Russell Jauary 1, 2015 Abstract There is presetly o strog iterpretatio for the otio of -vertex graph scalig. This paper presets a ew defiitio for the term i the cotext of

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis 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 information

Computers and Scientific Thinking

Computers and Scientific Thinking Computers ad Scietific Thikig David Reed, Creighto Uiversity Chapter 15 JavaScript Strigs 1 Strigs as Objects so far, your iteractive Web pages have maipulated strigs i simple ways use text box to iput

More information

1.2. Modeling Growth and Decay. Launch LESSON 1.2. Vocabulary decay growth principal simple interest compound interest. Materials

1.2. Modeling Growth and Decay. Launch LESSON 1.2. Vocabulary decay growth principal simple interest compound interest. Materials LESSON LESSON.2 Modelig Growth ad Decay.2 Each sequece you geerated i the previous lesso was either a arithmetic sequece with a recursive rule i the form u = u + d or a geometric sequece with a recursive

More information

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,

More information

1 Graph Sparsfication

1 Graph Sparsfication CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider

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

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

Solution printed. Do not start the test until instructed to do so! CS 2604 Data Structures Midterm Spring, Instructions:

Solution 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 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

Homework 1 Solutions MA 522 Fall 2017

Homework 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 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

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 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 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

It just came to me that I 8.2 GRAPHS AND CONVERGENCE

It 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 information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

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

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

Lecture 28: Data Link Layer

Lecture 28: Data Link Layer Automatic Repeat Request (ARQ) 2. Go ack N ARQ Although the Stop ad Wait ARQ is very simple, you ca easily show that it has very the low efficiecy. The low efficiecy comes from the fact that the trasmittig

More information

Intermediate Statistics

Intermediate Statistics Gait Learig Guides Itermediate Statistics Data processig & display, Cetral tedecy Author: Raghu M.D. STATISTICS DATA PROCESSING AND DISPLAY Statistics is the study of data or umerical facts of differet

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS

MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS Fura Uiversity Electroic Joural of Udergraduate Matheatics Volue 00, 1996 6-16 MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS DAVID SITTON Abstract. How ay edges ca there be i a axiu atchig i a coplete

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

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

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

6.854J / J Advanced Algorithms Fall 2008

6.854J / 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 information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

Random Graphs and Complex Networks T

Random Graphs and Complex Networks T Radom Graphs ad Complex Networks T-79.7003 Charalampos E. Tsourakakis Aalto Uiversity Lecture 3 7 September 013 Aoucemet Homework 1 is out, due i two weeks from ow. Exercises: Probabilistic iequalities

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 26 Ehaced Data Models: Itroductio to Active, Temporal, Spatial, Multimedia, ad Deductive Databases Copyright 2016 Ramez Elmasri ad Shamkat B.

More information

Normal Distributions

Normal Distributions Normal Distributios Stacey Hacock Look at these three differet data sets Each histogram is overlaid with a curve : A B C A) Weights (g) of ewly bor lab rat pups B) Mea aual temperatures ( F ) i A Arbor,

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

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

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

Chapter 3 Classification of FFT Processor Algorithms

Chapter 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 information

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Descriptive Statistics

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Descriptive Statistics ENGI 44 Probability ad Statistics Faculty of Egieerig ad Applied Sciece Problem Set Descriptive Statistics. If, i the set of values {,, 3, 4, 5, 6, 7 } a error causes the value 5 to be replaced by 50,

More information

1&1 Next Level Hosting

1&1 Next Level Hosting 1&1 Next Level Hostig Performace Level: Performace that grows with your requiremets Copyright 1&1 Iteret SE 2017 1ad1.com 2 1&1 NEXT LEVEL HOSTING 3 Fast page loadig ad short respose times play importat

More information

Exercise 6 (Week 42) For the foreign students only.

Exercise 6 (Week 42) For the foreign students only. These are the last exercises of the course. Please, remember that to pass exercises, the sum of the poits gathered by solvig the questios ad attedig the exercise groups must be at least 4% ( poits) of

More information

Filter design. 1 Design considerations: a framework. 2 Finite impulse response (FIR) filter design

Filter design. 1 Design considerations: a framework. 2 Finite impulse response (FIR) filter design Filter desig Desig cosideratios: a framework C ı p ı p H(f) Aalysis of fiite wordlegth effects: I practice oe should check that the quatisatio used i the implemetatio does ot degrade the performace of

More information

Protected points in ordered trees

Protected points in ordered trees Applied Mathematics Letters 008 56 50 www.elsevier.com/locate/aml Protected poits i ordered trees Gi-Sag Cheo a, Louis W. Shapiro b, a Departmet of Mathematics, Sugkyukwa Uiversity, Suwo 440-746, Republic

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

3n

3n 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 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

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1 COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,

More information

Linear Time-Invariant Systems

Linear Time-Invariant Systems 9/9/00 LIEAR TIE-IVARIAT SYSTES Uit, d Part Liear Time-Ivariat Sstems A importat class of discrete-time sstem cosists of those that are Liear Priciple of superpositio Time-ivariat dela of the iput sequece

More information

Basic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000.

Basic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000. 5-23 The course that gives CM its Zip Memory Maagemet II: Dyamic Storage Allocatio Mar 6, 2000 Topics Segregated lists Buddy system Garbage collectio Mark ad Sweep Copyig eferece coutig Basic allocator

More information

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters. SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that

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

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19 CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.

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

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

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a 4. [10] Usig a combiatorial argumet, prove that for 1: = 0 = Let A ad B be disjoit sets of cardiality each ad C = A B. How may subsets of C are there of cardiality. We are selectig elemets for such a subset

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

Reliable Transmission. Spring 2018 CS 438 Staff - University of Illinois 1

Reliable Transmission. Spring 2018 CS 438 Staff - University of Illinois 1 Reliable Trasmissio Sprig 2018 CS 438 Staff - Uiversity of Illiois 1 Reliable Trasmissio Hello! My computer s ame is Alice. Alice Bob Hello! Alice. Sprig 2018 CS 438 Staff - Uiversity of Illiois 2 Reliable

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

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

Xiaozhou (Steve) Li, Atri Rudra, Ram Swaminathan. HP Laboratories HPL Keyword(s): graph coloring; hardness of approximation

Xiaozhou (Steve) Li, Atri Rudra, Ram Swaminathan. HP Laboratories HPL Keyword(s): graph coloring; hardness of approximation Flexible Colorig Xiaozhou (Steve) Li, Atri Rudra, Ram Swamiatha HP Laboratories HPL-2010-177 Keyword(s): graph colorig; hardess of approximatio Abstract: Motivated b y reliability cosideratios i data deduplicatio

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

Taylor 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 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 information

c-dominating Sets for Families of Graphs

c-dominating Sets for Families of Graphs c-domiatig Sets for Families of Graphs Kelsie Syder Mathematics Uiversity of Mary Washigto April 6, 011 1 Abstract The topic of domiatio i graphs has a rich history, begiig with chess ethusiasts i the

More information

Optimum Solution of Quadratic Programming Problem: By Wolfe s Modified Simplex Method

Optimum Solution of Quadratic Programming Problem: By Wolfe s Modified Simplex Method Volume VI, Issue III, March 7 ISSN 78-5 Optimum Solutio of Quadratic Programmig Problem: By Wolfe s Modified Simple Method Kalpaa Lokhade, P. G. Khot & N. W. Khobragade, Departmet of Mathematics, MJP Educatioal

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

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5 Morga Kaufma Publishers 26 February, 28 COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 5 Set-Associative Cache Architecture Performace Summary Whe CPU performace icreases:

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

COMP 558 lecture 6 Sept. 27, 2010

COMP 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 information

Structuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software

Structuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software Structurig Redudacy for Fault Tolerace CSE 598D: Fault Tolerat Software What do we wat to achieve? Versios Damage Assessmet Versio 1 Error Detectio Iputs Versio 2 Voter Outputs State Restoratio Cotiued

More information

New Results on Energy of Graphs of Small Order

New 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 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

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information