Katedra Informatiky Fakulta Matematiky, Fyziky a Informatiky Univerzita Komenského, Bratislava. Infinite series: Convergence tests (Bachelor thesis)

Size: px
Start display at page:

Download "Katedra Informatiky Fakulta Matematiky, Fyziky a Informatiky Univerzita Komenského, Bratislava. Infinite series: Convergence tests (Bachelor thesis)"

Transcription

1 Katedra Iformatiky Fakulta Matematiky, Fyziky a Iformatiky Uiverzita Komeského, Bratislava Ifiite series: Covergece tests Bachelor thesis) Frtišek Ďuriš Study programme: Iformatics Supervisor: doc. RNDr. Zbyěk Kubáček, CSc. Bratislava, 2009

2

3 I hereby proclaim, that I worked out this bachelor thesis aloe d usig oly quoted sources

4 Author would like to thk his supervisor doc. RNDr. Zbyěk Kubáček, CSc. for his kid help d guidce, Bc. Peter Lečéš for extesive TEXical help, Bc. Peter Beňák for my advices d Jozef Vojtek for lguage support. 4

5 Thou, ature, art my goddess; to thy laws my services are boud... W. Shakespear, Kig Lear motto of J. C. F. Gauss 5

6 Abstract The theory of ifiite series, old d well examied part of calculus, gives us a powerfull tool for solvig a wide rge of problems such as evaluatig importt mathematical costts e, π,...), values of trigoometric fuctios etc. This thesis itroduces the idea of ifiite series with some basic theorems ecessary i the followig chapters. The buildig o this, it derives some covergece tests, amely Raabe s test, Gauss test, Bertrd s test d Kummer s test. It also proves that there is o uiversal compariso test for all series. Keywords: Bertrd, Raabe. Ifiite series, covergece, divergece, Kummer, Gauss, Abstrakt Teóriekoečých radov, stará a dobre preskúmá oblasť matematickej alýzy, ám dáva silý ástroj a riešeie širokého spektra problémov, ako apríklad vyčísleie zámych matematických koštát e, π,...), hodôt trigoometrických fukcii atď. Táto práca uvádza základé defiície a teorémy z tejto oblasti, ktoré budú dôležité v ďalších kapitolách. Potom, stavajúc a týchto základoch, odvádza vybré kritéria vyšetrujúce kovergeciu ekoečých radov, meovite Raabeho test, Gaussov test, Bertrdov test a Kummerov test. Tiež dokážeme, že eexistuje uiverzále porovávacie kritérium, ktoré by vedelo rohodúť kovergeciu/divergeciu všetkých radov. Kľúčové slová: Gauss, Bertrd, Raabe. Nekoečé rady, kovergecia, divergecia, Kummer, 6

7 Cotets Abstract 6 Cotets 7 Prologue 8. The historical backgroud of ifiite series Elemetary defiitios d theorems Joseph Ludwig Raabe 2 2. Raabe s ratio test Joh Carl Friedrich Gauss 7 3. Gauss test Joseph Louis Frcois Bertrd Bertrd s test O the uiversal criterio 30 6 Erst Eduard Kummer Kummer s test The Last chapter 4 8 Epilogue 50 Refereces 52 Bibliography 55 7

8 Chapter Prologue. The historical backgroud of ifiite series The first metio of ifiite series dates back to tiquity. I those times, to uderstd that a sum with ifiite umber of summds could have a fiite result was importt philosophical challege. Later, the theory of ifiite series was thoroughly developed d used to work out my sigifict problems that eluded solutios with y other approach. Greek Archimedes c. 287 BC c. 22 BC) was first kow mathematici who applied ifiite series to calculate the area uder the arc of parabola usig the method of exhaustio. Several ceturies later, Madhava c.350 c.425) from Idia came up with the idea of expdig fuctios ito ifiite series. He laid dow the precursors of moder coceptio of power series, Taylor series, Maclauri series, ratioal approximatios of ifiite series d ifiite cotiued fractios. Next importt step was take by Scotish mathematici James Gregory ). Gregory uderstood the differetial d itegral, before it was formulated by Newto d Leibiz, o such a level that he was able to fid the ifiite series of arctget by usig his ow methods. Eve though this is the mai result attributed to him, he discovered ifiite series The method of exhaustio is a method of fidig the area of a shape by iscribig iside it a sequece of polygos whose areas coverge to the area of the cotaiig shape. If the sequece is correctly costructed, the differece i area betwee the th polygo d the cotaiig shape will become arbitrarily small as becomes large. 2 2 adopted from of exhaustio o 2d May,

9 for tget, sect, arcsect d some others as well. After Eglish Sir Isaac Newto ) d Germ Gottfried Wilhelm Leibiz ) idepedetly developed d published formal methods of calculus, they achieved my discoveries withi the theory of ifiite series. But eve such a geius as Leibiz was uable to fid the sum of iverse squares This problem was evetually resolved by Swiss Leohard Euler ). Euler solved this problem usig ifiite series d developed ew ways to mipulate them. With the discovery of ifiite series there also came a questio which of them actually yield a fiite result. Obviously, the result of series k= k cot be fiite so how we c decide the more obscure oes? First mathematici to study covergece was Madhava i 4th cetury. He developed a test 3, which was further developed by his followers i the Kerala school. I Europe the developmet of covergece tests was started by Germ Joh Carl Friedrich Gauss ), but the terms of covergece d divergece had bee itroduced log before by J. Gregory. Frech Augusti Louis Cauchy ) proved that a product of two coverget series does ot have to be coverget, d with him begis the discovery of effective criterios, although his methods led to a special applicable for a certai rge of series) rather th a geeral criterios. Ad the same applies for Swiss Joseph Ludwig Raabe ), British Augustus De Morg ), Frech Joseph Louis Frcois Bertrd ) d others. Developmet of geeral criterios beg with Germ Erst Eduard Kummer ) d later was carried o by Germ Karl Theodor Wilhelm Weierstrass85-897), Germ Ferdid Eisestei ) d my others. 5 3 Early form of itegral test of covergece; i Europe it was later developed by Maclauri d Cauchy d is sometimes kow as the Maclauri - Cauchy test. 4 4 adopted from test for covergece o 2d May, text sources o 2d May, 2009): series 9

10 .2 Elemetary defiitios d theorems I this sectio we itroduce the basic defiitios such as what exactly the ifiite series are, what the covergece d divergece terms me etc. Also we will get acquaited with a few importt theorems which we will refer to i upcomig chapters. All defiitios were adopted from [].) Defiitio.2.. Let { } =k k N {0}) be a sequece. The we call the symbol =k or a k + a k+ + a k ) a ifiite) series. Moreover, as we cosider the symbols =k d =m +k m idetical, because we c write all series =k i the form = +k, all defiitios d theorems will be formulated for the series i the form = b. Defiitio.2.2. Let { } = be a sequece. The umber a k, resp. S k = a + a a k, k N is called k-th term, resp. k-th partial sum of series =. Defiitio.2.3. We say that the series = coverges is coverget) if there exists a fiite lim S. We call the umber lim S the sum of series = d we deote it with the same symbol = or a + a ). If the lim S is ot fiite the we say that the series = diverges is diverget) d we c distiguish three cases: ) if lim S = + we say that the series = diverges to + ; 2) if lim S = we say that the series = diverges to ; 3) if lim S does ot exist we say that the series = oscillate. Defiitio.2.4. =k+ is called k-th remaider of series =. 0

11 Havig become familiar with the most importt defiitios we c proceed to the formulatio of some theorems. All theorems were adopted from [2].) Theorem.2.5. The series = coverges if d oly if the followig is true: ɛ > 0, N N, N > N, p N p < ɛ. Remark.2.6. This theorem is kow as Cauchy-Bolzo s theorem. Corollary.2.7. Necessary requiremet for covergece) If the series = coverges the lim = 0. Corollary.2.8. If the series = coverges the ɛ > 0, k N : < ɛ. =k+ Theorem.2.9. Let at most fiite cout of umbers fail + b + b, where > 0, b > 0, = 0,, 2,... The the covergece of series = b implies the covergece of series = d the divergece of series = implies the divergece of series = b. Remark.2.0. Kow as the secod compariso test. Theorem.2.. Let y = fx) be a cotiuous, o-egative, o-icreasig fuctio defied o the iterval [, ). Let = f) for =, 2,... d F x) be a primitive fuctio to the fuctio fx) o the iterval [, a], where a > is arbitrary real umber. The: ) if lim x F x) is fiite the the series = coverges. 2) if lim x F x) = + the the series = diverges. Remark.2.2. Kow as the itegral test of covergece.

12 Chapter 2 Joseph Ludwig Raabe Bor o May 5th, 80, i Brody, Galicia, J. L. Raabe was a Swiss mathematici. He beg to study mathematics i 820 at the Polytechicum i Viea, Austria. I autum 83, he moved to Zurich, where he became a professor of mathematics i 833. I 855, he became a professor at the ewly fouded Swiss Polytechicum. Joseph Ludwig Raabe died o Juary 22d, 859, i Zurich, Switzerld. His best kow success is Raabe s test of covergece. biography source from 2d May, 2009): Ludwig Raabe 2

13 2. Raabe s ratio test Let s cosider the followig sequece = p p > 0 =, 2,... The first questio we may ask is wether the series = sometimes called Riem s series) coverges. At first glce we see that the ecessary requiremet for covergece lim = 0 holds, so we eed somethig more sophisticated. Let s put this sequece ito the itegral criterio d see what happes. First, we defie fx) as a fuctio fx) = x p o the iterval [, ), which satisfies f) = =, 2, 3,...). We check that the fuctio fx) meets all the precoditios required for this test d we cotiue by fidig a primitive fuctio F x) to the fuctio fx). After some easy calculatios, we have the result: { l x if p = F x) = if p p)x p ) Now we check the lim x F x) { lim F x) = K < if p > x if p 0, ] d accordig to the itegral test theorem.2.) we c coclude that the series = { p coverges if p > diverges if p 0, ] 2.) a This was easy, but let s go o d see what we c lear about +. This might tell us somethig about how fast approaches zero as teds to ifiity. Equatio follows: + = + p +) p = ) p = + ) p = + p ) + O 2 We used here + ) p = + p ) x x + O x )

14 as asymptotic approximatio valid for x. After some adjustmets we get ) = p + O + ) where the expresio O ) becomes isigifict compared to p as teds to ifiity. Thus for sufficietly large we c write ) p + Put together with 2.), we c formulate the followig theorem, which is kow as Raabe s test: Theorem 2... Let = be a series with positive terms k N : a k > 0). If ) p >, N N, > N : p 2.3) + the the series = coverges. If N N, > N : the the series = diverges. Proof. Because we c write p = q + ɛ, q >, ɛ > 0 d with 2.3) for sufficietly large ): ) q + ɛ q + O + + q ) + + O 2 = ) 2.4) + ) + ) q = q +) q Ad sice q >, the series = q coverges. Accordig to the secod compariso test theorem.2.9) the series = coverges as well. O the other hd, adjustig 2.4) we get + + = + Theorem.2.9 implies the divergece of series =. 4

15 A more geeral versio of this test is usig the remaider O 2 ), which we formerly hid iside the costt p): Theorem Let = be a series with positive terms. Assume that there exists a real bouded sequece B such that for all = + p + + B 2 The the series = coverges if d oly if p >. The series diverges if d oly if p. Proof. Let s assume that the series = coverges d p =. Usig Bertrd s test theorem 4.., see chapter four) l ) + With l Hopital s rule we get l ) = B l l 0 as teds to ifiity) d therefore the series = diverges accordig to Bertrd s test). A cotradictio. If p < the for sufficietly large ) ) + = p + B < d accordig to the theorem 2.. the series = diverges. A cotradictio. Thus p >. Now we assume that p >. For sufficietly large + = + p + B 2 + q, q > We fiish the proof with theorem 2... Divergece follows by alogy. Theorem Let = be a series with positive terms. Assume that there exists a real bouded sequece B such that for all = + p + + B 2 The the series = diverges if d oly if p. 5

16 Proof. Let s cosider a diverget series = d let p >. The for sufficietly large we get ) + = p + B = q, q > which implies accordig to the theorem 2..), that the series = is actually coverget. A cotradictio hece p ). Now for the part where p. Let us assume that p =, for the divergece i case p < is obvious. By usig Bertrd s test we get ) ) l = l B ) ) 2 = B l Because B is bouded With l Hopital s rule we get l the series = diverges. B l l 0 as teds to ifiity) d therefore A example. We wt to determie the character of series 2 )!! = 2)!!. + = 2 )!! 2)!! 2+)!! 2+2)!! = ) ) = Accordig to Raabe s test, the series diverges. = 2 + 6

17 Chapter 3 Joh Carl Friedrich Gauss Bor o 30th April, 777, i Brauschweig, i the Electorate of Bruswick-Lueburg ow part of Lower Saxoy), Germy, J. C. F. Gauss was a Germ mathematici. He is kow as the priceps mathematicorum which mes the price of mathematicis. Ad verily this title suits him up to the hilt. He leart to read d cout by the age of three. At elemetary school whe give a task to sum the itegers from to 00, he produced a correct swer withi secods. His prodigious mid was quickly recogized by his teachers. Supported by his mother d by Duke of Bruswick - Wolfebuttel, who gave him scholarship, Gauss etered the Bruswick Collegium Carolium 792 to 795), d subsequetly he moved to the Uiversity of Gottige795 to 798). While studyig at uiversity, he rediscovered several importt theorems, icludig Bode s law, the biomial theorem, the law of quadratic reciprocity d the prime umber theorem. His breakthrough came i 796, whe he showed that y regular polygo with umber of sides that equals a Fermat prime c be costructed by compass d straightedge. This was the most importt advcemet i this field sice the time of Greek mathematicis. I 799, he proved fudametal theorem of algebra, that every ocostt sigle-variable polyomial over the complex umbers has at least oe root. I 80, he published a book Disquisitioes Arithmeticae Arithmetical Ivestigatios). I the same year, Gauss helped astroomer Giuseppe 7

18 Piazzi with observatio of a small plet Ceres by predictig its positio. This opeed him the doors to astroomy. His brillit work published as Theory of Celestial Movemet remais a corerstoe of astroomical computatio. I 807, he was appoited Professor of Astroomy d Director of the astroomical observatory i Gottige. I 88, while carrig out a geodesic survey of the state of Hover, he iveted a heliotrope, istrumet for measurig positios by reflectig sulight over great distces. He also discovered the possibility of o- Euclide geometries, though he ever published it because of fear of cotroversy. I 828, he published Disquisitioes geerales circa superficies curva, a work o differetial geometry. His famous theorem Theorema Egregium Remarkable Theorem) from this publicatio states i a broad lguage), that the curvature of a surface c be determied by measurig gles d distces o the surface oly. I his late years, Gauss collaborated with physicist professor Wilhelm Weber ivestigatig the theory of terrestrial magetism. By the year 840, he published three importt papers o this subject. Allgemeie Theorie des Erdmagetismus 839) showed that there c be oly two poles i the globe d specified the locatio of the magetic South pole. Together with Weber they discovered Kirchhoff s circuit laws i electricity d costructed electromagetic telegraph, which was able to sed a message over 5000 feet distce. But Gauss was far more iterested i magetic field of Earth. This lead to establishig a world-wide et of magetic observatio poits, foudig of The Magetischer Verei The Magetic Club) d publishig the atlas of geomagetism. He developed a method of measurig the horizotal itesity of the magetic field which was i use for more th oe hudred followig years d worked out the mathematical theory for separatig the ier core d crust) d outer magetospheric) sources of Earth s magetic field. After the year 837, his activity gradually decreased. I 849, he preseted his golde jubilee lecture, 50 years after his diploma. Joh Carl Friedrich Gauss died o 23rd February, 855, i Gottige, Hover ow part of Lower Saxoy, Germy), i his sleep. biography sources from 2d May, 2009): 8

19 3. Gauss test Theorem 3... Let = be a series with positive terms. Assumig that there exist a real umber p, a real umber r > d a real bouded sequece {B } = such that for all = + p + + B r The the series = coverges if d oly if p >. 2 Almost immediately we see that this test is somehow improved versio of Raabe s test. The oly differece is the umber r, which i the former test was explicitly set to 2. So Gauss test is more geeral, allowig us to decide covergece of more series. Not suprisigly the proof is very similar. Proof. If we start with p >, the for sufficietly large we have for egative terms of {B }) ) + or for positive terms of {B }) ) + Sice i both cases = p + B q, q, p) r = p + B r p ) q > + by usig Raabe s test theorem 2..) we c coclude that the series = coverges. As for the other part of equivalece, we will assume that the series = coverges d p =. By usig Bertrd s test theorem 4..) we get l ) ) = l B ) ) r = = B l r l r 2 adopted from o 2d May,

20 With l Hopital s rule we get l 0 as teds to ifiity) d therefore r the series = diverges. A cotradictio. If the series = coverges d p < the for sufficietly large ) ) = p + B + r < d accordig to Raabe s test theorem 2..) the series = diverges. A cotradictio. Thus p >. Theorem Let = be a series with positive terms. Assumig that there exist a real umber p, a real umber r > d a real bouded sequece B such that for all + = + p + B r 3.) The the series = diverges if d oly if p. Proof. Let = be a diverget series d p >. The for sufficietly large ) + = p + B > q, q > r d by Raabe s test theorem 2..) the series = coverges. A cotradictio hece p ). If p = the by usig Bertrd s test theorem 4..) ) ) l = B l + r ) With l Hopital s rule we get l 0 as teds to ifiity) d therefore r the series = diverges. If p < the for sufficietly large ) = p + B + r < We fiish the proof with Raabe s test. Now we show how to make a good use of mysterious lookig sequece B that is, how to reduce our thikig d use algorithmic procedure istead). Look agai at 3.). The best p c be obtaied by a limit ) p = lim )

21 Moreover, we set A = + p Now we try to fid umber r such that 3.3) r >, B = A r < 3.4) That is, {B } is a bouded sequece. If we succeed, we c apply Gauss test. 3 a > 0. A example. We wt to determie the character of the series = + = 2 )!! 2)!! 2+)!! 2+2)!! ) ) a = ) With x ) a = ) a 2 + = + ) p = + p ) x x + o x ) + a + ) 2 we get the fial result ) + a = + o ) ) + + a 2 + ) = a 2 + o) Accordig to Raabe s test, the series coverges whe a > 2 d diverges whe a < 2 but we get o iformatio whe a = 2. We try Gauss test : 2 )!! ) a, 2)!! First, we try to fid p see 3.2)) p = lim 2 )!! 2)!! 2+)!! 2+2)!! 2 = lim 2 ) ) = adopted from o 2d May,

22 Secod, we fid A see 3.3)) A = + p = A = 2 )!! 2)!! 2+)!! 2+2)!! 2 = ) Third, we try to fid r > such that B = A r is bouded see 3.4)) A = If we set r = 2 the Ad because p = the series = = 3 2 ) B = A 2 = < 2 )!! ) 2 2)!! diverges. Therefore, accordig to Gauss d Raabe s test, the series 2 )!! ) a = 2)!! coverges whe a > 2 d diverges whe a 2. 22

23 Chapter 4 Joseph Louis Frcois Bertrd Bor o th March, 822, i Paris, J. L. F Bertrd was a Frech mathematici. He studied at Ecole Polytechique d at the age of 6, he was awarded his first degree. I followig year, he received his doctorate for a thesis o thermodyamics. I 845, Bertrd cojectured that there is at least oe prime betwee d 2 2 for every > 3. The cojecture was later proved by Pafuty Lvovich Chebyshev d it is ow kow as Bertrd s postulate. He made major cotributio to the group theory d published works o differetial geometry, o probability theory Bertrd s paradox ) d o game theory Bertrd paradox). He was also famous for writig textbooks mostly for pupils at secodary schools but later also for more advced studets. I 856, Bertrd was appoited a professor at Ecole Polytechique d also a member of the Paris Academy of Scieces. I 862, he was made a professor at College de Frce. Joseph Louis Frcois Bertrd died o 5th April, 900, i Paris. 2 Bertrd s paradox cocers the probability that arbitrary chord of a circle is loger th a side of equilateral trigle iscribed i the circle. 2 biography sources from 2d May, 2009): Louis Frcois Bertrd 23

24 4. Bertrd s test We begi with the series =2 l ) p p > 0 4.) First we wt to kow for which p the give series coverges. To use itegral criterio theorem.2.), we defie fx) as a fuctio fx) = xl x) p o the iterval, ), which satisfies f) = =, 2, 3,...). The the primitive fuctio F x) is F x) = xl x) p dx = l x l x) p p After some adjustmets, we have the result { l l x if p = F x) = if p p)l x) p l x dx xl x) p+ d from that we c coclude owig to lim x F x)), that the series { coverges if p > l ) p 4.2) diverges if p 0, ] = Now we alyze + : + = l ) p = +)l +)) p = + ) ) l + ) p = + l ) l + l + ) l Usig little-o otatio for asymptotic approximatio whe x teds to ifiity: l + ) = ) x x + o x + ) p = + p x x + o ) x ) p 24

25 We put these two formulas ito + = + ) l + + o )) p = + l = = + ) + + ) + p = + + p l + o )) p l + o = l )) l + o = l ) l d after some adjustmets we get ) ) l = p + o ) + p 4.3) Puttig all this together, we c formulate ew test of covergece, which is kow as Bertrd s test: Theorem 4... Let = be a series with positive terms. If ) ) p >, N N, > N : l p 4.4) + the the series = coverges. If ) ) N N, > N : l 4.5) + the the series = diverges. Proof. Truly, 4.4) implies the existece of two costts q >, ɛ > 0 : q + ɛ = p Therefore we c write for sufficietly large ) ) ) l q + ɛ q + o) + 25

26 with 4.3) q ) l + o = l l ) q +)l +)) q Ad because q > the series = l ) is coverget see 4.2)). Accordig to the secod compariso test theorem.2.9) the series q = coverges as well. Now to prove divergece part. Note that we cot directly use equatio 4.3) here as we did i covergece part) because there c be hidde a egative fuctio f) i o l ), which would make Betrd s test uusable: From 4.3) l +) l +) From 4.5): Because the umbers = + + ) l + o = + l + l f) l +) l +) + l +) l +) + + l l c possibly be foud betwee the umbers ) d + + ) l we do ot have yet eough iformatio to compare + with l +) l +) What we eed is somethig like equatio 4.3) but without the o ) l term. l +) l +) = + ) l + ) l = + )l + l + ) ) = l = + ) l + + ) l + ) l 26 = ) l + l ) =

27 = + + l + ) l + l ) + l = = + + l + ) l + + ) = + l + l + ɛ) l Where ɛ) = + ) l + ) ) 2 + o 0 Oly ow we have eough iformatio to write l + + l + ɛ) l = l +) l +) The secod compariso test implies the divergece of the series = because the series l diverges see 4.2)). Remark The reader should start to suspect that whe we formulated Bertrd s test we commited a small imprecisio. Luckily for us, this imprecisio oly weakeed Bertrd s test but formulatio 4.5) is easy to use). As a result of this flaw, Bertrd s test cot decide covergece of his ow series 4.), as it will be show i Chapter 6. Aother versio of Bertrd s test c be costructed by usig the same Gauss improvemet as with Raabe s test: Theorem Let = be a series with positive terms. Assume that there exist a real umber p, a real umber r > d a real bouded sequece B such that for all + = + + p l + B l ) r The the series = coverges if d oly if p >. 27

28 Proof. Let = be a coverget series d p =. Accordig to the theorem see the Last chapter) l l l + ) ) ) = B l l l l l ) r l ) r usig l Hopital s rule) we get a cotradictio with the covergece we assumed. If p < the for sufficietly large ) ) B l = p + + l ) r < Accordig to the theorem 4.. we get a cotradictio with the covergece we assumed. Thus p >. If p > the = q l, q > with theorem 4.. we c coclude the covergece of series =. Divergece follows by alogy. Theorem Let = be a series with positive terms. Assume that there exist a real umber p, a real umber r > d a real bouded sequece B such that + = + + p l + The the series = diverges if d oly if p. B l ) r 4.6) Proof. Let p =. Accordig to the theorem l l l + + l + B l ) r = B l l l ) r ) 0 ) ) ) = usig l Hopital s rule) we c coclude the divergece of series =. If p < the for sufficietly large l ) + ) = p + B l ) r < 28

29 d the theorem 4.. prooves the divergece of series =. Now let = be a diverget series. The theorem 4.. with p > cotradicts the assumed divergece. Thus p. Remark Agai, we c use B d r to algorithmize the procedure. With p = lim l ) ), A = + + p l we c try to fid such r >, that B = A l ) r will be bouded. If we succeed, we c apply this test. 29

30 Chapter 5 O the uiversal criterio I the chapters 2, 3 d 4 we dealt with various compariso tests but for each oe of them we c costruct a series, that by usig this test we get o iformatio about the character of this series). That is, with tests that combie the secod compariso test theorem.2.9) d some series, whose covergece/divergece we kow. Now we ask whether there c be made some fixed criterio, which will resolve the character of all series with positive terms). Ufortuately, o such criterio c be costructed. The followig two theorems will explai why. Both theorems were adopted from [].) Theorem If = is a coverget series with positive terms the there exists a mootoous sequece {B } = such that lim B = d series = B coverges. Proof. Let = be a coverget series. From the corollary.2.7 we have lim = From the corollary.2.8 we have the umbers ξ, where {ξ } = is icreasig subsequece of atural umbers such that a k < k>ξ Let us costruct the umbers B this way: { 0 if [, ξ ) B = a k if [ξ k, ξ k+ ) 30

31 Now we oly eed to show that = B is coverget: ξ B = 0a k + ξ + a k = k= = k=ξ = Theorem If = is a diverget series with positive terms the there exists a mootoous sequece {B } = such that lim B = 0 d the series = B diverges. Proof. Let = be a diverget series. Here we cosider two cases. First, if lim sup ɛ > 0 the by leavig out all < ɛ 2 we do ot chge the character of series =. We fid all idices such that ɛ 2 d deote them with ξ k. Thus {ξ k } k= is icreasig subsequece of atural umbers. By {B } = we deote such a sequece that { 0 if ξ k for all k B = if = ξ k for some k k Moreover lim B = 0 d B = = ɛ 2 = Secod, if lim = 0 the from the divergece of = we have such icreasig subsequece {ξ } = of atural umbers that ξ + k=ξ a k >, ξ = If we let Ξ = ξ + k=ξ a k 3

32 the the series = Ξ diverges. We costruct the umbers B this way B = k for [ξ k, ξ k+ ), k =, 2,... Now we oly eed to show that the series = B is diverget: B = ξ + a k = Ξ = = = k=ξ = = We showed that to y coverget resp. diverget) series with positive terms we c costruct other positive series b, that coverges resp. diverges) much more slowly. I other words, for y positive coverget resp. diverget) series, there exists some positive series b that coverges resp. diverges) d the secod compariso test theorem.2.9) does ot hold or cot be used). This implies that there c be o ultimate compariso test that is, a test based o some fixed series) as all compariso tests are based o the theorem.2.9. But what we c tell from this is that for y positive series there c be costructed such a criterio based o some kow series b ), which will decide covergece/divergece of series, although fidig such series b might be difficult. Ad that is how Kummer s test works. Agai i plai lguage, for all positive series there exists a compariso test which c decide its character. But there is o such fixed compariso test that c decide the character of all positive series. 32

33 Chapter 6 Erst Eduard Kummer Bor o 29th Juary, 80, i Sorau, Brdeburg the part of Prussia, ow Germy), E. E. Kummer was a Germ mathematici. He etered the Uiversity of Halle with the itetio to study philosophy, but after he wo a prize for his mathematical essay i 83, he deciced for the mathematics. I the same year, he was awarded with a certificate eablig him to teach i schools d, for the stregth of his essay, also a doctorate. For te years he was teachig mathematics d physics at the Gymasium i Liegitz, ow Legica i Pold. I 836, he published a paper o hypergeometric series i Crelle s Joural d this work led Carl G. J. Jacobi together with Peter G. L. Dirichlet to correspod with him. I 839, Kummer was elected to the Berli Academy of Sciece. I 842, he was appoited a professor at the Uiversity of Breslau, ow Wroclaw i Pold, d he beg his reaserch i umber theory. I 855, Kummer became a professor of mathematics at the Uiversity of Berli. With Karl T. W. Weierstrass d Leopold Kroecker, Berli became oe of the leadig mathematical cetres i the world. Kummer helped to advce fuctio theory d umber theory. As for the fuctio theory, he exteded Gauss work o hypergeometric fuctios, givig developmets that are useful i the theory of differetial equatios. I umber theory, he itroduced the cocept of ideal umbers. This work was fudametal to Fermat s last theorem d also to the developmet of 33

34 the rig theory. I 857, he was awarded the Grd Prix by Paris Academy of Scieces for his work o Fermat s last theorem d soo after that, he was give a membership of the Paris Academy of Scieces. I 863, he was elected a Fellow of the Royal Society of Lodo. A year later, he published a work o Kummer surface, a surface that has 6 isolated coical double poits d 6 sigular tget ples. Erst Eduard Kummer died o 4 May, 893, i Berli. biography sources from 2d May, 2009): Kummer

35 6. Kummer s test Here comes probably the most powerful test for covergece, sice it applies to all series with positive terms. 2 : Theorem 6... Let = be a series with positive terms. The the series coverges if d oly if there exist a positive umber A, positive umbers p d umber N N such that for all > N p + p + A 6.) The series diverges if d oly if there exist positive umbers p such that p = d umber N N such that for all > N p + p ) Proof. First we prove the covergece. For the right-to-left implicatio, we adjust the equatio 6.) p p + + A+ With q = p A we c write q q ) Sice we kow the left side of upper iequality, we c costruct a sequece {B } = such that q q + + = B + +, B The sequece {q } = Therefore it has a limit is positive d decreasig follows from 6.3)). Thus the series B = = = 0 lim q < q a q q + + ) = q a lim q > 0 2 adopted from o 2d May,

36 coverges. Ad because B for all B = B I words, the series = B coverges d creates upper boud for the series =, therefore the series = must coverge as well. This is othig more the just a first compariso criteria. We showed that fidig umbers p is equally hard as fidig a coverget series b which creates upper boud for the series i the first compariso test. = For left-to-right implicatio, let p a be a positive umber. Accordig to the theorem 5.0.6, we assume the existece of positive mootoous sequece {B } = Now we shift the idex lim B = 6.4) B = p a + a B = + B + = p a = We defie the sequece {p } = this way where p + + = p + B + lim p = p a lim a k+ B k+ = 0 k= So, usig 6.4) we have for sufficietly large ) Ad fially p p + + = + B + A+ where A > 0 p hece the umbers p are foud. + p + A Remark The reader surely perceives that, i fact, the requiremet 6.4) is ot ecessary as y positive d mootoous sequece {B } with lim B = A is totally sufficiet where A is arbitrary costt). 36

37 For example, let be a coverget series. If we let B = for all d we costruct umbers p usig the terms from p + = p + Kummer s test will cofirm the covergece of series as would first compariso test). Now the divergece part. Proof. For the right-to-left implicatio, we have p = d from 6.2) we get + p p + We c coclude the divergece of series = by usig the secod compariso test theorem.2.9). To prove left-to-right implicatio whe = is diverget), we c assume, accordig to the theorem 5.0.7, the existece of positive d mootoous sequece B such that We have + lim B = 0 6.5) B = = B + B + p = B + Ad after some adjustmets, we get the right formula p It is ot difficult to see, that p the umbers we were lookig for. + p + 0 p p + = d p > 0 for all, thus we foud Remark Agai, the requiremet 6.5) is ot ecessary as y positive d o-icreasig sequece {B } with lim B = A > 0 will do the trick as it is the mootoy we are iterested i). 37

38 As i the previous remark, if is a diverget series d if we let B = for all the Kummer s test will cofirm the divergece. Remark To sum it up, Kummer s test is very powerful because it really works for all the series with positive terms. O the other hd, usig this test is equally difficult as usig the first d secod compariso test. The true stregth of this test therefore lies i the the umbers p. That is, the form of this test is a masterpiece, ot its cotets. To demostrate the power of Kummer s test, we show that Raabe s test d Bertrd s test are i fact its corollaries. As for Raabe s test, if we set p =, we get A > 0, N N, > N : + ) A + ) + A compare with 2.3) + for covergece d ) =, N N, > N : + ) 0 + for divergece. ) compare with 2.4) + We see, that what we c decide with Raabe s test, we c also decide with Kummer s test with p = ) d vice versa, thus they are equivalet. As for Kummer s versio of Bertrd s test, we set p = l d put it ito the left side of 6.): l + ) l + ) = + = l a + ) l + l + )) = + 38

39 = l l l l + ) = l + + ) +) = ) ɛ ) So l a ) ) + ) l + ) = l ɛ ) 6.6) + + Where ɛ) = l + ) +) = + ) )) o 2 = ) 2 +o We go back to 6.). With 6.6), if l the the series = is coverget. A > 0, N N, > N : ) ) + A + ɛ) 6.7) + Compare with Bertrd s test we worked out i the fourth chapter see 4.4)): If A > 0, N N, > N : ) ) l + A 6.8) + the the series = is coverget. Sice ɛ) c get arbitrarily small as teds to ifiity, we c hide it iside the positive costt A. Thus 6.7) d 6.8) are equivalet. Divergece is a bit differet d we will see that i this case, the tests are ot equivalet. That is, Kummer s test is slightly stroger. It is because ow we have zero as a sharp border, while the costt A from the previous case was quite flexible. With 6.2), 6.6) d p = l : if ) l =, N N, > N : 39

40 ) ) l ɛ) + the the series = is diverget. Compare with Bertrd s test see 4.5)): If ) ) N N, > N : l 0 + the the series = is diverget. test Now let s cosider the series =, where = l. Usig Bertrd s + ) l + ) l l +) l + ) l l = +) l Usig Kummer s test with p = l ) ) ) 0 + ) = ɛ) 0 false + ) l + ) l + ) l + ) 0 l 0 0 true There is ifiite umber of series that c be decided oly with Kummer s versio of Bertrd s test, but if we use limits, the tests are equivalet. 40

41 Chapter 7 The Last chapter I this chapter we will retur to Raabe s test to show how we got the sequece l ) i Bertrd s test d cosequetly, how to create limitless p umber of more d more powerful tests. We start by alyzig whe Raabe s test fails. If ) = + ξ), lim ξ) = 0+ + the there exists o such costt p from 2.3), or + ξ). This mes, that the give series coverges more slowly th the series, p >. But it does ot me that the series a p diverges. So what c we do? We eed a series whose covergece/divergece is kow, plus the series must coverge more slowly th. A good cdidate is p l ), sice p ɛ > 0, p > 0, 0, > 0 : ɛ < l ) p To prove use l Hopital s rule.) So, i chapter 4, we worked out Bertrd s test from the series l ). But eve this test fails whe p ) l + ) d the situatio repeats itself. Agai, because = + ξ), lim ξ) = 0+ ɛ > 0, p > 0, 0, > 0 : l ) ɛ < l l ) p 4

42 we c formulate ew, more powerful criterio. First, we have a closer look at the series Let fx) be a fuctio l l l ) p, p > 0 fx) = x l xl l x) p o the iterval e, ) which satisfies f) =. To use itegral criteria we fid the primitive fuctio F x) F x) = x l xl l x) p dx = l l x l l x) p p Thus the series F x) = { l l l ) p l l l x if p = p)l l x) p ) if p { coverges if p > l l x l l x) p+) l x diverges if p 0, ] x dx 7.) Expressig + l l l ) p +) l +)l l +)) p = + ) l + )l l + ))p l l l ) p = after some adjustmets very similar to 4.3)) = + + l + p l l l + o ) l l l 7.2) Now we c create ew test: Theorem Let = be a series with positive terms. If p >, N N, > N : ) ) ) l l l p 7.3) + 42

43 the the series = coverges. If l l l the the series = diverges. N N, > N : ) ) + ) 7.4) Proof. Followig 7.3) p > q > ɛ > 0 p = q + ɛ Thus, for sufficietly large l l l + d with 7.2) l + Because q > the series + ) ) ) q + ɛ q + o) q l l l + o l l l ) q +) l +)l l +)) q l l l ) q l l l coverges see 7.)). Accordig to the secod compariso test theorem.2.9) the series coverges as well. ) As for the divergece, we bump here ito the same problem as we did i Chapter 4 whe we were provig Bertrd s test theorem 4..). We will ot poit out the problem agai.) l l l +) l +) l l +) = + ) l + ) l l + ) l l l = = + ) l + ) l l + l + l l l )) = 43

44 )) + ) l + ) l l + l + l + ) l = = l l l = + ) l + ) l + ) + ) l + ) l + l + ) l l l l = = + + l + l l l + ς) l l l Where: ς) = l l + ) l + ) ) + + ) l + ) l + l + )) l With lim l l + ) l + ) ) = 0 we c simplify ς) ς) + ) l + ) l + l + )) = l = + ) l + l + )) l + l + )) l Ad pull out the biggest part Adjustig 7.4) we get ς) + ) l l + l + l )) l + l l l 0 44

45 Note that the terms we eglected i ς) were all positive so the followig iequality is correct l + l l l Ad fially + + l + l l l + ς) l l l + l l l +) l +) l l +) The secod compariso test implies the divergece of the series because the series diverges see 7.)). l l l Now we state oe of the most geeral versio of Bertrd s test. Lemma Let us deote k-th k N) iteratio of k 0 = ) with l k so l 3 = l l l ). The the series = l k = l l 2... l k l k ) p p > 0 k N coverges for p > d diverges for p. Proof. Let k be arbitrary atural umber. Let fx) be a fuctio o the iterval [, ) such that f) = l l 2... l k l k ) p To use itegral criteria we fid F x) F x) = x l x l 2 x... l k xl k x) dx = p = lk x l k x) p p l k x l k x) p+ l k x...l x)x dx 45

46 { l k x if p = F x) = if p p)l k x) p Now we examie lim x F x) d we c coclude that the series { coverges if p > = l l 2... l k l k ) p diverges if p 0, ] Lemma Usig the otatio from the previous lemma ɛ > 0, p N, k N, 0 : > 0 l k ) ɛ < l k+ ) p Proof. Use substitutio m = l k d l Hopital s rule. Lemma where + ) l + ) l 2 + )... l k + ) l l 2... l k = = + + l l...l k + ϑ) l...l k ϑ) 0 d lim ϑ) = 0 Theorem Let = be a series with positive memebers. If k N, p >, N N, > N : l k l k...l ) ) ) )... p + the the series = coverges. If l k l k the the series = diverges. k N, N N, > N :...l ) ) )... + ) Agai, the theorem c be made ito equivalece. We will use k l i = l )l 2 )...l k ) i= 46

47 Theorem Let = be a series with positive memebers. Assume that there exist atural umber k, a real umber p, umber r > d a real bouded sequece B d = + k + + j= j i= li + p k i= li + B k i= li ) l k ) r The the series = coverges if d oly if p >. Ad the series = diverges if d oly if p. Eve though it might seem that this is a powerful tool to test covergece, it actually c resolve oly a fractio of all possible series with positive terms). We will discuss the results i the chapter amed Epilogue. A example of the case whe the upper criterio fails. Let { } = be a mootoous positive sequece d = be a coverget series. Let {b } = be a sequece such that b = = a2, a N b = 0 for all other It is easy to see that the series = b coverges moreover, if we look at the idex d the term, it coverges very slowly). Now we use these two series to create ew series = Ξ : Ξ = b = 0 Ξ = b b 0 47

48 {Ξ } = : Obviously, the series = Ξ coverges. Now let s try to fid such least mootoous sequece {Υ } = that : Ξ Υ 48

49 It is ot very difficult to see that the sequece we are lookig for is Υ k = 2 k ) 2, 2 ], N Moreover, there are exactly 2 ) terms of value 2 i the sequece {Υ k } k=. Therefore k= Υ k = = 2 2 = = 7.5) Sequece {Υ } = is the least mootoous boud of sequece {Ξ } = d because the coverget series from the theorem is also mootoous, it cot be upper boud for this sequece otherwise it would boud a diverget series from 7.5)). 49

50 Chapter 8 Epilogue Ad that s all folks. We hope that this work has helped all readers to familiarize with the techiques used to determie covergece/divergece of ifiite series with positive terms oly, though). But also to realize, that these special tests Raabe, Gauss, Bertrd) will work oly for a small fractio of them d that o give series c lead to a uiversal compariso test. The example from the previous chapter c also serve as a proof that o mootoous series c lead to a test that will be able to decide all positive series. We are sure that the reader c see where the problem arises. Note that we are distiguishig betwee the terms uiversal criterio d geeral criterio. The former mes a test, based o a fixed series, that c uravel the character of all series with positive terms. That is, the usage of such criterio is straithforward. The latter mes a test e.g. first or secod compariso test) that works for all positive series, but it usually requires additioal d ofte big) effort to make a good use of it as a price for its geerality). Ad that is the situatio with Kummer s test, as this is a geeral test d it truly works for all the series with positive terms) there are. However, whe looked at more closely, we c see that it is just the first d secod compariso criterio i a smart disguise. So makig this test work fidig the umbers p ) c be very difficult. O the other hd, the beefit of this test is that we c see my special criterios as its corollaries. That is, the treasure is the treasure chest itself, ot its cotets, d this is probably the best result we c expect to get with geeral criterios. 50

51 Last but ot least, thk you for readig this work. We hope that someday, somewhere, it will come i hd. 5

52 Refereces to images Joseph Ludwig Raabe Ludwig Raabe.jpeg Joh Carl Friedrich Gauss Friedrich Gauss.jpg Joseph Louis Frcois Bertrd Erst Eduard Kummer 0 all images were retrieved o 2d May,

53 Refereces to biographies Joseph Ludwig Raabe Ludwig Raabe Joh Carl Friedrich Gauss Joseph Louis Frcois Bertrd Louis Frcois Bertrd Erst Eduard Kummer Kummer all texts were retrieved o 2d May,

54 Refereces to other sources of exhaustio test for covergece series 0 all texts were retrieved o 2d May,

55 Bibliography [] Zbyěk Kubáček d Já Valášek. Cvičeia z matematickej alýzy II. Uiverzita Komeského, 996. [2] Tibor Neubru d Jozef Vecko. Matematická alýza II. Uiverzita Komeského,

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

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

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

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

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

4.3. The Integral and Comparison Tests

4.3. The Integral and Comparison Tests 4.3. THE INTEGRAL AND COMPARISON TESTS 9 4.3. The Itegral ad Compariso Tests 4.3.. The Itegral Test. Suppose f is a cotiuous, positive, decreasig fuctio o [, ), ad let a = f(). The the covergece or divergece

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

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

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

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

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

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

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

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

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

INFINITE SERIES KEITH CONRAD

INFINITE SERIES KEITH CONRAD INFINITE SERIES KEITH CONRAD. Itroductio The two basic cocepts of calculus, differetiatio ad itegratio, are defied i terms of limits (Newto quotiets ad Riema sums). I additio to these is a third fudametal

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

Factors of sums of powers of binomial coefficients

Factors of sums of powers of binomial coefficients ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the

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

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

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

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

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

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

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

a 4 = 4 2 4 = 12. 2. Which of the following sequences converge to zero? n 2 (a) n 2 (b) 2 n x 2 x 2 + 1 = lim x n 2 + 1 = lim x

a 4 = 4 2 4 = 12. 2. Which of the following sequences converge to zero? n 2 (a) n 2 (b) 2 n x 2 x 2 + 1 = lim x n 2 + 1 = lim x 0 INFINITE SERIES 0. Sequeces Preiary Questios. What is a 4 for the sequece a? solutio Substitutig 4 i the expressio for a gives a 4 4 4.. Which of the followig sequeces coverge to zero? a b + solutio

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

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

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

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

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

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

On Formula to Compute Primes. and the n th Prime

On Formula to Compute Primes. and the n th Prime Applied Mathematical cieces, Vol., 0, o., 35-35 O Formula to Compute Primes ad the th Prime Issam Kaddoura Lebaese Iteratioal Uiversity Faculty of Arts ad cieces, Lebao issam.kaddoura@liu.edu.lb amih Abdul-Nabi

More information

MARTINGALES AND A BASIC APPLICATION

MARTINGALES AND A BASIC APPLICATION MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this

More information

Irreducible polynomials with consecutive zero coefficients

Irreducible polynomials with consecutive zero coefficients Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem

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

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

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

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means)

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means) CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:

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

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

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

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

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

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

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

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

Chapter 7: Confidence Interval and Sample Size

Chapter 7: Confidence Interval and Sample Size Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum

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

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

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

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

A Note on Sums of Greatest (Least) Prime Factors

A Note on Sums of Greatest (Least) Prime Factors It. J. Cotemp. Math. Scieces, Vol. 8, 203, o. 9, 423-432 HIKARI Ltd, www.m-hikari.com A Note o Sums of Greatest (Least Prime Factors Rafael Jakimczuk Divisio Matemática, Uiversidad Nacioal de Luá Bueos

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

Analysis Notes (only a draft, and the first one!)

Analysis Notes (only a draft, and the first one!) Aalysis Notes (oly a draft, ad the first oe!) Ali Nesi Mathematics Departmet Istabul Bilgi Uiversity Kuştepe Şişli Istabul Turkey aesi@bilgi.edu.tr Jue 22, 2004 2 Cotets 1 Prelimiaries 9 1.1 Biary Operatio...........................

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

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

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

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k.

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k. 18.409 A Algorithmist s Toolkit September 17, 009 Lecture 3 Lecturer: Joatha Keler Scribe: Adre Wibisoo 1 Outlie Today s lecture covers three mai parts: Courat-Fischer formula ad Rayleigh quotiets The

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

The Stable Marriage Problem

The Stable Marriage Problem The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,

More information

Chapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity

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

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

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

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

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

Research Article Sign Data Derivative Recovery

Research Article Sign Data Derivative Recovery Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 63070, 7 pages doi:0.540/0/63070 Research Article Sig Data Derivative Recovery L. M. Housto, G. A. Glass, ad A. D. Dymikov

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

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

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

THE HEIGHT OF q-binary SEARCH TREES

THE HEIGHT OF q-binary SEARCH TREES THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average

More information

THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE

THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE THE LEAST COMMON MULTIPLE OF A QUADRATIC SEQUENCE JAVIER CILLERUELO Abstract. We obtai, for ay irreducible quadratic olyomial f(x = ax 2 + bx + c, the asymtotic estimate log l.c.m. {f(1,..., f(} log. Whe

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

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

Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives

Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives Douglas A. Lapp Multiple Represetatios for Patter Exploratio with the Graphig Calculator ad Maipulatives To teach mathematics as a coected system of cocepts, we must have a shift i emphasis from a curriculum

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

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

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

Chair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics

Chair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics Chair for Network Architectures ad Services Istitute of Iformatics TU Müche Prof. Carle Network Security Chapter 2 Basics 2.4 Radom Number Geeratio for Cryptographic Protocols Motivatio It is crucial to

More information

An Efficient Polynomial Approximation of the Normal Distribution Function & Its Inverse Function

An Efficient Polynomial Approximation of the Normal Distribution Function & Its Inverse Function A Efficiet Polyomial Approximatio of the Normal Distributio Fuctio & Its Iverse Fuctio Wisto A. Richards, 1 Robi Atoie, * 1 Asho Sahai, ad 3 M. Raghuadh Acharya 1 Departmet of Mathematics & Computer Sciece;

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

Ekkehart Schlicht: Economic Surplus and Derived Demand

Ekkehart Schlicht: Economic Surplus and Derived Demand Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/

More information

Tradigms of Astundithi and Toyota

Tradigms of Astundithi and Toyota Tradig the radomess - Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore

More information

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff,

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff, NEW HIGH PERFORMNCE COMPUTTIONL METHODS FOR MORTGGES ND NNUITIES Yuri Shestopaloff, Geerally, mortgage ad auity equatios do ot have aalytical solutios for ukow iterest rate, which has to be foud usig umerical

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

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

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

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

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

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the

More information

EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND 1 OBTAINED WITH ENGEL SERIES

EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND 1 OBTAINED WITH ENGEL SERIES EGYPTIAN FRACTION EXPANSIONS FOR RATIONAL NUMBERS BETWEEN 0 AND OBTAINED WITH ENGEL SERIES ELVIA NIDIA GONZÁLEZ AND JULIA BERGNER, PHD DEPARTMENT OF MATHEMATICS Abstract. The aciet Egyptias epressed ratioal

More information

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2 74 (4 ) Chapter 4 Sequeces ad Series 4. SEQUENCES I this sectio Defiitio Fidig a Formula for the th Term The word sequece is a familiar word. We may speak of a sequece of evets or say that somethig is

More information

SEQUENCES AND SERIES CHAPTER

SEQUENCES AND SERIES CHAPTER CHAPTER SEQUENCES AND SERIES Whe the Grat family purchased a computer for $,200 o a istallmet pla, they agreed to pay $00 each moth util the cost of the computer plus iterest had bee paid The iterest each

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

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