Möbius Inversion Formula. Multiplicative Functions Zvezdelina Stankova-Frenkel UC Berkeley

Size: px
Start display at page:

Download "Möbius Inversion Formula. Multiplicative Functions Zvezdelina Stankova-Frenkel UC Berkeley"

Transcription

1 BERKELEY MATH CIRCLE Möbus Inverson Forula. Multplcatve Functons Zvezdelna Stanova-Frenel UC Bereley Contents 1. Multplcatve Functons 1 2. Drchlet Product and Möbus Inverson 3 3. War-up Probles 5 4. The Euler Functon ϕ(n) 6 5. War-up Probles 7 6. Applcatons to Probles 7 7. Probles wth [x] and Multplcatve Functons 8 8. Hnts and Solutons to Selected Probles 9 1. Multplcatve Functons Defnton 1. A functon f : N C s sad to be arthetc. In ths secton we dscuss the set M of ultplcatve functons, whch s a subset of the set A of arthetc functons. Why ths subset s so specal can be explaned by the fact that t s usually easer to calculate explct forulas for ultplcatve functons, and not so easy to do ths for arbtrary arthetc functons. 1 Defnton 2. An arthetc functon f(n) : N C s ultplcatve f for any relatvely pre n, N: f(n) f()f(n). Exaples. Let n N. Defne functons τ, σ, π : N N as follows: τ(n) the nuber of all natural dvsors of n #{d > 0 }; σ(n) the su of all natural dvsors of n d; π(n) the product of all natural dvsors of n d. As we shall see below, τ and σ are ultplcatve functons, whle π s not. Fro now on we shall wrte the pre decoposton of n N as n p α 1 1 pα 2 2 pα r r for dstnct pres p and α > 1. It s easy to verfy the followng propertes of ultplcatve functons: 1 But on a deeper level, M s specal partly because t s closed under the Drchlet product n A. For ths one has to wat untl after Secton 2.

2 Lea 1. If f s ultplcatve, then ether f(1) 1 or f 0. Further, f f 1, f 2,..., f are ultplcatve, then the usual product f 1 f 2 f n s also ultplcatve. Usng the pre decoposton of n N, derve the followng representatons of τ, σ and π. Lea 2. The functons τ(n), σ(n) and π(n) are gven by the forulas: r r p α +1 1 τ(n) (α + 1), σ(n) p 1, π(n) n 1 2 τ(n). Conclude that τ and σ are ultplcatve, whle π s not. Exaples. The followng are further exaples of well-nown ultplcatve functons. µ(n), the Möbus functon; e(n) δ 1,n, the Drchlet dentty n A; I(n) 1 for all n N; d(n) n for all n N. Tang the su-functons of these, we obtan the relatons: S µ e, S e I, S I τ, and S d σ. These exaples suggest that the su-functon s ultplcatve, provded the orgnal functon s too. In fact, Theore 1. f(n) s ultplcatve ff ts su-functon S f (n) s ultplcatve. Proof: Let f(n) be ultplcatve, and let x, y N such that (x, y) 1. Further, let x 1, x 2,..., x and y 1, y 2,..., y be all dvsors of x and y, respectvely. Then (x, y j ) 1, and {x y j },j are all dvsors of xy. S f (x) S f (y) f(x ) Hence S f (n) s ultplcatve. j1 f(y j ),j f(x )f(y j ),j f(x y j ) S f (xy). Conversely, f S f (n) s ultplcatve, then let n 1, n 2 N such that (n 1, n 2 ) 1 and n n 1 n 2. We wll prove by nducton on n that f(n 1 n 2 ) f(n 1 )f(n 2 ). The stateent s trval for n 1: f(1) S f (1)( 1 or 0.) Assue that t s true for all 1 2 < n. Then for our n 1, n 2 we have: S f (n 1 n 2 ) d n f(d 1 d 2 ) d n f(d 1 d 2 ) + f(n 1 n 2 ) d n f(d 1 )f(d 2 ) + f(n 1 n 2 ). d 1 d 2 <n On the other hand, S f (n 1 )S f (n 2 ) f(d 1 ) f(d 2 ) d 1 n 1 d 2 n 2 d n d 1 d 2 <n d 1 d 2 <n f(d 1 )f(d 2 ) + f(n 1 )f(n 2 ). Snce S f (n 1 n 2 ) S f (n 1 )S f (n 2 ), equatng the above two expressons and cancelng approprately, we obtan f(n 1 n 2 ) f(n 1 )f(n 2 ). Ths copletes the nducton step, and shows that f(n) s ndeed ultplcatve. 2

3 Corollary 1. The su-functon S f (n) of a ultplcatve functon f(n) s gven by the forula: r ( S f (n) 1 + f(p ) + f(p 2 ) + + f(p α ) ). 2. Drchlet Product and Möbus Inverson Consder the set A of all arthetc functons, and defne the Drchlet product of f, g A as: f g(n) f(d 1 )f(d 2 ). d 1 d 2 n Note that f g s also arthetc, and that the product s coutatve, and assocatve: (f g) h(n) f (g h)(n) f g h(n) f(d 1 )f(d 2 )f(d 3 ). d 1 d 2 d 3 n Wth respect to the Drchlet product, the dentty eleent e A s easy to fnd: { 1 f n 1 e(n) 0 f n > 1. Indeed, chec that e f f e f for any f A. If we were worng wth the usual product of functons f g(n) f(n)g(n), then the dentty eleent would have been I(n) 1 for all n N, because I f f for all functons f. In the set A, however, I s certanly not the dentty eleent, but has the nce property of transforng each functon f nto ts so-called sufuncton S f. Defne S f (n) f(d), to be the su-functon of f A, and note that S f s also arthetc. Chec that: I f f I S f for all f A. It s nterestng to fnd the Drchlet nverse g A of ths sple functon I n our set A,.e. such that g I I g e. Ths naturally leads to the ntroducton of the so-called Möbus functon µ: Defnton 2. The Möbus functon µ : N C s defned by 1 f n 1 µ(n) 0 f n s not square-free ( 1) r f n p 1 p 2 p r, p j dstnct pres. Lea 3. The Drchlet nverse of I s the Möbus functon µ A. Proof: The lea eans that µ I e,.e. { 1 f n 1 (1) µ(d) 0 f n > 1. 3

4 Ths follows easly fro the defnton of µ. Indeed, for n p α 1 1 pα 2 2 pαr r > 1 we have r µ(d) µ(d) µ(1) + µ(p 1 p ).,d sq.free 1 1 < < r Here the last su runs over all square-free dvsors of n. For cobnatoral reasons, r ( ) r µ(d) ( 1) (1 1) r 0. 0 To suarze, we have shown that the Drchlet nverse of the functon I(n) s the Möbus functon µ(n): µ I I µ e. Unfortunately, not all arthetc functons have Drchlet nverses n A. In fact, show that Lea 4. An arthetc functon f has a Drchlet nverse n M ff f(1) 0. 2 The rght noton to replace nverses n A turns out to be su-functons, whch s the dea of the Möbus nverson theore. Theore 2 (Möbus nverson theore). Any arthetc functon f(n) can be expressed n ters of ts su-functon S f (n) f(d) as f(n) µ(d)s f ( n d ). Proof: The stateent s nothng else but the Drchlet product f µ S f n A: µ S f µ (I f) (µ I) f e f f. Note: Here s a ore tradtonal proof of the Möbus Inverson Forula: µ(d)s f ( n d ) µ( n d )S f (d) µ( n d ) f(d 1 ) d 1 d f(d 1 ) µ( n d ) f(d 1 ) µ( ), d 2 d 1 n d 1 d 1 n d 2 where n/d 1, d 2 d/d 1. By property (1), the second su s non-zero only when 1,.e. d 1 n, and hence the whole expresson equals f(n). Ths proof, however, hdes the product structure of A under the Drchlet product. It s natural to ass the opposte queston: gven the Möbus relaton f(n) µ(d)g( n d ) for two arthetc functons f and g, can we deduce that g s the su-functon S f of f? The answer should be obvous fro the product structure of A: f µ g I f I (µ g) S f (I µ)g e g g, and ndeed, g s the su-functon of f. If you prefer ore tradtonal proofs, you can recover one fro the above by recallng that ultplyng f by I s the sae as tang the su-functon of f. 2 For those who care about group theory nterpretatons, ths ples that M s not a group wth the Drchlet product, but the subset M of t consstng of all arthetc functons f wth f(1) 0 s a group. 4

5 Corollary 2. For two arthetc functons f and g we have the followng equvalence: g(n) f(d) f(n) µ(d)g( n d ). Ths ncdentally shows that every arthetc functon g s the su-functon of another arthetc functon f: sply defne f by the second forula as f µ g. Notce that Theore 1 does not use the Drchlet product at all, but nstead t hdes a ore general fact. For an arthetc functon f, ts su-functon s S f I f, and by Möbus nverson, f µ S f. Thus, Theore 1 sply proves that f f M, then the product of the two ultplcatve functons I and f s also ultplcatve, and that f S f M, then the product of the two ultplcatve functons S f and µ s also ultplcatve. Ths naturally suggest the ore general Theore 3. The set M of ultplcatve functons s closed under the Drchlet product: f, g M f g M. Proof: Let f, g M, (a, b) 1, and h f g. Then h(a)h(b) [ f g(a) ] [f g(b) ] d 1 a d ab d 1 a,d 2 b f(d 1 )f(d 2 )g( a d 1 )g( b d 2 ) f(d 1 )g( a d 1 ) d 2 b f(d)g( ab ) f g(ab) h(ab). d Thus, h s also ultplcatve, so that f g M. d 1 a,d 2 b f(d 2 )g( b d 2 ) f(d 1 d 2 )g( ab d 1 d 2 ) For the group theory fans: fnd the explct Drchlet nverse of any ultplcatve functon f 0, and conclude Lea 5. The set M {f 0} s a group under the Drchlet product. 3. War-up Probles Proble 1. Fnd, n N such that they have no pre dvsors other than 2 and 3, (, n) 18, τ() 21, and τ(n) 10. Proble 2. Fnd n N such that one of the followng s satsfed: π(n) , π(n) , π(n) 13 31, or τ(n) Proble 3. Defne σ (n) d. Thus, σ 0 (n) τ(n) and σ 1 (n) σ(n). Prove that σ (n) s ultplcatve for all N, and fnd a forula for t. Proble 4. Show that τ(n) s odd ff n s a perfect square, and that σ(n) s odd ff n s a perfect square or twce a perfect square. Proble 5. If f(n) s ultplcatve, f 0, then show r ( µ(d)f(d) 1 f(p ) ). 5

6 Proble 6. If f(n) s ultplcatve, then show that h(n) µ( n d )f(d) s also ultplcatve. Conclude that every ultplcatve functon s the su-functon of another ultplcatve functon. 4. The Euler Functon ϕ(n) Defnton 4. The Euler functon ϕ(n) assgns to each natural nuber n the nuber of the ntegers d between 1 and n whch are relatvely pre to n (set ϕ(1) 1.) Lea 6. ϕ(n) s ultplcatve. Proof: Consder the table of all ntegers between 1 and ab, where a, b N, (a, b) a 1 a a + 1 a + 2 a + 2a 1 2a 2a + 1 2a + 2 2a + 3a 1 3a ja + 1 ja + 2 ja + (j + 1)a 1 (j + 1)a (b 2)a + 1 (b 2)a + 2 (b 2)a + (b 1)a 1 (b 1)a (b 1)a + 1 (b 1)a + 2 (b 1)a + ba 1 ba Note that f ja + s relatvely pre wth a, then all nubers n ts (-th) colun wll be relatvely pre wth a. In the frst row there are exactly ϕ(a) nubers relatvely pre wth a, so ther ϕ(a) coluns wll be all nubers n the table whch are relatvely pre wth a. As for b, each colun s a syste of reanders odulo b because (a, b) 1 (chec that n each colun the b ntegers are dstnct odulo b.) Thus, n each colun we have precsely ϕ(b) relatvely pre ntegers to b. Hence, the total nuber of eleents n ths table relatvely pre to ab s exactly ϕ(ab) ϕ(a)ϕ(b), and ϕ(n) s ultplcatve. We can use ultplcatvty of the Euler functon to derve a forula for ϕ(n). For a pre p, all nubers d [1, p ] such that (d, p ) 1 are exactly those d dvsble by p: d p c wth c 1, 2,..., p 1,.e. p 1 n nuber. Hence ϕ(p ) p p 1. Therefore, r r r ϕ(n) ϕ(p α ) (p α p α 1 ) p α (1 1 p ) n(1 1 p 1 ) (1 1 p r ). Lea 7. The su-functon S ϕ (n) of the Euler functon ϕ(n) satsfes: ϕ(n) n. Proof: The ultplcatvty of the Euler functon ϕ(n) ples that S ϕ (n) s ultplcatve, so that S ϕ (n) S ϕ (p α 1 1 ) S ϕ(p αr r ). Ths reduces the proble to a pre power n p a, whch together wth ϕ(p j ) p j p j 1 easly ples S ϕ (p a ) p a. Rear: We can prove the lea also drectly by consderng the set of fractons { 1 n, 2 n,..., n 1 n, n } { a1, a 2,..., a n 1, a } n n b 1 b 2 b n 1 b n 6

7 where (a, b ) 1. The set D of all denonators {b } s precsely the set of all dvsors of n. Gven a dvsor d of n, d appears n the set D exactly ϕ(d) tes: n a a a b d d n, where (a, d) 1 and 1 a d. Countng the eleents n D n two dfferent ways ples n ϕ(d). We can use ths Rear to show agan ultplcatvty of the Euler functon: ndeed, ths follows fro the fact that ts su-functon S ϕ (n) n s tself obvously ultplcatve. 5. War-up Probles Proble 7. Show that ϕ(n ) n 1 ϕ(n) for all n, N. Proble 8. Solve the followng equatons: ϕ(2 x 5 y ) 80. ϕ(n) 12. ϕ(n) 2n/3. ϕ(n) n/2. ϕ(ϕ(n)) Proble 9. Show that ϕ(n)ϕ() ϕ((n, ))ϕ([n, ]); ϕ(n)ϕ((n, )) (n, )ϕ(n)ϕ(). 6. Applcatons to Probles Proble 10. For two sequences of coplex nubers {a 0, a 1,..., a n,...} and {b 0, b 1,..., b n,...} show that the followng relatons are equvalent: a n b for all n b n 0 Proble 11. Solve the equaton ϕ(σ(2 n )) 2 n. ( 1) +n a for all n. Proble 12. Let f(x) Z[x] and let ψ(n) be the nuber of values f(j), j 1, 2,..., n, such that (f(j), n) 1. Show that ψ(n) s ultplcatve and that ψ(p t ) p t 1 ψ(p). Conclude that 0 ψ(n) p n ψ(p)/p. 7

8 Proble 13. Fnd closed expressons for the followng sus: µ(d)τ(d) µ(d)ϕ(d) µ(d)σ(d) µ 2 (d)ϕ 2 (d) µ(d)τ( n d ) µ(d) ϕ(d) µ(d)σ( n d ) µ( n d )ln d µ(d) d (t,n)1 1 t<n t 1 Proble 14. Consder the functon ζ(s), the so-called Reann zeta-functon. ns n1 It converges for s > 1. In fact, one can extend t to an analytc functon over the whole coplex plane except s 1. The faous Reann Conjecture clas that all zeros of ζ(s) n the strp 0 Re s 1 le on the lne Re s 1/2. For ζ(s) show the followng foral denttes: ζ(s) p ζ(s) p s ζ(s) 2 τ(n) n s n1 µ(n) n s ζ(s)ζ(s 1) n1 Proble 15. [Please, do not dscuss ths proble - t s on Monthly Contest 8.] Suppose that we are gven nfntely any tcets, each wth one natural nuber on t. For any n N, the nuber of tcets on whch dvsors of n are wrtten s exactly n. For exaple, the dvsors of 6, {1, 2, 3, 6}, are wrtten n soe varaton on 6 tcets, and no other tcet has these nubers wrtten on t. Prove that any nuber n N s wrtten on at least one tcet. n1 σ(n) n s Proble 16. Let f(n) : N N be ultplcatve and strctly ncreasng. If f(2) 2, then f(n) n for all n. 7. Probles wth [x] and Multplcatve Functons [ n ] Proble 17. Prove that τ() and σ() [ n ]. 8

9 q 1 [ ] p Proble 18. Prove that for (p, q) 1: q p 1 [ ] q. p Proble 19. Prove that for n N: 1 <n ϕ( + 1) n 1 { } ( 1)! + 1. Proble 20. Let S(, n) { Z (od ) + n (od ) }. Fnd 8. Hnts and Solutons to Selected Probles Hnt 10. Defne a functon a : N C by a 0 f n 1 a(n) a r f n p 1 p 2 p r 0 f n not sq.free. S(,n) ϕ(). Soluton 11. We have ϕ(2 n+1 1) 2 n. If 2 n+1 1 1, then n 0 and ϕ(1) 1. Otherwse, 2 n+1 1 p α 1 1 pα 2 2 pαr r > 1 wth all p s odd. Fro a prevous forula, ϕ(p α 1 1 pα 2 2 pαr r ) 2 n p α p α p αr 1 r (p 1 1)(p 2 1) (p r 1). Therefore, all α 1, 2 n (p 1 1)(p 2 1) (p r 1), and all p 2 s + 1 for soe s 1. It s easy to see that f s has an odd dvsor > 1, then 2 s + 1 wll factor, ang p non-pre. Hence p 2 2q + 1 for soe q 0. We have reduced the proble to fndng all sets of pres p of the above type such that p 1 p 2 p r n+1 2(p 1 1)(p 2 1) (p r 1). Order the pres p 1 < p 2 < < p r. Chec consecutvely va odulo approprate powers of 2, that, f they exst, the sallest pres ust be: p 1 3, p 2 5, p 3 17, p However, f p 5 also exsts, then p , whch s not pre. Hence, r 4, and n 1, 2, 3, 4. Hnt 12. Modfy the table proof for ultplcatvty of the Euler functon ϕ(n). Hnt 13, lnd. Note that lnd s not ultplcatve. Consder the functon (n) defned by: (n) ln p f n s a power of a pre p, and (n) 0 otherwse, and use Möbus nverson. Soluton 16. We have f(1) 1 and f(2) 2. Let f(3) 3 + for N {0}. f(6) f(2) f(3) f(5) f(10) f(9) f(18) f(15) But f(15) f(3) f(5) (3 + ) (5 + ) Hence, 2 0,.e. 0 and f(3) 3. Now assue by nducton on that f(s) s for s 1, 2,..., 2 1, where 2. Then f(4 2) 2f(2 1) 4 2. Snce f(2 1) 2 1 and the functon s 9

10 strctly ncreasng, f(s) s for all s [2 1, 4 2]. In partcular, f(2) 2 and f(2 + 1) (4 2 > 2 + 1). Ths copletes the nducton step. [ n ] [ ] { n 1 1 when n, Soluton 17a. Set B n, for 1, 2,..., n. Then B n, 0 when n, and τ(n) B n,. Sung up, we obtan τ(),1 1 [ ] B, 1 n 1 n 1 1 [ ] Soluton 17b. Fro the prevous proble, B n, σ(),1 1 [ ] [ p Hnt 18. Use the dentty q (p 1)(q 1) both sus equal. 2 B, ] 1 n 1 n 1 1 [ ] ([ ] 1 n n [ ]) 1 [ ]. { when n, 0 when n, ( [ ] Hnt 19. Both sdes count the nuber of pres p n. Hnt 20. The gven su equals ([ ] + n [ [ ] l ϕ(). Further, where Σ(l) 1 n n and σ(n) [ ]) 1 [ ]. B n,. [ ] (q )p p 1 for 1, 2,..., q 1, and show that q ] [ n ] ) ϕ() Σ( + n) Σ() Σ(n), Σ(l) 1 q l d q ( l + 1 ϕ(d) 2 ). 10

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2) MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total

More information

We are now ready to answer the question: What are the possible cardinalities for finite fields?

We are now ready to answer the question: What are the possible cardinalities for finite fields? Chapter 3 Fnte felds We have seen, n the prevous chapters, some examples of fnte felds. For example, the resdue class rng Z/pZ (when p s a prme) forms a feld wth p elements whch may be dentfed wth the

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2016. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

Basic Queueing Theory M/M/* Queues. Introduction

Basic Queueing Theory M/M/* Queues. Introduction Basc Queueng Theory M/M/* Queues These sldes are created by Dr. Yh Huang of George Mason Unversty. Students regstered n Dr. Huang's courses at GMU can ake a sngle achne-readable copy and prnt a sngle copy

More information

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set

More information

How Much to Bet on Video Poker

How Much to Bet on Video Poker How Much to Bet on Vdeo Poker Trstan Barnett A queston that arses whenever a gae s favorable to the player s how uch to wager on each event? Whle conservatve play (or nu bet nzes large fluctuatons, t lacks

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

1 Example 1: Axis-aligned rectangles

1 Example 1: Axis-aligned rectangles COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby s Alg. for Maximal Independent Sets using Pairwise Independence Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.

More information

Small-Signal Analysis of BJT Differential Pairs

Small-Signal Analysis of BJT Differential Pairs 5/11/011 Dfferental Moe Sall Sgnal Analyss of BJT Dff Par 1/1 SallSgnal Analyss of BJT Dfferental Pars Now lets conser the case where each nput of the fferental par conssts of an entcal D bas ter B, an

More information

Extending Probabilistic Dynamic Epistemic Logic

Extending Probabilistic Dynamic Epistemic Logic Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set

More information

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful

More information

+ + + - - This circuit than can be reduced to a planar circuit

+ + + - - This circuit than can be reduced to a planar circuit MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to

More information

21 Vectors: The Cross Product & Torque

21 Vectors: The Cross Product & Torque 21 Vectors: The Cross Product & Torque Do not use our left hand when applng ether the rght-hand rule for the cross product of two vectors dscussed n ths chapter or the rght-hand rule for somethng curl

More information

Mean Molecular Weight

Mean Molecular Weight Mean Molecular Weght The thermodynamc relatons between P, ρ, and T, as well as the calculaton of stellar opacty requres knowledge of the system s mean molecular weght defned as the mass per unt mole of

More information

Nonbinary Quantum Error-Correcting Codes from Algebraic Curves

Nonbinary Quantum Error-Correcting Codes from Algebraic Curves Nonbnary Quantum Error-Correctng Codes from Algebrac Curves Jon-Lark Km and Judy Walker Department of Mathematcs Unversty of Nebraska-Lncoln, Lncoln, NE 68588-0130 USA e-mal: {jlkm, jwalker}@math.unl.edu

More information

Finite Math Chapter 10: Study Guide and Solution to Problems

Finite Math Chapter 10: Study Guide and Solution to Problems Fnte Math Chapter 10: Study Gude and Soluton to Problems Basc Formulas and Concepts 10.1 Interest Basc Concepts Interest A fee a bank pays you for money you depost nto a savngs account. Prncpal P The amount

More information

On Lockett pairs and Lockett conjecture for π-soluble Fitting classes

On Lockett pairs and Lockett conjecture for π-soluble Fitting classes On Lockett pars and Lockett conjecture for π-soluble Fttng classes Lujn Zhu Department of Mathematcs, Yangzhou Unversty, Yangzhou 225002, P.R. Chna E-mal: ljzhu@yzu.edu.cn Nanyng Yang School of Mathematcs

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The Greedy Method. Introduction. 0/1 Knapsack Problem The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton

More information

A Probabilistic Theory of Coherence

A Probabilistic Theory of Coherence A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want

More information

PERRON FROBENIUS THEOREM

PERRON FROBENIUS THEOREM PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

8 Algorithm for Binary Searching in Trees

8 Algorithm for Binary Searching in Trees 8 Algorthm for Bnary Searchng n Trees In ths secton we present our algorthm for bnary searchng n trees. A crucal observaton employed by the algorthm s that ths problem can be effcently solved when the

More information

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3 Royal Holloway Unversty of London Department of Physs Seres Solutons of ODEs the Frobenus method Introduton to the Methodology The smple seres expanson method works for dfferental equatons whose solutons

More information

How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole

How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole CHAPTER 7 VECTOR BUNDLES We next begn addressng the queston: how do we assemble the tangent spaces at varous ponts of a manfold nto a coherent whole? In order to gude the decson, consder the case of U

More information

OPTIMAL INVESTMENT POLICIES FOR THE HORSE RACE MODEL. Thomas S. Ferguson and C. Zachary Gilstein UCLA and Bell Communications May 1985, revised 2004

OPTIMAL INVESTMENT POLICIES FOR THE HORSE RACE MODEL. Thomas S. Ferguson and C. Zachary Gilstein UCLA and Bell Communications May 1985, revised 2004 OPTIMAL INVESTMENT POLICIES FOR THE HORSE RACE MODEL Thomas S. Ferguson and C. Zachary Glsten UCLA and Bell Communcatons May 985, revsed 2004 Abstract. Optmal nvestment polces for maxmzng the expected

More information

Ring structure of splines on triangulations

Ring structure of splines on triangulations www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAM-Report 2014-48 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

REGULAR MULTILINEAR OPERATORS ON C(K) SPACES

REGULAR MULTILINEAR OPERATORS ON C(K) SPACES REGULAR MULTILINEAR OPERATORS ON C(K) SPACES FERNANDO BOMBAL AND IGNACIO VILLANUEVA Abstract. The purpose of ths paper s to characterze the class of regular contnuous multlnear operators on a product of

More information

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.

More information

Cautiousness and Measuring An Investor s Tendency to Buy Options

Cautiousness and Measuring An Investor s Tendency to Buy Options Cautousness and Measurng An Investor s Tendency to Buy Optons James Huang October 18, 2005 Abstract As s well known, Arrow-Pratt measure of rsk averson explans a ratonal nvestor s behavor n stock markets

More information

1. Measuring association using correlation and regression

1. Measuring association using correlation and regression How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

LECTURE 1: MOTIVATION

LECTURE 1: MOTIVATION LECTURE 1: MOTIVATION STEVEN SAM AND PETER TINGLEY 1. Towards quantum groups Let us begn by dscussng what quantum groups are, and why we mght want to study them. We wll start wth the related classcal objects.

More information

substances (among other variables as well). ( ) Thus the change in volume of a mixture can be written as

substances (among other variables as well). ( ) Thus the change in volume of a mixture can be written as Mxtures and Solutons Partal Molar Quanttes Partal molar volume he total volume of a mxture of substances s a functon of the amounts of both V V n,n substances (among other varables as well). hus the change

More information

An Electricity Trade Model for Microgrid Communities in Smart Grid

An Electricity Trade Model for Microgrid Communities in Smart Grid An Electrcty Trade Model for Mcrogrd Countes n Sart Grd Tansong Cu, Yanzh Wang, Shahn Nazaran and Massoud Pedra Unversty of Southern Calforna Departent of Electrcal Engneerng Los Angeles, CA, USA {tcu,

More information

where the coordinates are related to those in the old frame as follows.

where the coordinates are related to those in the old frame as follows. Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product

More information

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem.

Production. 2. Y is closed A set is closed if it contains its boundary. We need this for the solution existence in the profit maximization problem. Producer Theory Producton ASSUMPTION 2.1 Propertes of the Producton Set The producton set Y satsfes the followng propertes 1. Y s non-empty If Y s empty, we have nothng to talk about 2. Y s closed A set

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

Fisher Markets and Convex Programs

Fisher Markets and Convex Programs Fsher Markets and Convex Programs Nkhl R. Devanur 1 Introducton Convex programmng dualty s usually stated n ts most general form, wth convex objectve functons and convex constrants. (The book by Boyd and

More information

Embedding lattices in the Kleene degrees

Embedding lattices in the Kleene degrees F U N D A M E N T A MATHEMATICAE 62 (999) Embeddng lattces n the Kleene degrees by Hsato M u r a k (Nagoya) Abstract. Under ZFC+CH, we prove that some lattces whose cardnaltes do not exceed ℵ can be embedded

More information

Addendum to: Importing Skill-Biased Technology

Addendum to: Importing Skill-Biased Technology Addendum to: Importng Skll-Based Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our

More information

Lecture 2: Single Layer Perceptrons Kevin Swingler

Lecture 2: Single Layer Perceptrons Kevin Swingler Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

The descriptive complexity of the family of Banach spaces with the π-property

The descriptive complexity of the family of Banach spaces with the π-property Arab. J. Math. (2015) 4:35 39 DOI 10.1007/s40065-014-0116-3 Araban Journal of Mathematcs Ghadeer Ghawadrah The descrptve complexty of the famly of Banach spaces wth the π-property Receved: 25 March 2014

More information

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co; rsrkant@llnos.edu

More information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuity Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

More information

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co; rsrkant@llnos.edu

More information

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

A Novel Dynamic Role-Based Access Control Scheme in User Hierarchy

A Novel Dynamic Role-Based Access Control Scheme in User Hierarchy Journal of Coputatonal Inforaton Systes 6:7(200) 2423-2430 Avalable at http://www.jofcs.co A Novel Dynac Role-Based Access Control Schee n User Herarchy Xuxa TIAN, Zhongqn BI, Janpng XU, Dang LIU School

More information

INTERPRETING TRUE ARITHMETIC IN THE LOCAL STRUCTURE OF THE ENUMERATION DEGREES.

INTERPRETING TRUE ARITHMETIC IN THE LOCAL STRUCTURE OF THE ENUMERATION DEGREES. INTERPRETING TRUE ARITHMETIC IN THE LOCAL STRUCTURE OF THE ENUMERATION DEGREES. HRISTO GANCHEV AND MARIYA SOSKOVA 1. Introducton Degree theory studes mathematcal structures, whch arse from a formal noton

More information

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters 01 Proceedngs IEEE INFOCOM Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva heja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co;

More information

Maximizing profit using recommender systems

Maximizing profit using recommender systems Maxzng proft usng recoender systes Aparna Das Brown Unversty rovdence, RI aparna@cs.brown.edu Clare Matheu Brown Unversty rovdence, RI clare@cs.brown.edu Danel Rcketts Brown Unversty rovdence, RI danel.bore.rcketts@gal.co

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

TheHow and Why of Having a Successful Home Office

TheHow and Why of Having a Successful Home Office Near Optal Onlne Algorths and Fast Approxaton Algorths for Resource Allocaton Probles Nkhl R Devanur Kaal Jan Balasubraanan Svan Chrstopher A Wlkens Abstract We present algorths for a class of resource

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Generalizing the degree sequence problem

Generalizing the degree sequence problem Mddlebury College March 2009 Arzona State Unversty Dscrete Mathematcs Semnar The degree sequence problem Problem: Gven an nteger sequence d = (d 1,...,d n ) determne f there exsts a graph G wth d as ts

More information

Energies of Network Nastsemble

Energies of Network Nastsemble Supplementary materal: Assessng the relevance of node features for network structure Gnestra Bancon, 1 Paolo Pn,, 3 and Matteo Marsl 1 1 The Abdus Salam Internatonal Center for Theoretcal Physcs, Strada

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

The Mathematical Derivation of Least Squares

The Mathematical Derivation of Least Squares Pscholog 885 Prof. Federco The Mathematcal Dervaton of Least Squares Back when the powers that e forced ou to learn matr algera and calculus, I et ou all asked ourself the age-old queston: When the hell

More information

Faraday's Law of Induction

Faraday's Law of Induction Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation of Deutsch's Algorithm Using Mathcad Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"

More information

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant

More information

BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET

BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET Yugoslav Journal of Operatons Research (0), Nuber, 65-78 DOI: 0.98/YJOR0065Y BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET Peng-Sheng YOU Graduate Insttute of Marketng and Logstcs/Transportaton,

More information

J. Parallel Distrib. Comput.

J. Parallel Distrib. Comput. J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n

More information

1 What is a conservation law?

1 What is a conservation law? MATHEMATICS 7302 (Analytcal Dynamcs) YEAR 2015 2016, TERM 2 HANDOUT #6: MOMENTUM, ANGULAR MOMENTUM, AND ENERGY; CONSERVATION LAWS In ths handout we wll develop the concepts of momentum, angular momentum,

More information

The University of Texas at Austin. Austin, Texas 78712. December 1987. Abstract. programs in which operations of dierent processes mayoverlap.

The University of Texas at Austin. Austin, Texas 78712. December 1987. Abstract. programs in which operations of dierent processes mayoverlap. Atomc Semantcs of Nonatomc Programs James H. Anderson Mohamed G. Gouda Department of Computer Scences The Unversty of Texas at Austn Austn, Texas 78712 December 1987 Abstract We argue that t s possble,

More information

Conversion between the vector and raster data structures using Fuzzy Geographical Entities

Conversion between the vector and raster data structures using Fuzzy Geographical Entities Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,

More information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 5.3 Annuities, Future Value, and Sinking Funds Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme

More information

On Robust Network Planning

On Robust Network Planning On Robust Network Plannng Al Tzghadam School of Electrcal and Computer Engneerng Unversty of Toronto, Toronto, Canada Emal: al.tzghadam@utoronto.ca Alberto Leon-Garca School of Electrcal and Computer Engneerng

More information

THE HIT PROBLEM FOR THE DICKSON ALGEBRA

THE HIT PROBLEM FOR THE DICKSON ALGEBRA TRNSCTIONS OF THE MERICN MTHEMTICL SOCIETY Volume 353 Number 12 Pages 5029 5040 S 0002-9947(01)02705-2 rtcle electroncally publshed on May 22 2001 THE HIT PROBLEM FOR THE DICKSON LGEBR NGUY ÊN H. V. HUNG

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

From Selective to Full Security: Semi-Generic Transformations in the Standard Model

From Selective to Full Security: Semi-Generic Transformations in the Standard Model An extended abstract of ths work appears n the proceedngs of PKC 2012 From Selectve to Full Securty: Sem-Generc Transformatons n the Standard Model Mchel Abdalla 1 Daro Fore 2 Vadm Lyubashevsky 1 1 Département

More information

The Full-Wave Rectifier

The Full-Wave Rectifier 9/3/2005 The Full Wae ectfer.doc /0 The Full-Wae ectfer Consder the followng juncton dode crcut: s (t) Power Lne s (t) 2 Note that we are usng a transformer n ths crcut. The job of ths transformer s to

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

The Noether Theorems: from Noether to Ševera

The Noether Theorems: from Noether to Ševera 14th Internatonal Summer School n Global Analyss and Mathematcal Physcs Satellte Meetng of the XVI Internatonal Congress on Mathematcal Physcs *** Lectures of Yvette Kosmann-Schwarzbach Centre de Mathématques

More information

Texas Instruments 30X IIS Calculator

Texas Instruments 30X IIS Calculator Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Quality of Service Analysis and Control for Wireless Sensor Networks

Quality of Service Analysis and Control for Wireless Sensor Networks Qualty of ervce Analyss and Control for Wreless ensor Networs Jaes Kay and Jeff Frol Unversty of Veront ay@uv.edu, frol@eba.uv.edu Abstract hs paper nvestgates wreless sensor networ spatal resoluton as

More information

RESEARCH DISCUSSION PAPER

RESEARCH DISCUSSION PAPER Reserve Bank of Australa RESEARCH DISCUSSION PAPER Competton Between Payment Systems George Gardner and Andrew Stone RDP 2009-02 COMPETITION BETWEEN PAYMENT SYSTEMS George Gardner and Andrew Stone Research

More information

2. FINDING A SOLUTION

2. FINDING A SOLUTION The 7 th Balan Conference on Operational Research BACOR 5 Constanta, May 5, Roania OPTIMAL TIME AND SPACE COMPLEXITY ALGORITHM FOR CONSTRUCTION OF ALL BINARY TREES FROM PRE-ORDER AND POST-ORDER TRAVERSALS

More information

Section C2: BJT Structure and Operational Modes

Section C2: BJT Structure and Operational Modes Secton 2: JT Structure and Operatonal Modes Recall that the semconductor dode s smply a pn juncton. Dependng on how the juncton s based, current may easly flow between the dode termnals (forward bas, v

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Upper Bounds on the Cross-Sectional Volumes of Cubes and Other Problems

Upper Bounds on the Cross-Sectional Volumes of Cubes and Other Problems Upper Bounds on the Cross-Sectonal Volumes of Cubes and Other Problems Ben Pooley March 01 1 Contents 1 Prelmnares 1 11 Introducton 1 1 Basc Concepts and Notaton Cross-Sectonal Volumes of Cubes (Hyperplane

More information

Combinatorial Agency of Threshold Functions

Combinatorial Agency of Threshold Functions Combnatoral Agency of Threshold Functons Shal Jan Computer Scence Department Yale Unversty New Haven, CT 06520 shal.jan@yale.edu Davd C. Parkes School of Engneerng and Appled Scences Harvard Unversty Cambrdge,

More information

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks Bulletn of Mathematcal Bology (21 DOI 1.17/s11538-1-9517-4 ORIGINAL ARTICLE Product-Form Statonary Dstrbutons for Defcency Zero Chemcal Reacton Networks Davd F. Anderson, Gheorghe Cracun, Thomas G. Kurtz

More information

An Overview of Financial Mathematics

An Overview of Financial Mathematics An Overvew of Fnancal Mathematcs Wllam Benedct McCartney July 2012 Abstract Ths document s meant to be a quck ntroducton to nterest theory. It s wrtten specfcally for actuaral students preparng to take

More information

Multiplication Algorithms for Radix-2 RN-Codings and Two s Complement Numbers

Multiplication Algorithms for Radix-2 RN-Codings and Two s Complement Numbers Multplcaton Algorthms for Radx- RN-Codngs and Two s Complement Numbers Jean-Luc Beuchat Projet Arénare, LIP, ENS Lyon 46, Allée d Itale F 69364 Lyon Cedex 07 jean-luc.beuchat@ens-lyon.fr Jean-Mchel Muller

More information

Inventory Control in a Multi-Supplier System

Inventory Control in a Multi-Supplier System 3th Intl Workng Senar on Producton Econocs (WSPE), Igls, Autrche, pp.5-6 Inventory Control n a Mult-Suppler Syste Yasen Arda and Jean-Claude Hennet LAAS-CRS, 7 Avenue du Colonel Roche, 3077 Toulouse Cedex

More information

Elastic Systems for Static Balancing of Robot Arms

Elastic Systems for Static Balancing of Robot Arms . th World ongress n Mechans and Machne Scence, Guanajuato, Méco, 9- June, 0 _ lastc Sstes for Statc alancng of Robot rs I.Sonescu L. uptu Lucana Ionta I.Ion M. ne Poltehnca Unverst Poltehnca Unverst Poltehnca

More information

How To Calculate The Accountng Perod Of Nequalty

How To Calculate The Accountng Perod Of Nequalty Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information