DIRAC s BRA AND KET NOTATION. 1 From inner products to bra-kets 1

Size: px
Start display at page:

Download "DIRAC s BRA AND KET NOTATION. 1 From inner products to bra-kets 1"

Transcription

1 DIRAC s BRA AND KET NOTATION B. Zwebach October 7, 2013 Cotets 1 From er products to bra-kets 1 2 Operators revsted Projecto Operators Adjot of a lear operator Hermta ad Utary Operators No-deumerable bass 11 1 From er products to bra-kets Drac veted a useful alteratve otato for er products that leads to the cocepts of bras ad kets. The otato s sometmes more effcet tha the covetoal mathematcal otato we have bee usg. It s also wdely although ot uversally used. It all begs by wrtg the er product dfferetly. The rule s to tur er products to bra-ket pars as follows ( u,v ) (u v). (1.1) Istead of the er product comma we smply put a vertcal bar! We ca traslate our earler dscusso of er products trvally. I order to make you famlar wth the ew look we do t. We ow wrte (u v) = (v u), as well as (v v) 0 for all v, whle (v v) = 0 f ad oly f v = 0. We have learty the secod argumet (u c 1 v 1 + c 2 v 2 ) = c 1 (u v 1 ) +c 2 (u v 2 ), (1.2) for complex costats c 1 ad c 2, but atlearty the frst argumet (c 1 u 1 + c 2 u 2 v) = c 1 (u 1 v) + c 2 (u 2 v). (1.3) Two vectors u ad v for whch (u v) = 0 are orthogoal. For the orm: v 2 = (v v). The Schwarz equalty, for ay par u ad v of vectors reads (u v) u v. For a gve physcal stuato, the er product must be defed ad should satsfy the axoms. Let us cosder two examples: 1

2 a 1 b 1. Let a = ad b = 1 be two vectors a complex dmesoal vector space of dmeso a 2 b 2 two. We the defe You should cofrm the axoms are satsfed. (a b) a 1 b 1 + a 2 b 2. (1.4) 2. Cosder the complex vector space of complex fucto f (x) C wth x [0,L]. Gve two such fuctos f (x),g(x) we defe L (f g) f (x)g(x)dx. (1.5) 0 The verfcato of the axoms s aga qute straghtforward. A set of bass vectors {e } labelled by the tegers = 1,..., satsfyg (e e j ) = δ j, (1.6) s orthoormal. A arbtrary vector ca be wrtte as a lear superposto of bass states: We the see that the coeffcets are determed by the er product v = α e, (1.7) We ca therefore wrte (e k v) = e k α e = α e k e = α k. (1.8) v = e (e v). (1.9) To obta ow bras ad kets, we reterpret the er product. We wat to splt the er product to two gredets (u v) (u v). (1.10) Here v) s called a ket ad (u s called a bra. We wll vew the ket v) just as aother way to represet the vector v. Ths s a small subtlety wth the otato: we thk of v V as a vector ad also v) V as a vector. It s as f we added some decorato ) aroud the vector v to make t clear by specto that t s a vector, perhaps lke the usual top arrows that are added some cases. The label the ket s a vector ad the ket tself s that vector! Bras are somewhat dfferet objects. We say that bras belog to the space V dual to V. Elemets of V are lear maps from V to C. I covetoal mathematcal otato oe has a v V ad a lear fucto φ V such that φ(v), whch deotes the acto of the fucto of the vector v, s a umber. I the bracket otato we have the replacemets v v), φ (u, (1.11) φ u (v) (u v), 2

3 where we used the otato (6.6). Our bras are labelled by vectors: the object sde the ( s a vector. But bras are ot vectors. If kets are vewed as colum vectors, the bras are vewed as row vectors. I ths way a bra to the left of a ket makes sese: matrx multplcato of a row vector tmes a colum vector gves a umber. Ideed, for vectors a 1 b 1 we had Now we thk of ths as a 2 b 2 a =, b =.. a b (1.12) (a b) = a 1 b 1 + a 2 b a b (1.13) ( ) (a = a 1,a 2...,a, ad matrx multplcato gves us the desred aswer (a b) = ( a 1,a...,a 2 b 1 b 2 b 1 b 2 b) = (1.14). b ) = a 1 b 1 + a 2 b a b. (1.15). b Note that the bra labeled by the vector a s obtaed by formg the row vector ad complex cojugatg the etres. More abstractly the bra (u labeled by the vector u s defed by ts acto o arbtrary vectors v) as follows (u : v) (u v). (1.16) As requred by the defto, ay lear map from V to C defes a bra, ad the correspodg uderlyg vector. For example let v be a geerc vector: v 1 v 2 v =, (1.17). v A lear map f (v) that actg o a vector v gves a umber s a expresso of the form f (v) = α 1 v 1 + α 2 v α v. (1.18) It s a lear fucto of the compoets of the vector. The lear fucto s specfed by the umbers α, ad for coveece (ad wthout loss of geeralty) we used ther complex cojugates. Note that we eed exactly costats, so they ca be used to assemble a row vector or a bra ( (α = α 1,α 2,...,α ) (1.19) 3

4 ad the assocated vector or ket Note that, by costructo α) = α 1 α2 (1.20). α f (v) = (α v). (1.21) Ths llustrates the pot that () bras represet dual objects that act o vectors ad () bras are labelled by vectors. Bras ca be added ad ca be multpled by complex umbers ad there s a zero bra defed to gve zero actg o ay vector, so V s also a complex vector space. As a bra, the lear superposto s defed to act o a vector (ket) c) to gve the umber (ω α(a + β(b V, α,β C, (1.22) α(a c) + β(b c). (1.23) For ay vector v) V there s a uque bra (v V. If there would be aother bra (v t would have to act o arbtrary vectors w) just lke (v : (v w) = (v w) (w v) (w v ) = 0 (w v v ) = 0. (1.24) I the frst step we used complex cojugato ad the secod step learty. Now the vector v v must have zero er product wth ay vector w, so v v = 0 ad v = v. We ca ow recosder equato (1.3) ad wrte a extra rght-had sde ( ) (α α α 1 a 1 + α 2 a 2 b) = 1(a 1 b) + α 2(a 2 b) = 1(a 1 + α 2 (a 2 b) (1.25) so that we coclude that the rules to pass from kets to bras clude v) = α α 1 a 1 ) + α 2 a 2 ) (v = 1(a 1 + α 2 (a 2. (1.26) For smplcty of otato we sometmes wrte kets wth labels smpler tha vectors. Let us recosder the bass vectors {e } dscussed (1.6). The ket e ) s smply called ) ad the orthoormal codto reads ( j) = δ j. (1.27) The expaso (1.7) of a vector ow reads v) = )α, (1.28) As (1.8) the expaso coeffcets are α k = (k v) so that v) = )( v). (1.29) 4

5 2 Operators revsted Let T be a operator a vector space V. Ths meas that actg o vectors o V t gves vectors o V, somethg we wrte as Ω : V V. (2.30) We deote by Ω a) the vector obtaed by actg wth Ω o the vector a): The operator Ω s lear f addtoally we have a) V Ω a) V. (2.31) ( ) Ω a) + b) = Ω a) +Ω b), ad Ω(α a)) = α Ω a). (2.32) Whe kets are labeled by vectors we sometmes wrte Ωa) Ω a), (2.33) It s useful to ote that a lear operator o V s also a lear operator o V Ω : V V, (2.34) We wrte ths as (a (a Ω V. (2.35) The object (a Ω s defed to be the bra that actg o the ket b) gves the umber (a Ω b). We ca wrte operators terms of bras ad kets, wrtte a sutable order. As a example of a operator cosder a bra (a ad a ket b). We clam that the object Ω = a)(b, (2.36) s aturally vewed as a lear operator o V ad o V. Ideed, actg o a vector we let t act as the bra-ket otato suggests: Ω v) a)(b v) a), sce (b v) s a umber. (2.37) Actg o a bra t gves a bra: (w Ω (w a)(b (b, sce (w a) s a umber. (2.38) Let us ow revew the descrpto of operators as matrces. The choce of bass s ours to make. For smplcty, however, we wll usually cosder orthoormal bases. Cosder therefore, two vectors expaded a orthoormal bass { )}: a) = )a, b) = )b. (2.39) 5

6 Assume b) s obtaed by the acto of Ω o a): Ω a) = b) Ω )a = )b. (2.40) Actg o both sdes of ths vector equato wth the bra (m we fd (m Ω )a = (m )b = b m (2.41) We ow defe the matrx elemets Ω m (m Ω ). (2.42) so that the above equato reads Ω m a = b m, (2.43) whch s the matrx verso of the orgal relato Ω a) = b). The chose bass has allowed us to vew the lear operator Ω as a matrx, also deoted as Ω, wth matrx compoets Ω m : Ω 11 Ω Ω 1N Ω 21 Ω Ω 2N Ω, wth Ω j = ( Ω j). (2.44)..... Ω N1 Ω N Ω NN There s oe addtoal clam. The operator tself ca be wrtte terms of the matrx elemets ad bass bras ad kets. We clam that Ω = m)ω m (. (2.45) m, We ca verfy that ths s correct by computg the matrx elemets usg t: (m Ω ) = Ω m (m m)( ) = Ω m δ m m δ = Ω m, (2.46) m, m, as expected from the defto (2.42). 2.1 Projecto Operators Cosder the famlar orthoormal bass { )} of V ad choose oe elemet m) from the bass to form a operator P m defed by P m m)(m. (2.47) Ths operator maps ay vector v) V to a vector alog, ). Ideed, actg o v) t gves P m v) = m)(m v) m). (2.48) 6

7 Comparg the above expresso for P m wth (2.45) we see that the chose bass, P s represeted by a matrx all of whose elemets are zero, except for the (, ) elemet (P ) whch s oe: P (2.49) A hermta operator P s sad to be a projecto operator f t satsfes the operator equato PP = P. Ths meas that actg twce wth a projecto operator o a vector gves the same as actg oce. The operator P m s a projecto operator sce ( )( ) P m P m = m)(m m)(m = m)(m m)(m = m)(m, (2.50) sce (m m) = 1. The operator P m s sad to be a rak oe projecto operator sce t projects to a oe-dmesoal subspace of V, the subspace geerated by m). Usg the bass vector m) wth m = we ca defe P m, m)(m + )(. (2.51) Actg o ay vector v) V, ths operator gves us a vector the subspace spaed by m) ad ): P m, v) = m)(m v) + )( v). (2.52) Usg the orthogoalty of m) ad ) we quckly fd that P m, P m, = P m, ad therefore P m, s a projector. It s a rak two projector, sce t projects to a two-dmesoal subspace of V, the subspace spaed by m) ad ). Smlarly, we ca costruct a rak three projector by addg a extra term k)(k wth k = m ad k =. If we clude all bass vectors we would have the operator P 1,...,N 1)(1 + 2)( N)(N. (2.53) As a matrx P 1,...,N has a oe o every elemet of the dagoal ad a zero everywhere else. Ths s therefore the ut matrx, whch represets the detty operator. Ideed we atcpated ths (1.29), ad we thus wrte 1 = )(. (2.54) Ths s the completeess relato for the chose orthoormal bass. Ths equato s sometmes called the resoluto of the detty. Example. For the sp oe-half system the ut operator ca be wrtte as a sum of two terms sce the vector space s two dmesoal. Usg the orthoormal bass vectors +) ad ) for sps alog the postve ad egatve z drectos, respectvely, we have 1 = +)(+ + )(. (2.55) 7

8 Example. We ca use the completeess relato to show that our formula (2.42) for matrx elemets s cosstet wth matrx multplcato. Ideed for the product Ω 1 Ω 2 of two operators we wrte (Ω 1 Ω 2 ) m = (m Ω 1 Ω 2 ) = (m Ω 1 1 Ω 2 ) ( N ) N N (2.56) = (m Ω 1 k)(k Ω 2 ) = (m Ω 1 k)(k Ω 2 ) = (Ω 1 ) mk (Ω 2 ) k. k=1 k=1 k=1 Ths s the expected rule for the multplcato of the matrces correspodg to Ω 1 ad Ω Adjot of a lear operator A lear operator Ω o V s defed by ts acto o the vectors V. We have oted that Ω ca also be vewed as a lear operator o the dual space V. We defed the lear operator Ω assocated wth Ω. I geeral Ω by (Ω u v) = (u Ωv) (2.57) Flppg the order o the left-had sde we get (v Ω u) = (u Ωv) (2.58) Complex cojugatg, ad wrtg the operators more explctly (v Ω u) = (u Ω v), u,v. (2.59) Flppg the two sdes of (2.57) we also get (v Ω u) = (Ωv u) (2.60) from whch, takg the ket away, we lear that (v Ω (Ωv. (2.61) Aother way to state the acto of the operator Ω s as follows. The lear operator Ω duces a map v) v ) of vectors V ad, fact, s defed by gvg a complete lst of these maps. The operator Ω s defed as the oe that duces the maps (v (v of the correspodg bras. Ideed, v ) = Ωv) = Ω v), (v = (Ωv = (v Ω (2.62) The frst le s just deftos. O the secod le, the frst equalty s obtaed by takg bras of the frst equalty o the frst le. The secod equalty s just (2.61). We say t as The bra assocated wth Ω v) s (v Ω. (2.63) 8

9 To see what hermtcty meas at the level of matrx elemets, we take u, v to be orthoormal bass vectors (2.59) ( Ω j) = (j Ω ) (Ω ) j = (Ω j ). (2.64) I matrx otato we have Ω = (Ω t ) where the superscrpt t deotes trasposto. Exercse. Show that (Ω 1 Ω 2 ) = Ω Ω by takg matrx elemets. 2 1 Exercse. Gve a operator Ω = a)(b for arbtrary vectors a, b, wrte a bra-ket expresso for Ω. Soluto: Actg wth Ω o v) ad the takg the dual gves Sce ths equato s vald for ay bra (v we read 2.3 Hermta ad Utary Operators Ω v) = a)(b v) (v Ω = (v b)(a, (2.65) A lear operator Ω s sad to be hermta f t s equal to ts adjot: Ω = b)(a. (2.66) Hermta Operator: Ω = Ω. (2.67) I quatum mechacs Hermta operators are assocated wth observables. The egevalues of a Hermta operator are the possble measured values of the observables. As we wll show soo, the egevalues of a Hermta operator are all real. A operator A s sad to be at-hermta f A = A. Exercse: Show that the commutator [Ω 1, Ω 2 ] of two hermta operators Ω 1 ad Ω 2 s at-hermta. There are a couple of equatos that rewrte useful ways the ma property of Hermta operators. Usg Ω = Ω (2.59) we fd If Ω s a Hermta Operator: (v Ω u) = (u Ω v), u,v. (2.68) It follows that the expectato value of a Hermta operator ay state s real (v Ω v) s real for ay hermta Ω. (2.69) Aother eat form of the hermtcty codto s derved as follows: (Ωu v) = (u Ω v) = (u Ω v) = (u Ωv), (2.70) so that all all Hermta Operator: (Ωu v) = (u Ωv). (2.71) 9

10 I ths expresso we see thatahermta operator moves freelyfrom thebra to theket (ad vceversa). Example: For wavefucto f (x) C we have wrtte (f g) = (f(x)) g(x)dx (2.72) For a Hermta Ω we have (Ωf g) = (f Ωg) or explctly (Ωf(x)) g(x)dx = (f(x)) Ωg(x)dx (2.73) Verfy that the lear operator Ω = d dx s hermta whe we restrct to fuctos that vash at ±. A operator U s sad to be a utary operator f U s a verse for U, that s, U U ad UU are both the detty operator: U s a utary operator: U U = UU = 1 (2.74) I fte dmesoal vector spaces U U = 1 mples UU = 1, but ths s ot always the case for fte dmesoal vector spaces. A key property of utary operators s that they preserve the orm of states. Ideed, assume that ψ ) s obtaed by the acto of U o ψ): ψ ) = U ψ) (2.75) Takg the dual we have ad therefore (ψ = (ψ U, (2.76) (ψ ψ ) = (ψ U U ψ) = (ψ ψ), (2.77) showg that ψ) ad U ψ) are states wth the same orm. More geerally (Ua Ub) = (a U U b) = (a b). (2.78) Aother mportat property of utary operators s that actg o a orthoormal bass they gve aother orthoormal bass. To show ths cosder the orthoormal bass a 1 ), a 2 ),... a N ), (a a j ) = δ j (2.79) Actg wth U we get Ua 1 ), Ua 2 ),... Ua N ), (2.80) To show that ths s a bass we must prove that β Ua ) = 0 (2.81) 10

11 mples β = 0 for all. Ideed, the above gves β Ua ) = β U a ) = U β a ) = 0. (2.82) Actg wth U from the left we fd that L β a ) = 0 ad, sce the a ) form a bass, we get β = 0 for all, as desred. The ew bass s orthoormal because (Ua Ua j ) = (a U U a j ) = (a a j ) = δ j. (2.83) It follows from the above that the operator U ca be wrtte as sce N U = Ua )(a, (2.84) N =1 U a j ) = Ua )(a a j ) = Ua ). (2.85) =1 I fact for ay utary operator U a vector space V there exst orthoormal bases { a )} ad { b )} such that U ca be wrtte as N U = b )(a. (2.86) Ideed, ths s just a rewrtg of (2.84), wth a ) ay orthoormal bass ad b ) = Ua ). Exercse: Verfy that U (2.86) satsfes U U = UU = 1. Exercse: Prove that (a U a j ) = (b U b j ). 3 No-deumerable bass =1 I ths secto we descrbe the use of bras ad kets for the posto ad mometum states of a partcle movg o the real le x R. Let us beg wth posto. We wll troduce posto states x) where the label x the ket s the value of the posto. Sce x s a cotuous varable ad we posto states x) for all values of x to form a bass, we are dealg wth a fte bass that s ot possble to label as 1), 2),..., t s a o-deumerable bass. So we have Bass states : x), x R. (3.87) Bass states wth dfferet values of x are dfferet vectors the state space (a complex vector space, as always quatum mechacs). Note here that the label o the ket s ot a vector! So ax) = a x), for ay real a = 1. I partcular x) = x) uless x = 0. For quatum mechacs three dmesos, we have posto states x ). Here the label s a vector a three-dmesoal real vector space (our space!) whlethe ket s a vector the ftedmesoal complex vector space of states of thetheory. 11

12 Aga somethg lke x 1 + x 2 ) has othg to do wth x 1 ) + x 2 ). The ) eclosg the label of the posto egestates plays a crucal role: t helps us see that object lves a fte dmesoal complex vector space. The er product must be defed, so we wll take (x y) = δ(x y). (3.88) It follows that posto states wth dfferet postos are orthogoal to each other. The orm of a posto state s fte: (x x) = δ(0) =, so these are ot allowed states of partcles. We vsualze the state x) as the state of a partcle perfectly localzed at x, but ths s a dealzato. We ca easly costruct ormalzable states usg superpostos of posto states. We also have a completeess relato 1 = dx x)(x. (3.89) Ths s cosstet wth our er product above. Lettg the above equato act o y) we fd a equalty: y) = dx x)(x y) = dx x) δ(x y) = y). (3.90) The posto operator ˆx s defed by ts acto o the posto states. Not surprsgly we let xˆ x) = x x), (3.91) thus declarg that x) are ˆx egestates wth egevalue equal to the posto x. We ca also show that ˆx s a Hermta operator by checkg that ˆx ad ˆx have the same matrx elemets: (x 1 xˆ x 2 ) = (x 2 xˆ x 1 ) = [x 1 δ(x 1 x 2 )] = x 2 δ(x 1 x 2 ) = (x 1 xˆ x 2 ). (3.92) We thus coclude that ˆx = xˆ ad the bra assocated wth (3.91) s (x xˆ = x(x. (3.93) Gve the state ψ) of a partcle, we defe the assocated posto-state wavefucto ψ(x) by ψ(x) (x ψ) C. (3.94) Ths s sesble: (x ψ) s a umber that depeds o the value of x, thus a fucto of x. We ca ow do a umber of basc computatos. Frst we wrte ay state as a superposto of posto egestates, by sertg 1 as the completeess relato ψ) = 1 ψ) = dx x)(x ψ) = dx x) ψ(x). (3.95) As expected, ψ(x) s the compoet of ψ alog the state x). Ovelap of states ca also be wrtte posto space: (φ ψ) = dx (φ x)(x ψ) = dx φ (x)ψ(x). (3.96) 12

13 Matrx elemets volvg ˆx are also easly evaluated (φ xˆ ψ) = (φ xˆ1 ψ) = dx (φ xˆ x)(x ψ) = dx (φ x) x (x ψ) = dx φ (x)xψ(x). (3.97) We ow troduce mometum states p) that are egestates of the mometum operator ˆp complete aalogy to the posto states Bass states : p), p R. (p p) = δ(p p ), 1 = dp p)(p, (3.98) Just as for coordate space we also have pˆ p) = p p) pˆ = p, ˆ ad (p ˆ p = p(p. (3.99) I order to relate the two bases we eed the value of the overlap (x p). Sce we terpret ths as the wavefucto for a partcle wth mometum p we have from (6.39) of Chapter 1 that e px/ (x p) =. (3.100) 2π The ormalzato was adjusted properly to be compatble wth the completeess relatos. Ideed, for example, cosder the (p p) overlap ad use the completeess x to evaluate t 1 )x/ 1 )u (p p) = dx(p x)(x p) = dxe (p p = du e (p p, (3.101) 2π 2π where we let u = x/ the last step. We clam that the last tegral s precsely the tegral represetato of the delta fucto δ(p p ): 1 du e (p p )u = δ(p p ). (3.102) 2π Ths, the gves the correct value for the overlap (p p ), as we clamed. The tegral (3.102) ca be justfed usg the fact that the fuctos 1 ( 2πx ) f (x) exp, (3.103) L L form a complete orthorormal set of fuctos over the terval x [ L/2, L/2]. Completeess the meas that f (x)f (x ) = δ(x x ). (3.104) We thus have Z 1 ( ) exp 2π (x x ) = δ(x x ). (3.105) L L Z 13

14 I the lmt as L goes to fty the above sum ca be wrtte as a tegral sce the expoetal s a very slowly varyg fucto of Z. Sce Δ = 1 wth u = 2π/L we have Δu = 2π/L 1 ad the 1 ( ) Δu ( ) 1 ) exp 2π (x x ) = exp u(x x ) due u(x x, (3.106) L L 2π 2π Z u ad back (3.105) we have justfed (3.102). We ca ow ask: What s (p ψ)? We compute 1 dxe px/ (p ψ) = dx(p x)(x ψ) = ψ(x) = ψ(p), 2π (3.107) whch s the Fourer trasform of ψ(x), as defed (6.41) of Chapter 1. Thus the Fourer trasform of ψ(x) s the wavefucto the mometum represetato. It s useful to kow how to evaluate (x pˆ ψ). We do t by sertg a complete set of mometum states: (x pˆ ψ) = dp (x p)(p pˆ ψ) = dp (p(x p))(p ψ) (3.108) Now we otce that d p(x p) = (x p) dx ad thus ( d ) (x pˆ ψ) = dp (x p) (p ψ). dx (3.109) (3.110) The dervatve ca be moved out of the tegral, sce o other part of the tegrad depeds o x: d (x pˆ ψ) = dp (x p)(p ψ) dx (3.111) The completeess sum s ow trval ad ca be dscarded to obta d d (x pˆ ψ) = (x ψ) = ψ(x). dx dx (3.112) Exercse. Show that d (p xˆ ψ) = ψ(p). (3.113) dp 14

15 MIT OpeCourseWare Quatum Physcs II Fall 2013 For formato about ctg these materals or our Terms of Use, vst:

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet gerry@me.pdx.edu Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

Online Appendix: Measured Aggregate Gains from International Trade

Online Appendix: Measured Aggregate Gains from International Trade Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. yayli@science.ankara.edu.tr. evrenziplar@yahoo.

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. yayli@science.ankara.edu.tr. evrenziplar@yahoo. ON SLANT HELICES AND ENERAL HELICES IN EUCLIDEAN -SPACE Yusuf YAYLI Evre ZIPLAR Departmet of Mathematcs Faculty of Scece Uversty of Akara Tadoğa Akara Turkey yayl@sceceakaraedutr Departmet of Mathematcs

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are : Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

More information

The simple linear Regression Model

The simple linear Regression Model The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg

More information

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute

More information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

The Digital Signature Scheme MQQ-SIG

The Digital Signature Scheme MQQ-SIG The Dgtal Sgature Scheme MQQ-SIG Itellectual Property Statemet ad Techcal Descrpto Frst publshed: 10 October 2010, Last update: 20 December 2010 Dalo Glgorosk 1 ad Rue Stesmo Ødegård 2 ad Rue Erled Jese

More information

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag

More information

Lecture 7. Norms and Condition Numbers

Lecture 7. Norms and Condition Numbers Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three

More information

Curve Fitting and Solution of Equation

Curve Fitting and Solution of Equation UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

More information

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh

More information

Sequences and Series

Sequences and Series Secto 9. Sequeces d Seres You c thk of sequece s fucto whose dom s the set of postve tegers. f ( ), f (), f (),... f ( ),... Defto of Sequece A fte sequece s fucto whose dom s the set of postve tegers.

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID CH. ME56 STTICS Ceter of Gravt, Cetrod, ad Momet of Ierta CENTE OF GITY ND CENTOID 5. CENTE OF GITY ND CENTE OF MSS FO SYSTEM OF PTICES Ceter of Gravt. The ceter of gravt G s a pot whch locates the resultat

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

α 2 α 1 β 1 ANTISYMMETRIC WAVEFUNCTIONS: SLATER DETERMINANTS (08/24/14)

α 2 α 1 β 1 ANTISYMMETRIC WAVEFUNCTIONS: SLATER DETERMINANTS (08/24/14) ANTISYMMETRI WAVEFUNTIONS: SLATER DETERMINANTS (08/4/4) Wavefuctos that descrbe more tha oe electro must have two characterstc propertes. Frst, scll electros are detcal partcles, the electros coordates

More information

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has

More information

Common p-belief: The General Case

Common p-belief: The General Case GAMES AND ECONOMIC BEHAVIOR 8, 738 997 ARTICLE NO. GA97053 Commo p-belef: The Geeral Case Atsush Kaj* ad Stephe Morrs Departmet of Ecoomcs, Uersty of Pesylaa Receved February, 995 We develop belef operators

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

An Effectiveness of Integrated Portfolio in Bancassurance

An Effectiveness of Integrated Portfolio in Bancassurance A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the

More information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information

Models for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,

More information

Generalizations of Pauli channels

Generalizations of Pauli channels Acta Math. Hugar. 24(2009, 65 77. Geeralzatos of Paul chaels Dées Petz ad Hromch Oho 2 Alfréd Réy Isttute of Mathematcs, H-364 Budapest, POB 27, Hugary 2 Graduate School of Mathematcs, Kyushu Uversty,

More information

THE McELIECE CRYPTOSYSTEM WITH ARRAY CODES. MATRİS KODLAR İLE McELIECE ŞİFRELEME SİSTEMİ

THE McELIECE CRYPTOSYSTEM WITH ARRAY CODES. MATRİS KODLAR İLE McELIECE ŞİFRELEME SİSTEMİ SAÜ e Blmler Dergs, 5 Clt, 2 Sayı, THE McELIECE CRYPTOSYSTEM WITH ARRAY CODES Vedat ŞİAP* *Departmet of Mathematcs, aculty of Scece ad Art, Sakarya Uversty, 5487, Serdva, Sakarya-TURKEY vedatsap@gmalcom

More information

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom. UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.

More information

Polyphase Filters. Section 12.4 Porat 1/39

Polyphase Filters. Section 12.4 Porat 1/39 Polyphase Flters Secto.4 Porat /39 .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful

More information

On Error Detection with Block Codes

On Error Detection with Block Codes BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,

More information

On Savings Accounts in Semimartingale Term Structure Models

On Savings Accounts in Semimartingale Term Structure Models O Savgs Accouts Semmartgale Term Structure Models Frak Döberle Mart Schwezer moeyshelf.com Techsche Uverstät Berl Bockehemer Ladstraße 55 Fachberech Mathematk, MA 7 4 D 6325 Frakfurt am Ma Straße des 17.

More information

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl

More information

Load and Resistance Factor Design (LRFD)

Load and Resistance Factor Design (LRFD) 53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning CIS63 - Artfcal Itellgece Logstc regresso Vasleos Megalookoomou some materal adopted from otes b M. Hauskrecht Supervsed learg Data: D { d d.. d} a set of eamples d < > s put vector ad s desred output

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

More information

Classic Problems at a Glance using the TVM Solver

Classic Problems at a Glance using the TVM Solver C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information

Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes

Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes Covero of No-Lear Stregth Evelope to Geeralzed Hoek-Brow Evelope Itroducto The power curve crtero commoly ued lmt-equlbrum lope tablty aaly to defe a o-lear tregth evelope (relatohp betwee hear tre, τ,

More information

CSSE463: Image Recognition Day 27

CSSE463: Image Recognition Day 27 CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)

More information

of the relationship between time and the value of money.

of the relationship between time and the value of money. TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp

More information

Projection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li

Projection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li Iteratoal Joural of Scece Vol No7 05 ISSN: 83-4890 Proecto model for Computer Network Securty Evaluato wth terval-valued tutostc fuzzy formato Qgxag L School of Software Egeerg Chogqg Uversty of rts ad

More information

How To Value An Annuity

How To Value An Annuity Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%

More information

On Cheeger-type inequalities for weighted graphs

On Cheeger-type inequalities for weighted graphs O Cheeger-type equaltes for weghted graphs Shmuel Fredlad Uversty of Illos at Chcago Departmet of Mathematcs 851 S. Morga St., Chcago, Illos 60607-7045 USA Rehard Nabbe Fakultät für Mathematk Uverstät

More information

Load Balancing Control for Parallel Systems

Load Balancing Control for Parallel Systems Proc IEEE Med Symposum o New drectos Cotrol ad Automato, Chaa (Grèce),994, pp66-73 Load Balacg Cotrol for Parallel Systems Jea-Claude Heet LAAS-CNRS, 7 aveue du Coloel Roche, 3077 Toulouse, Frace E-mal

More information

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,

More information

arxiv:math/0510414v1 [math.pr] 19 Oct 2005

arxiv:math/0510414v1 [math.pr] 19 Oct 2005 A MODEL FOR THE BUS SYSTEM IN CUERNEVACA MEXICO) JINHO BAIK ALEXEI BORODIN PERCY DEIFT AND TOUFIC SUIDAN arxv:math/05044v [mathpr 9 Oct 2005 Itroducto The bus trasportato system Cuerevaca Mexco has certa

More information

CHAPTER 2. Time Value of Money 6-1

CHAPTER 2. Time Value of Money 6-1 CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

More information

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting MANAGEMENT SCIENCE Vol. 52, No., Jauary 26, pp. 95 ss 25-99 ess 526-55 6 52 95 forms do.287/msc.5.447 26 INFORMS Prcg Amerca-Style Dervatves wth Europea Call Optos Scott B. Laprse BAE Systems, Advaced

More information

Plastic Number: Construction and Applications

Plastic Number: Construction and Applications Scet f c 0 Advaced Advaced Scetfc 0 December,.. 0 Plastc Number: Costructo ad Applcatos Lua Marohć Polytechc of Zagreb, 0000 Zagreb, Croata lua.marohc@tvz.hr Thaa Strmeč Polytechc of Zagreb, 0000 Zagreb,

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

SOLID STATE PHYSICS. Crystal structure. (d) (e) (f)

SOLID STATE PHYSICS. Crystal structure. (d) (e) (f) SOLID STAT PHYSICS y defto, sold state s that partcular aggregato form of matter characterzed by strog teracto forces betwee costtuet partcles (atoms, os, or molecules. As a result, a sold state materal

More information

Measuring the Quality of Credit Scoring Models

Measuring the Quality of Credit Scoring Models Measur the Qualty of Credt cor Models Mart Řezáč Dept. of Matheatcs ad tatstcs, Faculty of cece, Masaryk Uversty CCC XI, Edurh Auust 009 Cotet. Itroducto 3. Good/ad clet defto 4 3. Measur the qualty 6

More information

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,

More information

STOCHASTIC approximation algorithms have several

STOCHASTIC approximation algorithms have several IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 60, NO 10, OCTOBER 2014 6609 Trackg a Markov-Modulated Statoary Degree Dstrbuto of a Dyamc Radom Graph Mazyar Hamd, Vkram Krshamurthy, Fellow, IEEE, ad George

More information

MDM 4U PRACTICE EXAMINATION

MDM 4U PRACTICE EXAMINATION MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths

More information

Aggregation Functions and Personal Utility Functions in General Insurance

Aggregation Functions and Personal Utility Functions in General Insurance Acta Polytechca Huarca Vol. 7, No. 4, 00 Areato Fuctos ad Persoal Utlty Fuctos Geeral Isurace Jaa Šprková Departmet of Quattatve Methods ad Iformato Systems, Faculty of Ecoomcs, Matej Bel Uversty Tajovského

More information

Bayesian Network Representation

Bayesian Network Representation Readgs: K&F 3., 3.2, 3.3, 3.4. Bayesa Network Represetato Lecture 2 Mar 30, 20 CSE 55, Statstcal Methods, Sprg 20 Istructor: Su-I Lee Uversty of Washgto, Seattle Last tme & today Last tme Probablty theory

More information

On formula to compute primes and the n th prime

On formula to compute primes and the n th prime Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal

More information

Methods and Data Analysis

Methods and Data Analysis Fudametal Numercal Methods ad Data Aalyss by George W. Colls, II George W. Colls, II Table of Cotets Lst of Fgures...v Lst of Tables... Preface... Notes to the Iteret Edto...v. Itroducto ad Fudametal Cocepts....

More information

Analysis of Multi-product Break-even with Uncertain Information*

Analysis of Multi-product Break-even with Uncertain Information* Aalyss o Mult-product Break-eve wth Ucerta Iormato* Lazzar Lusa L. - Morñgo María Slva Facultad de Cecas Ecoómcas Uversdad de Bueos Ares 222 Córdoba Ave. 2 d loor C20AAQ Bueos Ares - Argeta lazzar@eco.uba.ar

More information

Statistical Decision Theory: Concepts, Methods and Applications. (Special topics in Probabilistic Graphical Models)

Statistical Decision Theory: Concepts, Methods and Applications. (Special topics in Probabilistic Graphical Models) Statstcal Decso Theory: Cocepts, Methods ad Applcatos (Specal topcs Probablstc Graphcal Models) FIRST COMPLETE DRAFT November 30, 003 Supervsor: Professor J. Rosethal STA4000Y Aal Mazumder 9506380 Part

More information

2009-2015 Michael J. Rosenfeld, draft version 1.7 (under construction). draft November 5, 2015

2009-2015 Michael J. Rosenfeld, draft version 1.7 (under construction). draft November 5, 2015 009-015 Mchael J. Rosefeld, draft verso 1.7 (uder costructo). draft November 5, 015 Notes o the Mea, the Stadard Devato, ad the Stadard Error. Practcal Appled Statstcs for Socologsts. A troductory word

More information

An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information

An Approach to Evaluating the Computer Network Security with Hesitant Fuzzy Information A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog A Approach to Evaluatg the Computer Network Securty wth Hestat Fuzzy Iformato Jafeg Dog, Frst ad Correspodg Author

More information

STATIC ANALYSIS OF TENSEGRITY STRUCTURES

STATIC ANALYSIS OF TENSEGRITY STRUCTURES SI NYSIS O ENSEGIY SUUES JUIO ES OE HESIS PESENED O HE GDUE SHOO O HE UNIVESIY O OID IN PI UIEN O HE EQUIEENS O HE DEGEE O SE O SIENE UNIVESIY O OID o m mother for her fte geerost. KNOWEDGENS I wat to

More information

Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases

Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases Locally Adaptve Dmesoalty educto for Idexg Large Tme Seres Databases Kaushk Chakrabart Eamo Keogh Sharad Mehrotra Mchael Pazza Mcrosoft esearch Uv. of Calfora Uv. of Calfora Uv. of Calfora edmod, WA 985

More information

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral

More information

Speeding up k-means Clustering by Bootstrap Averaging

Speeding up k-means Clustering by Bootstrap Averaging Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg

More information

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts Optmal replacemet ad overhaul decsos wth mperfect mateace ad warraty cotracts R. Pascual Departmet of Mechacal Egeerg, Uversdad de Chle, Caslla 2777, Satago, Chle Phoe: +56-2-6784591 Fax:+56-2-689657 rpascual@g.uchle.cl

More information

Near Neighbor Distribution in Sets of Fractal Nature

Near Neighbor Distribution in Sets of Fractal Nature Iteratoal Joural of Computer Iformato Systems ad Idustral Maagemet Applcatos. ISS 250-7988 Volume 5 (202) 3 pp. 59-66 MIR Labs, www.mrlabs.et/jcsm/dex.html ear eghbor Dstrbuto Sets of Fractal ature Marcel

More information

REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ),

REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ), REVISTA INVESTIGACION OPERACIONAL Vol 25, No, 24 RECURRENCE AND DIRECT FORMULAS FOR TE AL & LA NUMBERS Eduardo Pza Volo Cero de Ivesgacó e Maemáca Pura y Aplcada (CIMPA), Uversdad de Cosa Rca ABSTRACT

More information

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable

More information

Automated Event Registration System in Corporation

Automated Event Registration System in Corporation teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee

More information

Regression Analysis. 1. Introduction

Regression Analysis. 1. Introduction . Itroducto Regresso aalyss s a statstcal methodology that utlzes the relato betwee two or more quattatve varables so that oe varable ca be predcted from the other, or others. Ths methodology s wdely used

More information

Constrained Cubic Spline Interpolation for Chemical Engineering Applications

Constrained Cubic Spline Interpolation for Chemical Engineering Applications Costraed Cubc Sple Iterpolato or Chemcal Egeerg Applcatos b CJC Kruger Summar Cubc sple terpolato s a useul techque to terpolate betwee kow data pots due to ts stable ad smooth characterstcs. Uortuatel

More information

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1 akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of

More information

Design of Experiments

Design of Experiments Chapter 4 Desg of Expermets 4. Itroducto I Chapter 3 we have cosdered the locato of the data pots fxed ad studed how to pass a good respose surface through the gve data. However, the choce of pots where

More information

Incorporating demand shifters in the Almost Ideal demand system

Incorporating demand shifters in the Almost Ideal demand system Ecoomcs Letters 70 (2001) 73 78 www.elsever.com/ locate/ ecobase Icorporatg demad shfters the Almost Ideal demad system Jula M. Alsto, James A. Chalfat *, Ncholas E. Pggott a,1 1 a, b a Departmet of Agrcultural

More information

A particle swarm optimization to vehicle routing problem with fuzzy demands

A particle swarm optimization to vehicle routing problem with fuzzy demands A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg, Ye-me Qa A partcle swarm optmzato to vehcle routg problem wth fuzzy demads Yag Peg 1,Ye-me Qa 1 School of computer ad formato

More information

How Euler Did It. One of the most famous formulas in mathematics, indeed in all of science is commonly written in two different ways: i

How Euler Did It. One of the most famous formulas in mathematics, indeed in all of science is commonly written in two different ways: i How Euler Dd It by Ed Sadfer e, π ad : Why s Euler the Euler detty? August 007 Oe of the most famous formulas mathematcs, deed all of scece s commoly wrtte two dfferet ways: e π or e π + 0 Moreover, t

More information

Response surface methodology

Response surface methodology CHAPTER 3 Respose surface methodology 3. Itroducto Respose surface methodology (RSM) s a collecto of mathematcal ad statstcal techques for emprcal model buldg. By careful desg of epermets, the objectve

More information

Finite Difference Method

Finite Difference Method Fte Dfferece Method MEL 87 Computatoa Heat rasfer --4) Dr. Praba audar Assstat Professor Departmet of Mechaca Egeerg II Deh Dscretzato Methods Requred to covert the geera trasport equato to set of agebrac

More information

Compressive Sensing over Strongly Connected Digraph and Its Application in Traffic Monitoring

Compressive Sensing over Strongly Connected Digraph and Its Application in Traffic Monitoring Compressve Sesg over Strogly Coected Dgraph ad Its Applcato Traffc Motorg Xao Q, Yogca Wag, Yuexua Wag, Lwe Xu Isttute for Iterdscplary Iformato Sceces, Tsghua Uversty, Bejg, Cha {qxao3, kyo.c}@gmal.com,

More information

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet

More information

Report 52 Fixed Maturity EUR Industrial Bond Funds

Report 52 Fixed Maturity EUR Industrial Bond Funds Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:

More information

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree , pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal

More information

FINANCIAL MATHEMATICS 12 MARCH 2014

FINANCIAL MATHEMATICS 12 MARCH 2014 FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.

More information

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa

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

Fundamentals of Mass Transfer

Fundamentals of Mass Transfer Chapter Fudametals of Mass Trasfer Whe a sgle phase system cotas two or more speces whose cocetratos are ot uform, mass s trasferred to mmze the cocetrato dffereces wth the system. I a mult-phase system

More information

Research on the Evaluation of Information Security Management under Intuitionisitc Fuzzy Environment

Research on the Evaluation of Information Security Management under Intuitionisitc Fuzzy Environment Iteratoal Joural of Securty ad Its Applcatos, pp. 43-54 http://dx.do.org/10.14257/sa.2015.9.5.04 Research o the Evaluato of Iformato Securty Maagemet uder Itutostc Fuzzy Evromet LI Feg-Qua College of techology,

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

Finite Dimensional Vector Spaces.

Finite Dimensional Vector Spaces. Lctur 5. Ft Dmsoal Vctor Spacs. To b rad to th musc of th group Spac by D.Maruay DEFINITION OF A LINEAR SPACE Dfto: a vctor spac s a st R togthr wth a oprato calld vctor addto ad aothr oprato calld scalar

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