Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Size: px
Start display at page:

Download "Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following."

Transcription

1 Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal here all that often n the remander of ths course, but there are a couple of sectons n whch we wll need ths and so t s best to get t out of the way at ths pont. In the radcals secton we noted that we won t get a real number out of a square root of a negatve number. For nstance - 9 sn t a real number snce there s no real number that we can square and get a NEGATIVE 9. Now we also saw that f a and b were both postve then ab = a b. For a second let s forget that restrcton and do the followng. - 9 = ( 9)( - 1) = 9-1= 3-1 Now, - 1 s not a real number, but f you thnk about t we can do ths for any square root of a negatve number. For nstance, = 100-1= = = 90-1 etc. So, even f the number sn t a perfect square we can stll always reduce the square root of a negatve number down to the square root of a postve number (whch we or a calculator can deal wth) tmes - 1. So, f we just had a way to deal wth - 1 we could actually deal wth square roots of negatve numbers. Well the realty s that, at ths level, there just sn t any way to deal wth - 1 so nstead of dealng wth t we wll make t go away so to speak by usng the followng defnton. Note that f we square both sdes of ths we get, = - 1 =- 1 It wll be mportant to remember ths later on. Ths shows that, n some way, s the only number that we can square and get a negatve value. Usng ths defnton all the square roots above become, - 9 = = 10-5 = 5-90 = 90 These are all examples of complex numbers. The natural queston at ths pont s probably just why do we care about ths? The answer s that, as we wll see n the next chapter, sometmes we wll run across the square roots of negatve 007 Paul Dawkns 5

2 numbers and we re gong to need a way to deal wth them. So, to deal wth them we wll need to dscuss complex numbers. So, let s start out wth some of the basc defntons and termnology for complex numbers. The standard form of a complex number s a+ b where a and b are real numbers and they can be anythng, postve, negatve, zero, ntegers, fractons, decmals, t doesn t matter. When n the standard form a s called the real part of the complex number and b s called the magnary part of the complex number. Here are some examples of complex numbers The last two probably need a lttle more explanaton. It s completely possble that a or b could be zero and so n 16 the real part s zero. When the real part s zero we often wll call the complex number a purely magnary number. In the last example (113) the magnary part s zero and we actually have a real number. So, thnkng of numbers n ths lght we can see that the real numbers are smply a subset of the complex numbers. The conjugate of the complex number a+ b s the complex number a- b. In other words, t s the orgnal complex number wth the sgn on the magnary part changed. Here are some examples of complex numbers and ther conjugates. complex number conjugate Notce that the conjugate of a real number s just tself wth no changes. Now we need to dscuss the basc operatons for complex numbers. We ll start wth addton and subtracton. The easest way to thnk of addng and/or subtractng complex numbers s to thnk of each complex number as a polynomal and do the addton and subtracton n the same way that we add or subtract polynomals. Example 1 Perform the ndcated operaton and wrte the answers n standard form (a) (b) ( 4+ 1) -( 3-15) (c) 5-( - 9+ ) Soluton There really sn t much to do here other than add or subtract. Note that the parentheses on the frst terms are only there to ndcate that we re thnkng of that term as a complex number and n 007 Paul Dawkns 53

3 general aren t used = 1-3 (a) = = 1+ 7 (b) = = 9+ 4 (c) ( ) Next let s take a look at multplcaton. Agan, wth one small dfference, t s probably easest to just thnk of the complex numbers as polynomals so multply them out as you would polynomals. The one dfference wll come n the fnal step as we ll see. Example Multply each of the followng and wrte the answers n standard form [Soluton] (a) ( ) (b) ( 1 5)( 9 ) (c) ( 4 )( 3) (d) ( 1 8)( 1 8) [Soluton] + + [Soluton] - + [Soluton] Soluton (a) So all that we need to do s dstrbute the 7 through the parenthess = ( ) Now, ths s where the small dfference mentoned earler comes nto play. Ths number s NOT n standard form. The standard form for complex numbers does not have an n t. Ths however s not a problem provded we recall that =- 1 Usng ths we get, 7-5+ = = We also rearranged the order so that the real part s lsted frst. (b) In ths case we wll FOIL the two numbers and we ll need to also remember to get rd of the = = = ( ) (c) Same thng wth ths one = = = ( ) (d) Here s one fnal multplcaton that wll lead us nto the next topc = = 1+ 64= 65 ( )( ) Don t get excted about t when the product of two complex numbers s a real number. That can and wll happen on occason. 007 Paul Dawkns 54

4 In the fnal part of the prevous example we multpled a number by ts conjugate. There s a nce general formula for ths that wll be convenent when t comes to dscusson dvson of complex numbers. ( a+ b)( a- b) = a - ab+ ab- b = a + b So, when we multply a complex number by ts conjugate we get a real number gven by, ( a+ b)( a- b) = a + b Now, we gave ths formula wth the comment that t wll be convenent when t came to dvdng complex numbers so let s look at a couple of examples. Example 3 Wrte each of the followng n standard form. (a) 3 - [Soluton] (b) [Soluton] 9-8 (c) [Soluton] 1+ (d) 6-9 [Soluton] Soluton So, n each case we are really lookng at the dvson of two complex numbers. The man dea here however s that we want to wrte them n standard form. Standard form does not allow for any 's to be n the denomnator. So, we need to get the 's out of the denomnator. Ths s actually farly smple f we recall that a complex number tmes ts conjugate s a real number. So, f we multply the numerator and denomnator by the conjugate of the denomnator we wll be able to elmnate the from the denomnator. Now that we ve fgured out how to do these let s go ahead and work the problems. (a) ( 3-) ( -7) = = = = Notce that to offcally put the answer n standard form we broke up the fracton nto the real and magnary parts. 3 3 ( 9+ ) (b) = = = (c) ( 1 ) = = = = Paul Dawkns 55

5 (d) Ths one s a lttle dfferent from the prevous ones snce the denomnator s a pure magnary number. It can be done n the same manner as the prevous ones, but there s a slghtly easer way to do the problem. Frst, break up the fracton as follows = - = - Now, we want the out of the denomnator and snce there s only an n the denomnator of the frst term we wll smply multply the numerator and denomnator of the frst term by an ( ) = - = - = - = () The next topc that we want to dscuss here s powers of. Let s just take a look at what happens when we start lookng at varous powers of. 1 1 = = =- 1 = = =- =- 4 4 = = - 1 = 1 = = = = ( )() = = =- 1 = = =- =- ( ) () = = 1 = 1 = 1 Can you see the pattern? All powers f can be reduced down to one of four possble answers and they repeat every four powers. Ths can be a convenent fact to remember. We next need to address an ssue on dealng wth square roots of negatve numbers. From the secton on radcals we know that we can do the followng. 6= 36 = ( 4)( 9) = 4 9 = ( )( 3) = 6 In other words, we can break up products under a square root nto a product of square roots provded both numbers are postve. It turns out that we can actually do the same thng f one of the numbers s negatve. For nstance, ( )( ) 6 = - 36 = = = 3 = 6 However, f BOTH numbers are negatve ths won t work anymore as the followng shows. ( )( ) 6= 36 = = 3 = 6 =- 6 We can summarze ths up as a set of rules. If a and b are both postve numbers then, 007 Paul Dawkns 56

6 a b = ab - a b = -ab a - b = -ab ( )( ) -a -b -a -b Why s ths mportant enough to worry about? Consder the followng example. Example 4 Multply the followng and wrte the answer n standard form. ( )( ) Soluton If we where to multply ths out n ts present form we would get, ( )( ) = Now, f we were not beng careful we would probably combne the two roots n the fnal term nto one whch can t be done! So, there s a general rule of thumb n dealng wth square roots of negatve numbers. When faced wth them the frst thng that you should always do s convert them to complex number. If we follow ths rule we wll always get the correct answer. So, let s work ths problem the way t should be worked. ( )( ) = = = 6+ The rule of thumb gven n the prevous example s mportant enough to make agan. When faced wth square roots of negatve numbers the frst thng that you should do s convert them to complex numbers. There s one fnal topc that we need to touch on before leavng ths secton. As we noted back n the secton on radcals even though 9 = 3 there are n fact two numbers that we can square to get 9. We can square both 3 and -3. The same wll hold for square roots of negatve numbers. As we saw earler - 9 = 3. As wth square roots of postve numbers n ths case we are really askng what dd we square to get -9? Well t s easy enough to check that 3 s correct. ( ) 3 = 9 =- 9 However, that s not the only possblty. Consder the followng, - 3 = - 3 = 9 =- 9 and so f we square -3 we wll also get -9. So, when takng the square root of a negatve number there are really two numbers that we can square to get the number under the radcal. However, we wll ALWAYS take the postve number for the value of the square root just as we do wth the square root of postve numbers. 007 Paul Dawkns 57

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

Intro. Iterators. 1. Access

Intro. Iterators. 1. Access Intro Ths mornng I d lke to talk a lttle bt about s and s. We wll start out wth smlartes and dfferences, then we wll see how to draw them n envronment dagrams, and we wll fnsh wth some examples. Happy

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Brave New World Pseudocode Reference

Brave New World Pseudocode Reference Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Notes on Organizing Java Code: Packages, Visibility, and Scope

Notes on Organizing Java Code: Packages, Visibility, and Scope Notes on Organzng Java Code: Packages, Vsblty, and Scope CS 112 Wayne Snyder Java programmng n large measure s a process of defnng enttes (.e., packages, classes, methods, or felds) by name and then usng

More information

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky Improvng Low Densty Party Check Codes Over the Erasure Channel The Nelder Mead Downhll Smplex Method Scott Stransky Programmng n conjuncton wth: Bors Cukalovc 18.413 Fnal Project Sprng 2004 Page 1 Abstract

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Simulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010

Simulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010 Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement

More information

Pass by Reference vs. Pass by Value

Pass by Reference vs. Pass by Value Pass by Reference vs. Pass by Value Most methods are passed arguments when they are called. An argument may be a constant or a varable. For example, n the expresson Math.sqrt(33) the constant 33 s passed

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

MATHEMATICS FORM ONE SCHEME OF WORK 2004

MATHEMATICS FORM ONE SCHEME OF WORK 2004 MATHEMATICS FORM ONE SCHEME OF WORK 2004 WEEK TOPICS/SUBTOPICS LEARNING OBJECTIVES LEARNING OUTCOMES VALUES CREATIVE & CRITICAL THINKING 1 WHOLE NUMBER Students wll be able to: GENERICS 1 1.1 Concept of

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Lecture 3: Computer Arithmetic: Multiplication and Division

Lecture 3: Computer Arithmetic: Multiplication and Division 8-447 Lecture 3: Computer Arthmetc: Multplcaton and Dvson James C. Hoe Dept of ECE, CMU January 26, 29 S 9 L3- Announcements: Handout survey due Lab partner?? Read P&H Ch 3 Read IEEE 754-985 Handouts:

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

Lecture #15 Lecture Notes

Lecture #15 Lecture Notes Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Computer models of motion: Iterative calculations

Computer models of motion: Iterative calculations Computer models o moton: Iteratve calculatons OBJECTIVES In ths actvty you wll learn how to: Create 3D box objects Update the poston o an object teratvely (repeatedly) to anmate ts moton Update the momentum

More information

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

More information

CS221: Algorithms and Data Structures. Priority Queues and Heaps. Alan J. Hu (Borrowing slides from Steve Wolfman)

CS221: Algorithms and Data Structures. Priority Queues and Heaps. Alan J. Hu (Borrowing slides from Steve Wolfman) CS: Algorthms and Data Structures Prorty Queues and Heaps Alan J. Hu (Borrowng sldes from Steve Wolfman) Learnng Goals After ths unt, you should be able to: Provde examples of approprate applcatons for

More information

Optimizing Document Scoring for Query Retrieval

Optimizing Document Scoring for Query Retrieval Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

UNIT 2 : INEQUALITIES AND CONVEX SETS

UNIT 2 : INEQUALITIES AND CONVEX SETS UNT 2 : NEQUALTES AND CONVEX SETS ' Structure 2. ntroducton Objectves, nequaltes and ther Graphs Convex Sets and ther Geometry Noton of Convex Sets Extreme Ponts of Convex Set Hyper Planes and Half Spaces

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Report on On-line Graph Coloring

Report on On-line Graph Coloring 2003 Fall Semester Comp 670K Onlne Algorthm Report on LO Yuet Me (00086365) cndylo@ust.hk Abstract Onlne algorthm deals wth data that has no future nformaton. Lots of examples demonstrate that onlne algorthm

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

USING GRAPHING SKILLS

USING GRAPHING SKILLS Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp

More information

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law)

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law) Machne Learnng Support Vector Machnes (contans materal adapted from talks by Constantn F. Alfers & Ioanns Tsamardnos, and Martn Law) Bryan Pardo, Machne Learnng: EECS 349 Fall 2014 Support Vector Machnes

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Steve Setz Wnter 2009 Qucksort Qucksort uses a dvde and conquer strategy, but does not requre the O(N) extra space that MergeSort does. Here s the

More information

Algorithm To Convert A Decimal To A Fraction

Algorithm To Convert A Decimal To A Fraction Algorthm To Convert A ecmal To A Fracton by John Kennedy Mathematcs epartment Santa Monca College 1900 Pco Blvd. Santa Monca, CA 90405 jrkennedy6@gmal.com Except for ths comment explanng that t s blank

More information

Intra-Parametric Analysis of a Fuzzy MOLP

Intra-Parametric Analysis of a Fuzzy MOLP Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

Reading. 14. Subdivision curves. Recommended:

Reading. 14. Subdivision curves. Recommended: eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton

More information

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss.

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss. Today s Outlne Sortng Chapter 7 n Wess CSE 26 Data Structures Ruth Anderson Announcements Wrtten Homework #6 due Frday 2/26 at the begnnng of lecture Proect Code due Mon March 1 by 11pm Today s Topcs:

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

Math Homotopy Theory Additional notes

Math Homotopy Theory Additional notes Math 527 - Homotopy Theory Addtonal notes Martn Frankland February 4, 2013 The category Top s not Cartesan closed. problem. In these notes, we explan how to remedy that 1 Compactly generated spaces Ths

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references

More information

High level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization

High level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization What s a Computer Program? Descrpton of algorthms and data structures to acheve a specfc ojectve Could e done n any language, even a natural language lke Englsh Programmng language: A Standard notaton

More information

AP PHYSICS B 2008 SCORING GUIDELINES

AP PHYSICS B 2008 SCORING GUIDELINES AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Bran Curless Sprng 2008 Announcements (5/14/08) Homework due at begnnng of class on Frday. Secton tomorrow: Graded homeworks returned More dscusson

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

CS 534: Computer Vision Model Fitting

CS 534: Computer Vision Model Fitting CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust

More information

Priority queues and heaps Professors Clark F. Olson and Carol Zander

Priority queues and heaps Professors Clark F. Olson and Carol Zander Prorty queues and eaps Professors Clark F. Olson and Carol Zander Prorty queues A common abstract data type (ADT) n computer scence s te prorty queue. As you mgt expect from te name, eac tem n te prorty

More information

Edge Detection in Noisy Images Using the Support Vector Machines

Edge Detection in Noisy Images Using the Support Vector Machines Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona

More information

CE 221 Data Structures and Algorithms

CE 221 Data Structures and Algorithms CE 1 ata Structures and Algorthms Chapter 4: Trees BST Text: Read Wess, 4.3 Izmr Unversty of Economcs 1 The Search Tree AT Bnary Search Trees An mportant applcaton of bnary trees s n searchng. Let us assume

More information

Loop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation

Loop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation Loop Transformatons for Parallelsm & Localty Last week Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Scalar expanson for removng false dependences Loop nterchange Loop

More information

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005 Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed

More information

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) , VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

UB at GeoCLEF Department of Geography Abstract

UB at GeoCLEF Department of Geography   Abstract UB at GeoCLEF 2006 Mguel E. Ruz (1), Stuart Shapro (2), June Abbas (1), Slva B. Southwck (1) and Davd Mark (3) State Unversty of New York at Buffalo (1) Department of Lbrary and Informaton Studes (2) Department

More information

Chapter 4. Non-Uniform Offsetting and Hollowing by Using Biarcs Fitting for Rapid Prototyping Processes

Chapter 4. Non-Uniform Offsetting and Hollowing by Using Biarcs Fitting for Rapid Prototyping Processes Chapter 4 Non-Unform Offsettng and Hollowng by Usng Barcs Fttng for Rapd Prototypng Processes Ths chapter presents a new method of Non-Unform offsettng and usng barc fttngs to hollow out sold objects or

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.

BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc. [Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented

More information

Reducing Frame Rate for Object Tracking

Reducing Frame Rate for Object Tracking Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg

More information

Esc101 Lecture 1 st April, 2008 Generating Permutation

Esc101 Lecture 1 st April, 2008 Generating Permutation Esc101 Lecture 1 Aprl, 2008 Generatng Permutaton In ths class we wll look at a problem to wrte a program that takes as nput 1,2,...,N and prnts out all possble permutatons of the numbers 1,2,...,N. For

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Loop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont)

Loop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont) Loop Transformatons for Parallelsm & Localty Prevously Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Loop nterchange Loop transformatons and transformaton frameworks

More information

News. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example

News. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example Unversty of Brtsh Columba CPSC, Intro to Computaton Jan-Apr Tamara Munzner News Assgnment correctons to ASCIIArtste.java posted defntely read WebCT bboards Arrays Lecture, Tue Feb based on sldes by Kurt

More information

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR Judth Aronow Rchard Jarvnen Independent Consultant Dept of Math/Stat 559 Frost Wnona State Unversty Beaumont, TX 7776 Wnona, MN 55987 aronowju@hal.lamar.edu

More information

Electrical analysis of light-weight, triangular weave reflector antennas

Electrical analysis of light-weight, triangular weave reflector antennas Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

Insertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array

Insertion Sort. Divide and Conquer Sorting. Divide and Conquer. Mergesort. Mergesort Example. Auxiliary Array Inserton Sort Dvde and Conquer Sortng CSE 6 Data Structures Lecture 18 What f frst k elements of array are already sorted? 4, 7, 1, 5, 1, 16 We can shft the tal of the sorted elements lst down and then

More information

5.1 The ISR: Overvieui. chapter

5.1 The ISR: Overvieui. chapter chapter 5 The LC-3 n Chapter 4, we dscussed the basc components of a computer ts memory, ts processng unt, ncludng the assocated temporary storage (usually a set of regsters), nput and output devces, and

More information

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal

More information

RADIX-10 PARALLEL DECIMAL MULTIPLIER

RADIX-10 PARALLEL DECIMAL MULTIPLIER RADIX-10 PARALLEL DECIMAL MULTIPLIER 1 MRUNALINI E. INGLE & 2 TEJASWINI PANSE 1&2 Electroncs Engneerng, Yeshwantrao Chavan College of Engneerng, Nagpur, Inda E-mal : mrunalngle@gmal.com, tejaswn.deshmukh@gmal.com

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

$OJRULWKPV. (Feodor F. Dragan) Department of Computer Science Kent State University

$OJRULWKPV. (Feodor F. Dragan) Department of Computer Science Kent State University $GYDQF $OJRULWKPV (Feodor F. Dragan) Department of Computer Scence Kent State Unversty Advanced Algorthms, Feodor F. Dragan, Kent State Unversty Textbook: Thomas Cormen, Charles Lesterson, Ronald Rvest,

More information

THE THEORY OF REGIONALIZED VARIABLES

THE THEORY OF REGIONALIZED VARIABLES CHAPTER 4 THE THEORY OF REGIONALIZED VARIABLES 4.1 Introducton It s ponted out by Armstrong (1998 : 16) that Matheron (1963b), realzng the sgnfcance of the spatal aspect of geostatstcal data, coned the

More information

Fast exponentiation via prime finite field isomorphism

Fast exponentiation via prime finite field isomorphism Alexander Rostovtsev, St Petersburg State Polytechnc Unversty rostovtsev@sslstunevaru Fast exponentaton va prme fnte feld somorphsm Rasng of the fxed element of prme order group to arbtrary degree s the

More information

F Geometric Mean Graphs

F Geometric Mean Graphs Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 2 (December 2015), pp. 937-952 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) F Geometrc Mean Graphs A.

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

Introduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2

Introduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2 Introducton to Geometrcal Optcs - a D ra tracng Ecel model for sphercal mrrors - Part b George ungu - Ths s a tutoral eplanng the creaton of an eact D ra tracng model for both sphercal concave and sphercal

More information

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr) Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute

More information

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water.

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water. Purpose Theory REFRACTION a. To study the refracton of lght from plane surfaces. b. To determne the ndex of refracton for Acrylc and Water. When a ray of lght passes from one medum nto another one of dfferent

More information

Sorting: The Big Picture. The steps of QuickSort. QuickSort Example. QuickSort Example. QuickSort Example. Recursive Quicksort

Sorting: The Big Picture. The steps of QuickSort. QuickSort Example. QuickSort Example. QuickSort Example. Recursive Quicksort Sortng: The Bg Pcture Gven n comparable elements n an array, sort them n an ncreasng (or decreasng) order. Smple algorthms: O(n ) Inserton sort Selecton sort Bubble sort Shell sort Fancer algorthms: O(n

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Setup and Use. Version 3.7 2/1/2014

Setup and Use. Version 3.7 2/1/2014 Verson 3.7 2/1/2014 Setup and Use MaestroSoft, Inc. 1750 112th Avenue NE, Sute A200, Bellevue, WA 98004 425.688.0809 / 800.438.6498 Fax: 425.688.0999 www.maestrosoft.com Contents Text2Bd checklst 3 Preparng

More information

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

Newton-Raphson division module via truncated multipliers

Newton-Raphson division module via truncated multipliers Newton-Raphson dvson module va truncated multplers Alexandar Tzakov Department of Electrcal and Computer Engneerng Illnos Insttute of Technology Chcago,IL 60616, USA Abstract Reducton n area and power

More information

CHAPTER 10: ALGORITHM DESIGN TECHNIQUES

CHAPTER 10: ALGORITHM DESIGN TECHNIQUES CHAPTER 10: ALGORITHM DESIGN TECHNIQUES So far, we have been concerned wth the effcent mplementaton of algorthms. We have seen that when an algorthm s gven, the actual data structures need not be specfed.

More information

Wightman. Mobility. Quick Reference Guide THIS SPACE INTENTIONALLY LEFT BLANK

Wightman. Mobility. Quick Reference Guide THIS SPACE INTENTIONALLY LEFT BLANK Wghtman Moblty Quck Reference Gude THIS SPACE INTENTIONALLY LEFT BLANK WIGHTMAN MOBILITY BASICS How to Set Up Your Vocemal 1. On your phone s dal screen, press and hold 1 to access your vocemal. If your

More information

5.0 Quality Assurance

5.0 Quality Assurance 5.0 Dr. Fred Omega Garces Analytcal Chemstry 25 Natural Scence, Mramar College Bascs of s what we do to get the rght answer for our purpose QA s planned and refers to planned and systematc producton processes

More information

Why visualisation? IRDS: Visualization. Univariate data. Visualisations that we won t be interested in. Graphics provide little additional information

Why visualisation? IRDS: Visualization. Univariate data. Visualisations that we won t be interested in. Graphics provide little additional information Why vsualsaton? IRDS: Vsualzaton Charles Sutton Unversty of Ednburgh Goal : Have a data set that I want to understand. Ths s called exploratory data analyss. Today s lecture. Goal II: Want to dsplay data

More information

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert

More information