Linear recurrence relations with constant coefficients

Size: px
Start display at page:

Download "Linear recurrence relations with constant coefficients"

Transcription

1 Liea ecuece elatios with costat coefficiets Recall that a liea ecuece elatio with costat coefficiets c 1, c 2,, c k c k of degee k ad with cotol tem F has the fom a c 1 a 1 + c 2 a c k a k + F k It follows fom the geeal ecusio theoem that fo evey stig of iitial values a, a 1,, a k 1 thee is exactly oe sequece {a } that satisfies the above ecuece elatio ad matches the give iitial coditios Cosequetly, if o iitial coditios ae imposed, thee will always be a ifiite set of solutios The theoems below will show how to fid all solutios to such a ecuece elatio povided the cotol tem is of the special fom F qs fo some polyomial q ad some costat s Moe geeal cotol tems ae discussed i advaced text books o diffeece equatios, such as A Itoductio to Diffeece Equatios by Sabe N Elaydi Spige Velag 1996 We fist coside the case whee F fo all The ecuece elatio is the called homogeeous Hee is the solutio fomula: Theoem 1 [The homogeeous case: fidig all solutios] Let c 1, c 2,, c k be costats with c k Coside the homogeeous liea ecuece elatio a c 1 a 1 + c 2 a c k a k k Let λ 1, λ 2,, λ t be the distict solutios to the chaacteistic equatio λ k c 1 λ k 1 + c 2 λ k c k The a sequece {a } is a solutio to if ad oly if it is of the fom a p 1 λ 1 + p 2 λ p t λ t with polyomials p 1, p 2,, p t such that the degee of p i is less tha the multiplicity of λ i fo all 1 i t Note: The coefficiets of the polyomials p i ae detemied by a, a 1, a k 1

2 Example The chaacteistic equatio to the ecuece elatio is give by which is equivalet to a a a a a a 5 λ 5 λ λ λ λ , 3λ 2 3 2λ Hece, λ 1 2/3 with multiplicity 3 ad λ 2 1/2 with multiplicity 2 ad the geeic solutio to is give by 2 a α + α 1 + α α 3 + α If oe is give iitial coditios, like a 1, a 1 2, a 2 3, a 3, ad a 4 6, the α,, α 4 ca be detemied by solvig the appopiate system of five liea equatios i five ukows esultig fom takig, 1,, 4 i the fomula defiig a Poof [of Theoem 1] Thee ae may diffeet ways to establish this esult The poof that we will sketch hee uses some kowledge of liea algeba We will illustate the geeal poof at a example of degee 5 Fist suppose that {a } is a solutio to a c 1 a 1 + c 2 a c 5 a 5 5 We ca ewite this i matix fom as follows: a a 1 a 2 a 3 a 4 c 1 c 2 c 3 c 4 c } {{ } A a 1 a 2 a 3 a 4 a 5 So, the matix A effects a shift i ay cosecutive stig of legth 5 take fom the ifiite sequece {a } This allows us to compute all values of {a } fom a, a 1,, a 4 by iteatig the matix A: 1 a +4 a +3 a +2 a +1 a A A A A }{{} -times a 4 a 3 a 2 a 1 a

3 Also otice that the matix A is ivetible, sice c 5 We theefoe ca ceate a histoy a 1, a 2, a 3, fo ou sequece {a } that follows the same ecuece elatio by simply applyig A 1 athe tha A Howeve, the fom that the matix A is cuetly i does ot allow fo coveiet iteatio A chage of basis will help us out hee Recall that if B is ay 5 5 ivetible matix, the the liea tasfomatio epeseted by A i the stadad basis, will be epeseted by J B 1 AB i the basis made up fom the colum vectos of B To fid a good choice fo B, we compute the eigevalues of the matix A by expadig acoss the fist ow: det A λi det c 1 λ c 2 c 3 c 4 c 5 1 λ 1 λ 1 λ 1 λ c 1 λ λ 4 c 2 λ 3 + c 3 λ 2 c 4 λ + c λ 5 c 1 λ 4 c 2 λ 3 c 3 λ 2 c 4 λ c 5 Obseve that the chaacteistic equatio of the matix A is exactly the chaacteistic equatio of the ecuece elatio Hece the ame! To stay withi a specific example, say this polyomial factos as follows: 1 5 λ 5 c 1 λ 4 c 2 λ 3 c 3 λ 2 c 4 λ c λ λ 1 2 λ λ 2 3 The Joda Nomal Fom Theoem ow implies that we ca choose B such that J has the fom λ 1 1 λ 1 J λ 2 1 λ 2 1 λ 2 I geeal, the Joda caoical fom has squae blocks o its diagoal ad zeos elsewhee Each squae block has oe eigevalue o its mai diagoal ad the umbe 1 o its subdiagoal ad is of size equal to the multiplicity of that eigevalue With a bief iductio you ca veify that the matix J ca be iteated quite easily 5: J J J J }{{} -times 1 λ 1 λ 1 λ 1 1 λ 2 λ 1 2 λ 1 2 λ 2 2 λ λ 2

4 Sice A BJB 1 that helps us iteate the matix A: A A A A A BJB 1 BJB 1 BJB 1 BJB 1 BJ B 1 Obseve that the expessio f ,! whe viewed as a polyomial i the vaiable, has degee Also, λ i λ i λ i Theefoe, if we substitute BJ B 1 fo A i 1 ad focus o its last ow we get 3 a α + α 1 λ 1 + α 2 + α 3 + α 4 2 λ 2 5, with some coefficiets α,, α 4 O the face of it, 3 holds oly fo 5 Howeve, we could have used the iitial coditios a 1, a 2,, a 5 i all of the above, athe tha a 4, a 3,, a, ad still aive at a fomula that is of the fom 3 Because such a shift i pespective substitutes + 5 fo i 3, which the tus ito aothe fomula of the same type Hece, fomula 3 actually holds fo all This shows that evey solutio is of the poposed fom Next, we pove that evey sequece of this fom is a solutio As pat of the above discussio we see that the five iitial coditios a,, a 4 of ay solutio {a } to detemie the five coefficiets α,, α 4 i the fomula fo {a }, ad they do so by a liea tasfomatio Moeove, this tasfomatio is ijective, because the α s detemie the etie sequece {a } Now, evey liea tasfomatio fom IR 5 to IR 5 which is ijective, must also be sujective Cosequetly, if we ae give ay umbes α,, α 4, whatsoeve, we ca fid umbes a,, a 4 by the ivese of this tasfomatio, ad the poduce a ifiite sequece {a } by applyig the ecuece ule whose fomula will be a α + α 1 λ 1 + α 2 + α 3 + α 4 2 λ 2 I shot, evey sequece of this fom is a solutio to

5 Next, we will examie how to fid oe paticula solutio to a o-homogeeous ecuece elatio of the specific type F qs : Theoem 2 [Fidig oe paticula solutio] Let costats c 1, c 2,, c k c k be give, alog with a costat s ad a polyomial q The the ecuece elatio a c 1 a 1 + c 2 a c k a k + qs k amog othe solutios always has a solutio of the fom b m ps, whee m is the multiplicity of s i the chaacteistic equatio λ k c 1 λ k 1 + c 2 λ k c k fo the coespodig homogeeous equatio ad p is a polyomial whose degee is less tha o equal to the degee of q Note: This paticula solutio caot accommodate abitay iitial coditios So, it might ot be the solutio you wee lookig fo I this case, you eed to fid all solutios usig Theoem 3 below ad pick the oe you actually wated Example Coside a 11a 1 39a a Sice the chaacteistic equatio to the coespodig homogeeous poblem is λ 3 11λ 2 39λ + 45, which is equivalet to λ 3 2 λ 5, we ca take b 2 β + β 1 + β β The coect coefficiets β,, β 3 ca be detemied by substitutig {b } back ito the oigial o-homogeeous equatio: b 11b 1 39b b This will poduce oe amog ifiitely may solutios To fid them all, icludig the oe you wated, use Theoem 3

6 Poof [of Theoem 2] Deote the degee i the vaiable of the polyomial q by d The fo evey, the fuctio fx q x is a polyomial of degee d i the vaiable x So, by the execise below, d+1 k d + 1 k Multiplyig though with s we see that d+1 k d + 1 k 1 k q k fo all }{{} fk 1 k s k }{{} s k k q ks } {{ } F k fo all But that meas that the sequece {F } {qs } satisfies a homogeeous liea ecuece elatio whose chaacteistic equatio is d+1 k d + 1 k s k λ d+1 k λ s d+1 Now, let {a } be ay solutio to the o-homogeeous ecuece elatio We claim that the sequece {a } also satisfies a homogeeous liea ecuece elatio whose chaacteistic equatio is λ k c 1 λ k 1 c 2 λ k 2 c k λ s d+1 I ode to keep the fomalism to a miimum, we will illustate this with a example that coveys the geeal idea: Suppose ou oigial o-homogeeous ecuece elatio eads a 7a s That is, k 1, c 1 7, d 1, q 3 + 2, ad we ae claimig that {a } satisfies a homogeeous liea ecuece elatio with chaacteistic equatio λ 7λ s 2 The above says that the sequece {F } satisfies a ecuece elatio coespodig to the chaacteistic equatio λ 2 2sλ + s 2 λ s 2, amely F 2sF 1 + s 2 F 2

7 The a 7a 1 F 2sa 1 7a 2 2sF 1 s 2 a 2 7a 3 s 2 F 2 a 7 + 2sa 1 +s 2 7a 2 7a 3, which has chaacteistic equatio λ 7λ s 2 This is best see by substitutig λ i fo a i ow by ow ad factoig out λ 3 λ 7 Hece the claim The we ca apply Theoem 1 to fid a fomula fo {a } We get a α 7 + α 1 + α 2 s if s 7 ad m, O the othe had, a α + α 1 + α 2 2 s if s 7 ad m 1 d α 7 solves the homogeeous pat of ou ecuece, by Theoem 1 Hece, by Theoem 3 below, b a d is agai a solutio to Notice that i both cases, this solutio {b } has the fom m ps, with degee p equal to the degee of q amely 1 The same agumet holds fo ay o-homogeeous liea ecuece elatio of the fom Theoem 3 [The geeal fomula] Let costats c 1, c 2,, c k c k ad a cotol sequece F : IN IR be give Suppose you have foud oe paticula solutio {b } to the o-homogeeous ecuece elatio a c 1 a 1 + c 2 a c k a k + F IN The the geeic solutio {a } to is of the fom a d + b, whee {d } is the geeic solutio to the coespodig homogeeous ecuece elatio a c 1 a 1 + c 2 a c k a k IN

8 Poof If a is of the stated fom, the it satisfies : a d + b c 1 d 1 + c 2 d c k d k + c1 b 1 + c 2 b c k b k + F c 1 d 1 + b 1 + c 2 d 2 + b c k d k + b k + F c 1 a 1 + c 2 a c k a k + F Covesely, if {a } is ay solutio to the the sequece {d } defied by d a b satisfies the homogeeous ecuece elatio : d a b c 1 a 1 + c 2 a c k a k + F c 1 b 1 + c 2 b c k b k + F c 1 a 1 b 1 + c 2 a 2 b c k a k b k c 1 d 1 + c 2 d c k d k Hece, a d + b has the poposed fom

9 Execise a Show that 1 c fo all > ad all costats c [Simply facto out c] b Show that 1 1 fo all 1 [This is the Chaipeso Idetity] c Show that fo evey polyomial fx of degee d we have wheeve d < 1 f, [Use iductio o d Pat a seves as the basic step d Fo the iductive step, assume that d 1 ad that the statemet is tue fo all polyomials of degee less tha d To show that the statemet also holds fo polyomials of degee d, you oly eed to veify that 1 d fo all d < Make use of Pat b ad the iductive hypothesis: 1 d 1 d d d d 1 ]

Two degree of freedom systems. Equations of motion for forced vibration Free vibration analysis of an undamped system

Two degree of freedom systems. Equations of motion for forced vibration Free vibration analysis of an undamped system wo degee of feedom systems Equatios of motio fo foced vibatio Fee vibatio aalysis of a udamped system Itoductio Systems that equie two idepedet d coodiates to descibe thei motio ae called two degee of

More information

Periodic Review Probabilistic Multi-Item Inventory System with Zero Lead Time under Constraints and Varying Order Cost

Periodic Review Probabilistic Multi-Item Inventory System with Zero Lead Time under Constraints and Varying Order Cost Ameica Joual of Applied Scieces (8: 3-7, 005 ISS 546-939 005 Sciece Publicatios Peiodic Review Pobabilistic Multi-Item Ivetoy System with Zeo Lead Time ude Costaits ad Vayig Ode Cost Hala A. Fegay Lectue

More information

The dinner table problem: the rectangular case

The dinner table problem: the rectangular case The ie table poblem: the ectagula case axiv:math/009v [mathco] Jul 00 Itouctio Robeto Tauaso Dipatimeto i Matematica Uivesità i Roma To Vegata 00 Roma, Italy tauaso@matuiomait Decembe, 0 Assume that people

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

Money Math for Teens. Introduction to Earning Interest: 11th and 12th Grades Version

Money Math for Teens. Introduction to Earning Interest: 11th and 12th Grades Version Moey Math fo Tees Itoductio to Eaig Iteest: 11th ad 12th Gades Vesio This Moey Math fo Tees lesso is pat of a seies ceated by Geeatio Moey, a multimedia fiacial liteacy iitiative of the FINRA Ivesto Educatio

More information

Understanding Financial Management: A Practical Guide Guideline Answers to the Concept Check Questions

Understanding Financial Management: A Practical Guide Guideline Answers to the Concept Check Questions Udestadig Fiacial Maagemet: A Pactical Guide Guidelie Aswes to the Cocept Check Questios Chapte 4 The Time Value of Moey Cocept Check 4.. What is the meaig of the tems isk-etu tadeoff ad time value of

More information

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required.

S. Tanny MAT 344 Spring 1999. be the minimum number of moves required. S. Tay MAT 344 Sprig 999 Recurrece Relatios Tower of Haoi Let T be the miimum umber of moves required. T 0 = 0, T = 7 Iitial Coditios * T = T + $ T is a sequece (f. o itegers). Solve for T? * is a recurrece,

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix

FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

Learning Objectives. Chapter 2 Pricing of Bonds. Future Value (FV)

Learning Objectives. Chapter 2 Pricing of Bonds. Future Value (FV) Leaig Objectives Chapte 2 Picig of Bods time value of moey Calculate the pice of a bod estimate the expected cash flows detemie the yield to discout Bod pice chages evesely with the yield 2-1 2-2 Leaig

More information

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen THE PRINCIPLE OF THE ACTIVE JC SCATTERER Seppo Uoukaie VTT Buildig ad Tapot Ai Hadlig Techology ad Acoutic P. O. Bo 1803, FIN 02044 VTT, Filad Seppo.Uoukaie@vtt.fi ABSTRACT The piciple of fomulatig the

More information

Network Theorems - J. R. Lucas. Z(jω) = jω L

Network Theorems - J. R. Lucas. Z(jω) = jω L Netwo Theoems - J.. Lucas The fudametal laws that gove electic cicuits ae the Ohm s Law ad the Kichoff s Laws. Ohm s Law Ohm s Law states that the voltage vt acoss a esisto is diectly ootioal to the cuet

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Saturated and weakly saturated hypergraphs

Saturated and weakly saturated hypergraphs Satuated and weakly satuated hypegaphs Algebaic Methods in Combinatoics, Lectues 6-7 Satuated hypegaphs Recall the following Definition. A family A P([n]) is said to be an antichain if we neve have A B

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Derivation of Annuity and Perpetuity Formulae. A. Present Value of an Annuity (Deferred Payment or Ordinary Annuity)

Derivation of Annuity and Perpetuity Formulae. A. Present Value of an Annuity (Deferred Payment or Ordinary Annuity) Aity Deivatios 4/4/ Deivatio of Aity ad Pepetity Fomlae A. Peset Vale of a Aity (Defeed Paymet o Odiay Aity 3 4 We have i the show i the lecte otes ad i ompodi ad Discoti that the peset vale of a set of

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

On the Optimality and Interconnection of Valiant Load-Balancing Networks

On the Optimality and Interconnection of Valiant Load-Balancing Networks O the Optimality ad Itecoectio of Valiat Load-Balacig Netwoks Moshe Babaioff ad Joh Chuag School of Ifomatio Uivesity of Califoia at Bekeley Bekeley, Califoia 94720 4600 {moshe,chuag}@sims.bekeley.edu

More information

Annuities and loan. repayments. Syllabus reference Financial mathematics 5 Annuities and loan. repayments

Annuities and loan. repayments. Syllabus reference Financial mathematics 5 Annuities and loan. repayments 8 8A Futue value of a auity 8B Peset value of a auity 8C Futue ad peset value tables 8D Loa epaymets Auities ad loa epaymets Syllabus efeece Fiacial mathematics 5 Auities ad loa epaymets Supeauatio (othewise

More information

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series

Our aim is to show that under reasonable assumptions a given 2π-periodic function f can be represented as convergent series 8 Fourier Series Our aim is to show that uder reasoable assumptios a give -periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series

More information

Solutions to Exercises Chapter 4: Recurrence relations and generating functions

Solutions to Exercises Chapter 4: Recurrence relations and generating functions Solutios to Exercises Chapter 4: Recurrece relatios ad geeratig fuctios 1 (a) There are seatig positios arraged i a lie. Prove that the umber of ways of choosig a subset of these positios, with o two chose

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

Negotiation Programs

Negotiation Programs Negotiatio Pogams Javie Espaza 1 ad Jög Desel 2 1 Fakultät fü Ifomatik, Techische Uivesität Müche, Gemay espaza@tum.de 2 Fakultät fü Mathematik ud Ifomatik, FeUivesität i Hage, Gemay joeg.desel@feui-hage.de

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

The Binomial Distribution

The Binomial Distribution The Binomial Distibution A. It would be vey tedious if, evey time we had a slightly diffeent poblem, we had to detemine the pobability distibutions fom scatch. Luckily, thee ae enough similaities between

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Cosider a legth- sequece x[ with a -poit DFT X[ where Represet the idices ad as +, +, Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Usig these

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

Heat (or Diffusion) equation in 1D*

Heat (or Diffusion) equation in 1D* Heat (or Diffusio) equatio i D* Derivatio of the D heat equatio Separatio of variables (refresher) Worked eamples *Kreysig, 8 th Ed, Sectios.4b Physical assumptios We cosider temperature i a log thi wire

More information

Breakeven Holding Periods for Tax Advantaged Savings Accounts with Early Withdrawal Penalties

Breakeven Holding Periods for Tax Advantaged Savings Accounts with Early Withdrawal Penalties Beakeve Holdig Peiods fo Tax Advataged Savigs Accouts with Ealy Withdawal Pealties Stephe M. Hoa Depatmet of Fiace St. Boavetue Uivesity St. Boavetue, New Yok 4778 Phoe: 76-375-209 Fax: 76-375-29 e-mail:

More information

OPTIMALLY EFFICIENT MULTI AUTHORITY SECRET BALLOT E-ELECTION SCHEME

OPTIMALLY EFFICIENT MULTI AUTHORITY SECRET BALLOT E-ELECTION SCHEME OPTIMALLY EFFICIENT MULTI AUTHORITY SECRET BALLOT E-ELECTION SCHEME G. Aja Babu, 2 D. M. Padmavathamma Lectue i Compute Sciece, S.V. Ats College fo Me, Tiupati, Idia 2 Head, Depatmet of Compute Applicatio.

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Paper SD-07. Key words: upper tolerance limit, macros, order statistics, sample size, confidence, coverage, binomial

Paper SD-07. Key words: upper tolerance limit, macros, order statistics, sample size, confidence, coverage, binomial SESUG 212 Pae SD-7 Samle Size Detemiatio fo a Noaametic Ue Toleace Limit fo ay Ode Statistic D. Deis Beal, Sciece Alicatios Iteatioal Cooatio, Oak Ridge, Teessee ABSTRACT A oaametic ue toleace limit (UTL)

More information

How To Solve The Homewor Problem Beautifully

How To Solve The Homewor Problem Beautifully Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

Logistic Regression, AdaBoost and Bregman Distances

Logistic Regression, AdaBoost and Bregman Distances A exteded abstact of this joual submissio appeaed ipoceedigs of the Thiteeth Aual Cofeece o ComputatioalLeaig Theoy, 2000 Logistic Regessio, Adaoost ad egma istaces Michael Collis AT&T Labs Reseach Shao

More information

580.439 Course Notes: Nonlinear Dynamics and Hodgkin-Huxley Equations

580.439 Course Notes: Nonlinear Dynamics and Hodgkin-Huxley Equations 58.439 Couse Notes: Noliea Dyamics ad Hodgki-Huxley Equatios Readig: Hille (3 d ed.), chapts 2,3; Koch ad Segev (2 d ed.), chapt 7 (by Rizel ad Emetout). Fo uthe eadig, S.H. Stogatz, Noliea Dyamics ad

More information

Lecture 5: Span, linear independence, bases, and dimension

Lecture 5: Span, linear independence, bases, and dimension Lecture 5: Spa, liear idepedece, bases, ad dimesio Travis Schedler Thurs, Sep 23, 2010 (versio: 9/21 9:55 PM) 1 Motivatio Motivatio To uderstad what it meas that R has dimesio oe, R 2 dimesio 2, etc.;

More information

Chapter 04.05 System of Equations

Chapter 04.05 System of Equations hpter 04.05 System of Equtios After redig th chpter, you should be ble to:. setup simulteous lier equtios i mtrix form d vice-vers,. uderstd the cocept of the iverse of mtrix, 3. kow the differece betwee

More information

16. Mean Square Estimation

16. Mean Square Estimation 6 Me Sque stmto Gve some fomto tht s elted to uow qutty of teest the poblem s to obt good estmte fo the uow tems of the obseved dt Suppose epeset sequece of dom vbles bout whom oe set of obsevtos e vlble

More information

Figure 2. So it is very likely that the Babylonians attributed 60 units to each side of the hexagon. Its resulting perimeter would then be 360!

Figure 2. So it is very likely that the Babylonians attributed 60 units to each side of the hexagon. Its resulting perimeter would then be 360! 1. What ae angles? Last time, we looked at how the Geeks intepeted measument of lengths. Howeve, as fascinated as they wee with geomety, thee was a shape that was much moe enticing than any othe : the

More information

AP Calculus BC 2003 Scoring Guidelines Form B

AP Calculus BC 2003 Scoring Guidelines Form B AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

ANNUITIES SOFTWARE ASSIGNMENT TABLE OF CONTENTS... 1 ANNUITIES SOFTWARE ASSIGNMENT... 2 WHAT IS AN ANNUITY?... 2 EXAMPLE 1... 2 QUESTIONS...

ANNUITIES SOFTWARE ASSIGNMENT TABLE OF CONTENTS... 1 ANNUITIES SOFTWARE ASSIGNMENT... 2 WHAT IS AN ANNUITY?... 2 EXAMPLE 1... 2 QUESTIONS... ANNUITIES SOFTWARE ASSIGNMENT TABLE OF CONTENTS ANNUITIES SOFTWARE ASSIGNMENT TABLE OF CONTENTS... 1 ANNUITIES SOFTWARE ASSIGNMENT... WHAT IS AN ANNUITY?... EXAMPLE 1... QUESTIONS... EXAMPLE BRANDON S

More information

Symmetric polynomials and partitions Eugene Mukhin

Symmetric polynomials and partitions Eugene Mukhin Symmetic polynomials and patitions Eugene Mukhin. Symmetic polynomials.. Definition. We will conside polynomials in n vaiables x,..., x n and use the shotcut p(x) instead of p(x,..., x n ). A pemutation

More information

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>

Factoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu> (March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 1

More information

Finance Practice Problems

Finance Practice Problems Iteest Fiace Pactice Poblems Iteest is the cost of boowig moey. A iteest ate is the cost stated as a pecet of the amout boowed pe peiod of time, usually oe yea. The pevailig maket ate is composed of: 1.

More information

A Recursive Formula for Moments of a Binomial Distribution

A Recursive Formula for Moments of a Binomial Distribution A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,

More information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

AP Calculus AB 2006 Scoring Guidelines Form B

AP Calculus AB 2006 Scoring Guidelines Form B AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a ot-for-profit membership associatio whose missio is to coect studets to college success

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Notes on Power System Load Flow Analysis using an Excel Workbook

Notes on Power System Load Flow Analysis using an Excel Workbook Notes o owe System Load Flow Aalysis usig a Excel Woboo Abstact These otes descibe the featues of a MS-Excel Woboo which illustates fou methods of powe system load flow aalysis. Iteative techiques ae epeseted

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4

GCE Further Mathematics (6360) Further Pure Unit 2 (MFP2) Textbook. Version: 1.4 GCE Further Mathematics (660) Further Pure Uit (MFP) Tetbook Versio: 4 MFP Tetbook A-level Further Mathematics 660 Further Pure : Cotets Chapter : Comple umbers 4 Itroductio 5 The geeral comple umber 5

More information

UNIT CIRCLE TRIGONOMETRY

UNIT CIRCLE TRIGONOMETRY UNIT CIRCLE TRIGONOMETRY The Unit Cicle is the cicle centeed at the oigin with adius unit (hence, the unit cicle. The equation of this cicle is + =. A diagam of the unit cicle is shown below: + = - - -

More information

Notes on exponential generating functions and structures.

Notes on exponential generating functions and structures. Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

How Euler Did It. In a more modern treatment, Hardy and Wright [H+W] state this same theorem as. n n+ is perfect.

How Euler Did It. In a more modern treatment, Hardy and Wright [H+W] state this same theorem as. n n+ is perfect. Amicable umbers November 005 How Euler Did It by Ed Sadifer Six is a special umber. It is divisible by, ad 3, ad, i what at first looks like a strage coicidece, 6 = + + 3. The umber 8 shares this remarkable

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

5.3. Generalized Permutations and Combinations

5.3. Generalized Permutations and Combinations 53 GENERALIZED PERMUTATIONS AND COMBINATIONS 73 53 Geeralized Permutatios ad Combiatios 53 Permutatios with Repeated Elemets Assume that we have a alphabet with letters ad we wat to write all possible

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Euler, Goldbach and Exact Values of Trigonometric Functions. Hieu D. Nguyen and Elizabeth Volz Rowan University Glassboro, NJ 08028 nguyen@rowan.

Euler, Goldbach and Exact Values of Trigonometric Functions. Hieu D. Nguyen and Elizabeth Volz Rowan University Glassboro, NJ 08028 nguyen@rowan. I. Itoductio Eule, Goldbach ad Exact Values of Tigoometic Fuctios Hieu D. Nguye ad Elizabeth Volz Rowa Uivesity Glassboo, NJ 0808 guye@owa.edu Jauay 9, 04 I a potio of a lette set to Chistia Goldbach o

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

2. TRIGONOMETRIC FUNCTIONS OF GENERAL ANGLES

2. TRIGONOMETRIC FUNCTIONS OF GENERAL ANGLES . TRIGONOMETRIC FUNCTIONS OF GENERAL ANGLES In ode to etend the definitions of the si tigonometic functions to geneal angles, we shall make use of the following ideas: In a Catesian coodinate sstem, an

More information

The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero.

The LCOE is defined as the energy price ($ per unit of energy output) for which the Net Present Value of the investment is zero. Poject Decision Metics: Levelized Cost of Enegy (LCOE) Let s etun to ou wind powe and natual gas powe plant example fom ealie in this lesson. Suppose that both powe plants wee selling electicity into the

More information

Lecture 2: Karger s Min Cut Algorithm

Lecture 2: Karger s Min Cut Algorithm priceto uiv. F 3 cos 5: Advaced Algorithm Desig Lecture : Karger s Mi Cut Algorithm Lecturer: Sajeev Arora Scribe:Sajeev Today s topic is simple but gorgeous: Karger s mi cut algorithm ad its extesio.

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

Section 8.3 : De Moivre s Theorem and Applications

Section 8.3 : De Moivre s Theorem and Applications The Sectio 8 : De Moivre s Theorem ad Applicatios Let z 1 ad z be complex umbers, where z 1 = r 1, z = r, arg(z 1 ) = θ 1, arg(z ) = θ z 1 = r 1 (cos θ 1 + i si θ 1 ) z = r (cos θ + i si θ ) ad z 1 z =

More information

The Binomial Multi- Section Transformer

The Binomial Multi- Section Transformer 4/15/21 The Bioial Multisectio Matchig Trasforer.doc 1/17 The Bioial Multi- Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where: Γ ( ω

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork Solutios to Selected Problems I: Patter Classificatio by Duda, Hart, Stork Joh L. Weatherwax February 4, 008 Problem Solutios Chapter Bayesia Decisio Theory Problem radomized rules Part a: Let Rx be the

More information

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients 652 (12-26) Chapter 12 Sequeces ad Series 12.5 BINOMIAL EXPANSIONS I this sectio Some Examples Otaiig the Coefficiets The Biomial Theorem I Chapter 5 you leared how to square a iomial. I this sectio you

More information

Week 3-4: Permutations and Combinations

Week 3-4: Permutations and Combinations Week 3-4: Pemutations and Combinations Febuay 24, 2016 1 Two Counting Pinciples Addition Pinciple Let S 1, S 2,, S m be disjoint subsets of a finite set S If S S 1 S 2 S m, then S S 1 + S 2 + + S m Multiplication

More information

Estimating Surface Normals in Noisy Point Cloud Data

Estimating Surface Normals in Noisy Point Cloud Data Estiatig Suface Noals i Noisy Poit Cloud Data Niloy J. Mita Stafod Gaphics Laboatoy Stafod Uivesity CA, 94305 iloy@stafod.edu A Nguye Stafod Gaphics Laboatoy Stafod Uivesity CA, 94305 aguye@cs.stafod.edu

More information

Maximum Likelihood Estimators.

Maximum Likelihood Estimators. Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio

More information

Solving equations. Pre-test. Warm-up

Solving equations. Pre-test. Warm-up Solvig equatios 8 Pre-test Warm-up We ca thik of a algebraic equatio as beig like a set of scales. The two sides of the equatio are equal, so the scales are balaced. If we add somethig to oe side of the

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Hypergeometric Distributions

Hypergeometric Distributions 7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you

More information

Chapter 4: Matrix Norms

Chapter 4: Matrix Norms EE448/58 Vesion.0 John Stensby Chate 4: Matix Noms The analysis of matix-based algoithms often equies use of matix noms. These algoithms need a way to quantify the "size" of a matix o the "distance" between

More information