MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY. e S(A)/ da, h N

Size: px
Start display at page:

Download "MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY. e S(A)/ da, h N"

Transcription

1 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY 9 4. Matrx tgrals Lt h N b th spac of Hrmta matrcs of sz N. Th r product o h N s gv by (A, B) = Tr(AB). I ths scto w wll cosdr tgrals of th form Z N = N 2 /2 S(A)/ da, h N whr th Lbsgu masur da s ormalzd by th codto Tr(A2 )/2 da =, ad S(A) = Tr(A 2 )/2 m 0 g mtr(a m )/m s th acto fuctoal. 3 W wll b trstd th bhavor of th coffcts of th xpaso of Z N g for larg N. Th study of ths bhavor wll lad us to cosdrg ot smply Fyma graphs, but actually fat (or rbbo) graphs, whch ar fact 2-dmsoal surfacs. Thus, bfor w procd furthr, w d to do som 2-dmsoal combatoral topology. 4.. Fat graphs. Rcall from th proof of Fyma s thorm that gv a ft collcto of flowrs ad a parg σ o th st T of dpots of thr dgs, w ca obta a graph Γ σ by coctg (or glug) th pots whch fall th sam par. Now, gv a -flowr, lt us scrb t a closd dsk D (so that th ds of th dgs ar o th boudary) ad tak ts small tubular ghborhood D. Ths producs a rgo wth pcws smooth boudary. W wll qup ths rgo wth a ortato, ad call t a fat -valt flowr. Th boudary of a fat -valt flowr has th form P Q P 2 Q 2...P Q P,whr P,Q ar th agl pots, th trvals P j Q j ar arcs o D, ad Q j P j+ ar (smooth) arcs lyg sd D (s Fg. 0). 3-valt flowr fat 3-valt flowr Q P P 2 Q 2 Q 3 P 3 Fgur 0 Now, gv a collcto of usual flowrs ad a parg σ as abov, w ca cosdr th corrspodg fat flowrs, ad glu thm (rspctg th ortato) alog trvals P j Q j accordg to σ. Ths wll produc a compact ortd surfac wth boudary (th boudary s glud from trvals P j Q j+ ). W wll dot ths surfac by Γ σ, ad call t th fattg of Γ wth rspct to σ. A fattg of a graph wll b calld a fat (or rbbo) graph. Thus, a fat graph s ot just a ortd surfac wth boudary, but such a surfac togthr wth a partto of ths surfac to fat flowrs. Not that th sam graph Γ ca hav may dffrt fattgs, ad partcular th gus g of th fattg s ot dtrmd by Γ (s Fg. ) Matrx tgrals larg N lmt ad plaar graphs. Lt us ow rtur to th study of th tgral Z N. By th proof of Fyma s thorm, g (/2 ) l Z N = ( ) F σ,! whr th summato s tak ovr all pargs of T = T () that produc a coctd graph Γ σ,ad F σ dots th cotracto of th tsors Tr(A )usg σ. For a surfac Σ wth boudary, lt ν(σ) dot th umbr of coctd compots of th boudary. = N ν( Γ Proposto 4.. F σ ) σ. 3 Not that w dvd by m ad ot by m!. W wll s blow why such ormalzato wll b mor covt. σ

2 20 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY Γ g =0 Γ 2 g =0 Γ 3 g = Fgur. Glug a fat graph from fat flowrs Proof. Lt b th stadard bass of C N,ad th dual bass. Th th tsor Tr(A m )cab wrtt as N Tr(A m )= ( m,a m ).,..., m= O ca vsualz ach moomal ths sum as a lablg of th agl pots P,Q,...,P m,q m o th boudary of a fat m-valt flowr by, 2, 2, 3,..., m,. Now, th cotracto usg σ of som st of such moomals s ozro ff th subscrpt s costat alog ach boudary compot of Γ σ (s Fg. 2). Ths mpls th rsult. j j k k l m Cotracto ozro ff = r, j = p, j = m, k = r, k = p, = m, that s = r = k = p = j = m. Fgur 2. Cotracto dfd by a fat graph. Lt G c () s th st of somorphsm classs of coctd fat graphs wth -valt vrtcs. For Γ G c (), lt b( Γ) b th umbr of dgs mus th umbr of vrtcs of th udrlyg usual graph Γ. Corollary 4.2. l Z N = ( g ) Γ G c() N ν( Γ) b( Γ). Proof. Lt G fat () = S ( Z/Z). Ths group acts o T,sothat Γ σ = Γ gσ, for ay g G fat (sc cyclc prmutatos of dgs of a flowr xtd to ts fattg). Morovr, th group acts trastvly o th st of σ gvg a fxd fat graph Γ σ, ad th stablzr of ay σ s Aut( Γ σ ). Ths mpls th rsult. Now for ay compact surfac Σ wth boudary, lt g(σ) b th gus of Σ. Th for a coctd fat graph Γ, b( Γ) = 2g( Γ) 2+ν( Γ) (mus th Eulr charactrstc). Thus, dfg ẐN( ) =Z N ( /N ), w fd

3 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY 2 Thorm 4.3. l Ẑ N = ( g ) Γ) b( Γ) N 2 2g(. Γ G c() whr G c ()[0] dots th st of plaar coctd fat graphs,.. thos whch hav gus zro. (2) Morovr, thr xsts a xpaso l Ẑ N /N 2 = g 0 a gn 2g,whr ad G c ()[g] dots th st of coctd fat graphs whch hav gus g Itgrato ovr ral symmtrc matrcs. O may also cosdr th matrx tgral ovr th spac s N of ral symmtrc matrcs of sz N. Namly, o puts N (N +)/4 Z N = S(A)/ da, s N whr S ad da ar as abov. Lt us gralz Thorm 4.4 to ths cas. As bfor, cosdrato of th larg N lmt lads to cosdrato of fat flowrs ad glug of thm. Howvr, th xact atur of glug s ow somwhat dffrt. Namly, th Hrmta cas w had ( j, k l )= δ l δ jk, whch forcd us to glu fat flowrs prsrvg ortato. O th othr had, th ral symmtrc cas =, ad th r product of th fuctoals j o th spac of symmtrc matrcs s gv by ( j, k l )= δ k δ jl + δ l δ jk. Ths mas that bsds th usual (ortato prsrvg) glug of fat flowrs, w ow must allow glug wth a twst of th rbbo by 80 o. Fat graphs thus obtad wll b calld twstd fat graphs. That mas, a twstd fat graph s a surfac wth boudary (possbly ot ortabl), togthr wth a partto to fat flowrs, ad ortatos o ach of thm (whch may or may ot match at th cuts, s Fg. 3). Ths mpls th followg mportat rsult, du to t Hooft. l Z Thorm 4.4. () Thr xsts a lmt W := lm ˆ N N. Ths lmt s gv by th formula N 2 b( Γ) W = ( g ), Γ G c ()[0] b( Γ) a g = ( g ), Γ G c ()[g] Rmark. Gus zro fat graphs ar sad to b plaar bcaus th udrlyg usual graphs ca b put o th 2-sphr (ad hc o th pla) wthout slf-trsctos. Rmark 2. t Hooft s thorm may b trprtd trms of th usual Fyma dagram xpaso. Namly, t mpls that for larg N, th ladg cotrbuto to l(z N ( /N )) coms from th trms th Fyma dagram xpaso corrspodg to plaar graphs (.. thos that admt a mbddg to th 2-sphr). Fgur 3. Twstd-fat graph Now o ca show aalogously to th Hrmta cas that th /N xpaso of l ẐN (whr Z ˆ N = Z N (2 /N )) s gv by th sam formula as bfor, but wth summato ovr th st G tw c () of twstd fat graphs:

4 22 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY Thorm 4.5. l ˆ N 2 2g( Z N = ( g ) Γ) b( Γ). Γ G tw c () Hr th gus g of a (possbly o-ortabl) surfac s dfd by g = χ/2, whr χ s th Eulr charactrstc. Thus th gus of RP 2 s /2, th gus of th Kl bottl s, ad so o. I partcular, w hav th aalog of t Hooft s thorm. Thorm 4.6. () Thr xsts a lmt W := lm N l ˆN. Ths lmt s gv by th formula 4.4. Applcato to a coutg problm. Matrx tgrals ar so rch that v th smplst possbl xampl rducs to a otrval coutg problm. Namly, cosdr th matrx tgral Z N ovr complx Hrmta matrcs th cas S(A) = Tr(A 2 )/2 str(a 2m )/2m, whr s 2 = 0 (.. w work ovr th rg C[s]/(s 2 )). I ths cas w ca st =. Th from Thorm 4.4 w gt Tr(A 2m ) Tr(A2 )/2 da = P m (N), whr P m (N) s a polyomal, gv by th formula P m (N) = g 0 ε g(m)n m+ 2g,ad ε g (m) s th umbr of ways to glu a surfac of gus g from a 2m-go wth labld sds by glug sds prsrvg th ortato. Idd, ths cas w hav oly o fat flowr of valcy 2m, whch has to b glud wth tslf; so a drct applcato of our Fyma ruls lads to coutg ways to glu a surfac of a gv gus from a polygo. Th valu of ths tgral s gv by th followg o-trval thorm. Corollary ( ) 4.8. Th umbr of ways to glu a sphr of a 2m-go s th Catala umbr C m = 2m m+. m Z N 2 b( W = ( g ) Γ G tw ()[0] c Γ), whr G tw c ()[0] dots th st of plaar coctd twstd fat graphs,.. thos whch hav gus zro. (2) Morovr, thr xsts a xpaso l Ẑ N /N 2 = g 0 a gn 2g,whr b( a g = ( g ) Γ G tw c ()[g] Γ), ad G tw c ()[g] dots th st of coctd twstd fat graphs whch hav gus g. Exrcs. Cosdr th matrx tgral ovr th spacq N of quatroc Hrmta matrcs. Show that ths cas th rsults ar th sam as th ral cas, xcpt that ach twstd fat graph couts wth a sg, qual to ( ) m,whr m s th umbr of twstgs (.. msmatchs of ortato at cuts). I othr words, l Ẑ N for quatroc matrcs s qual l Ẑ 2N for ral matrcs wth N rplacd by N. Ht: us that th utary group U(N, H) s a ral form of Sp(2N), ad q N s a ral form of th rprstato of Λ 2 V, whr V s th stadard (vctor) rprstato of Sp(2N). Compar to th cas of ral symmtrc matrcs, whr th rlvat rprstato s S 2 V for O(N), ad th cas of complx Hrmta matrcs, whr t s V V for GL(N). h N Thorm 4.7. (Harr-Zagr, 986) m ( ) P m (x) = (2m)! m x(x )...(x p) 2 p 2 m. m! p (p +)! p=0 Th thorm s provd th xt subsctos. Lookg at th ladg coffct of P m,w gt.

5 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY 23 Corollary 4.8 actually has aothr (lmtary combatoral) proof, whch s as follows. For ach parg σ o th st of sds of th 2m-go, lt us coct th mdpots of th sds that ar pard by straght ls (Fg. 4). It s gomtrcally vdt that f ths ls do t trsct th th glug wll gv a sphr. W clam that th covrs s tru as wll. Idd, w ca assum that th statmt s kow for th 2m 2-go. Lt σ b a glug of th 2m-go that gvs a sphr. If thr s a cocto btw two adjact sds, w may glu thm ad go from a 2m-go to a 2m 2-go (Fg. 5). Thus, t s suffct to cosdr th cas wh adjact sds ar vr coctd. Th thr xst adjact sds a ad b whos ls (coctg thm to som c, d) trsct wth ach othr. Lt us ow rplac σ by aothr parg σ, whos oly dffrc from σ s that a s coctd to b ad c to d (Fg. 6). O ss by spcto (chck t!) that ths dos ot dcras th umbr of boudary compots of th rsultg surfac. Thrfor, sc σ gvs a sphr, so dos σ.but σ has adjact sds coctd, th cas cosdrd bfor, hc th clam. Fgur 4. Parg of sds of a 6-go. 6 * Fgur 5 - Fgur 6 Now t rmas to cout th umbr of ways to coct mdpots of sds wth ls wthout trsctos. Suppos w draw o such l, such that th umbr of sds o th lft of t s 2k ad o th rght s 2l (so that k + l = m ). Th w fac th problm of coctg th two sts of 2k ad 2l sds wthout trsctos. Ths shows that th umbr of glugs D m satsfs th rcurso D m = D k D l k+l=m I othr words, th gratg fucto D m x m = + x + satsfs th quato f = xf 2.Ths mpls that f = 4x 2x, whch ylds that D m = C m. W ar do. Corollary 4.8 ca b usd to drv th th followg fudamtal rsult from th thory of radom matrcs, dscovrd by Wgr 955.

6 24 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY Thorm 4.9. (Wgr s smcrcl law) Lt f b a cotuous fucto o R of at most polyomal growth at fty. Th lm Trf (A/ N ) Tr(A2 )/2 2 f (x) 4 x 2 dx. N N h N 2π 2 Ths thorm s calld th smcrcl law bcaus t says that th graph of th dsty of gvalus of a larg radom Hrmta matrx dstrbutd accordg to th Gaussa utary smbl (.. wth dsty Tr(A2 )/2 da) s a smcrcl. Proof. By Wrstrass uform approxmato thorm, w may assum that f s a polyomal. (Exrcs: Justfy ths stp). Thus, t suffcs to chck th rsult f f (x) = x 2m. I ths cas, by Corollary 4.8, th lft had sd 2 s C m. O th othr had, a lmtary computato ylds 2π 2 x 2m 4 x 2 = C m, whch mpls th thorm Hrmt polyomals. Th proof 4 of Thorm 4.7 gv blow uss Hrmt polyomals. So lt us rcall thr proprts. Hrmt s polyomals ar dfd by th formula x 2 d x H 2 (x) = ( ). dx So th ladg trm of H (x) s (2x). W collct th stadard proprts of H (x) th followg thorm. Thorm 4.0. () Th gratg fucto of H (x) s f (x, t) = H (x) t 0! =. 2xt t2 () H (x) satsfy th dffrtal quato f 2xf +2f =0. I othr words, H (x) x 2 /2 ar gfuctos of th oprator L = 2 + x 2 (Hamltoa of th quatum harmoc oscllator) wth 2 2 gvalus +. 2 () H (x) ar orthogoal: (v) O has (f k > m, th aswr s zro). (v) O has x 2 H m (x)h (x)dx = 2!δ m π x 2 x 2m (2m)! H 2k (x)dx = (m k)! π H r r 2 (x) r! = H 2k (x). 2 r r! 2 k k! 2 (r k)! k=0 2 2(k m) Proof. (sktch) () Follows mmdatly from th fact that th oprator d ( ) t! dx maps a fucto g(x) to g(x t). () Follows from () ad th fact that th fucto f (x, t) satsfs th PDE f xx 2xf x +2tf t =0. () Follows from () by drct tgrato (o should comput R f (x, t)f (x, u) x 2 dx usg a shft of coordat). 2m (v) By (), o should calculat R x x 2 dx. Ths tgral quals 2xt t2 ( ) 2m (2m 2p)! x 2m (x t)2 dx = (y + t) 2m y 2 dy == π t 2p 2p 2 m p. (m p)! R R p Th rsult s ow obtad by xtractg dvdual coffcts. 4 I adoptd ths proof from D. Jackso s ots

7 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY 25 (v) By (), t suffcs to show that 2 r+k H 2 r (x)h 2k (x) x 2 r!2 (2k)! dx = k! 2 (r k)! π R To prov ths dtty, lt us tgrat th product of thr gratg fuctos. By (), w hav f (x, t)f (x, u)f (x, v) x 2 dx = 2(tu+tv+uv). π R Extractg th coffct of t r u r v 2k, w gt th rsult Proof of Thorm 4.7. W d to comput th tgral Tr(A 2m ) Tr(A2 )/2 da. h N To do ths, w ot that th tgrad s varat wth rspct to cojugato by utary matrcs. Thrfor, th tgral ca b rducd to a tgral ovr th gvalus λ,...,λ N of A. Mor prcsly, cosdr th spctrum map σ : h N R N /S N. It s wll kow (du to H. Wyl) that th drct mag σ /2 da s gv by th formula σ da = C P λ 2 <j (λ λ j ) 2 dλ, whr C> 0 s a ormalzato costat that wll ot b rlvat to us. Thus, w hav P ( λ 2m ) λ2 /2 P m (N) = RN <j (λ λ j ) 2 dλ P λ 2 R /2 N <j (λ λ j ) 2 dλ To calculat th tgral J m th umrator, w wll us Hrmt s polyomals. Obsrv that sc H (x) ar polyomals of dgr wth hghst coffct 2, w hav <j (λ λ j ) = 2 N (N )/2 dt(h k (λ )), whr k rus through th st 0,,...,. Thus, w fd P J m := ( λ 2m ) λ2 /2 (λ λ j ) 2 dλ = R N <j 2 m+n 2 /2 N λ 2m P λ 2 (λ λ j ) 2 dλ = (2) P 2 m N (N 2)/2 2m λ2 N λ dt(hk (λ j )) 2 dλ = R N P 2 m N (N 2)/2 2m λ2 N λ ( ( ) σ ( ) τ H σ (λ )H τ (λ ))dλ. (Hr ( ) σ dots th sg of σ). Sc Hrmt polyomals ar orthogoal, th oly trms whch ar ozro ar th trms wth σ() = τ () for = 2,...,N. That s, th ozro trms hav σ = τ. Thus, w hav P J λ 2m λ2 m =2 m N (N 2)/2 N ( Hσ (λ ) 2 dλ = R N R N σ,τ S N R N σ S N (3) N x 2 m N (N 2)/2 N!γ 0... γ N x 2m H j (x) 2 2 dx, j=0 whr γ = H (x) 2 x 2 dx ar th squard orms of th Hrmt polyomals. Applyg ths for m = 0 ad dvdg J m by J 0, w fd J m /J 0 =2 m γ j N x x 2m H j (x) 2 2 γ j=0 j Usg Thorm 4.0 () ad (v), w fd: γ =2! π, ad hc N <j dx j 2 m x 2m H 2k (x) J m /J 0 = x 2 dx π R j=0 2 k k! 2 (j k)! k=0

8 26 MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY Now, usg (v), w gt (2m)! N j 2 k j! J m /J 0 = = 2 m (m k)!k! 2 (j k)! j=0 k=0 )( ) (2m)! N j ( m j 2 k 2 m m! k k. j=0 k=0 Th sum ovr k ca b rprstd as a costat trm of a polyomal: j ( )( ) 2 k m j = C.T.(( + z) m ( + 2z ) k ). k k k=0 Thrfor, summato ovr j (usg th formula for th sum of th gomtrc progrsso) ylds ( ) (2m)! C.T.(( + z) m ( + 2z ) N (2m)! m )= 2 p m )( N J m /J 0 = 2m m! 2z 2 m m! p p + p=0 W ar do.

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Superbosonization meets Free Probability

Superbosonization meets Free Probability Suprbosoato mts Fr Probablty M Zrbaur jot wor wth S Madt Eulr Symposum St Ptrsburg Ju 3 009 Itroducto From momts to cumulats Larg- charactrstc fucto by fr probablty Suprbosoato Applcato to dsordrd scattrg

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Almost all Cayley Graphs Are Hamiltonian

Almost all Cayley Graphs Are Hamiltonian Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

Phase diagram and frustration of decoherence in Y-shaped Josephson junction networks. D.Giuliano(Cosenza), P. Sodano(Perugia)

Phase diagram and frustration of decoherence in Y-shaped Josephson junction networks. D.Giuliano(Cosenza), P. Sodano(Perugia) Phas dagram ad frustrato of dcohrc Y-shapd Josphso jucto tworks D.GulaoCosza, P. SodaoPruga Frz, Frz, Octobr Octobr 008 008 Ma da Y-Shapd twork of Josphso jucto chas YJJN wth a magtc frustrato Ft-couplg

More information

Section 5.1/5.2: Areas and Distances the Definite Integral

Section 5.1/5.2: Areas and Distances the Definite Integral Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w

More information

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since 56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations Appld Mathmatcal Sccs ol. 9 5 o. 43 75-73 HKAR Ltd www.m-hkar.com http://dx.do.org/.988/ams.5.567 Thr-Dmsoal Thory of Nolar-Elastc Bods Stablty udr Ft Dformatos Yu.. Dmtrko Computatoal Mathmatcs ad Mathmatcal

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

Different types of Domination in Intuitionistic Fuzzy Graph

Different types of Domination in Intuitionistic Fuzzy Graph Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,

More information

On the Possible Coding Principles of DNA & I Ching

On the Possible Coding Principles of DNA & I Ching Sctfc GOD Joural May 015 Volum 6 Issu 4 pp. 161-166 Hu, H. & Wu, M., O th Possbl Codg Prcpls of DNA & I Chg 161 O th Possbl Codg Prcpls of DNA & I Chg Hupg Hu * & Maox Wu Rvw Artcl ABSTRACT I ths rvw artcl,

More information

On Approximation Lower Bounds for TSP with Bounded Metrics

On Approximation Lower Bounds for TSP with Bounded Metrics O Approxmato Lowr Bouds for TSP wth Boudd Mtrcs Mark Karpsk Rchard Schmd Abstract W dvlop a w mthod for provg xplct approxmato lowr bouds for TSP problms wth boudd mtrcs mprovg o th bst up to ow kow bouds.

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by D. Klain Vrsion 207.0.05 Corrctions and commnts ar wlcom. Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix A A k I + A + k!

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by Dan Klain Vrsion 28928 Corrctions and commnts ar wlcom Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix () A A k I + A + k!

More information

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.

More information

Week 3: Connected Subgraphs

Week 3: Connected Subgraphs Wk 3: Connctd Subgraphs Sptmbr 19, 2016 1 Connctd Graphs Path, Distanc: A path from a vrtx x to a vrtx y in a graph G is rfrrd to an xy-path. Lt X, Y V (G). An (X, Y )-path is an xy-path with x X and y

More information

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto   {t-asano, School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp

More information

Integral points on hyperbolas over Z: A special case

Integral points on hyperbolas over Z: A special case Itgral pots o hprbolas ovr Z: A spcal cas `Pag of 7 Kostat Zlator Dpartmt of Mathmatcs ad Computr Scc Rhod Islad Collg 600 Mout Plasat Avu Provdc, R.I. 0908-99, U.S.A. -mal addrss: ) Kzlator@rc.du ) Kostat_zlator@ahoo.com

More information

cycle that does not cross any edges (including its own), then it has at least

cycle that does not cross any edges (including its own), then it has at least W prov th following thorm: Thorm If a K n is drawn in th plan in such a way that it has a hamiltonian cycl that dos not cross any dgs (including its own, thn it has at last n ( 4 48 π + O(n crossings Th

More information

SECTION where P (cos θ, sin θ) and Q(cos θ, sin θ) are polynomials in cos θ and sin θ, provided Q is never equal to zero.

SECTION where P (cos θ, sin θ) and Q(cos θ, sin θ) are polynomials in cos θ and sin θ, provided Q is never equal to zero. SETION 6. 57 6. Evaluation of Dfinit Intgrals Exampl 6.6 W hav usd dfinit intgrals to valuat contour intgrals. It may com as a surpris to larn that contour intgrals and rsidus can b usd to valuat crtain

More information

' 1.00, has the form of a rhomb with

' 1.00, has the form of a rhomb with Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC

More information

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES AYMPTOTIC AD TOLERACE D-MODELLIG I ELATODYAMIC OF CERTAI THI-WALLED TRUCTURE B. MICHALAK Cz. WOŹIAK Dpartmt of tructural Mchacs Lodz Uvrsty of Tchology Al. Poltrchk 6 90-94 Łódź Polad Th objct of aalyss

More information

PURE MATHEMATICS A-LEVEL PAPER 1

PURE MATHEMATICS A-LEVEL PAPER 1 -AL P MATH PAPER HONG KONG EXAMINATIONS AUTHORITY HONG KONG ADVANCED LEVEL EXAMINATION PURE MATHEMATICS A-LEVEL PAPER 8 am am ( hours) This papr must b aswrd i Eglish This papr cosists of Sctio A ad Sctio

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

1985 AP Calculus BC: Section I

1985 AP Calculus BC: Section I 985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b

More information

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

Ordinary Least Squares at advanced level

Ordinary Least Squares at advanced level Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 07 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.

More information

Self-Adjointness and Its Relationship to Quantum Mechanics. Ronald I. Frank 2016

Self-Adjointness and Its Relationship to Quantum Mechanics. Ronald I. Frank 2016 Ronald I. Frank 06 Adjoint https://n.wikipdia.org/wiki/adjoint In gnral thr is an oprator and a procss that dfin its adjoint *. It is thn slf-adjoint if *. Innr product spac https://n.wikipdia.org/wiki/innr_product_spac

More information

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach Po Producto va Proto Sychrotro Radato Strog Magtc Flds Rlatvstc Quatum Approach Partcl Productos TV Ergy Rgo Collaborators Toshtaka Kajo Myog-K Chou Grad. J. MATHEWS Tomoyuk Maruyama BRS. Nho Uvrsty NaO,

More information

Chapter Discrete Fourier Transform

Chapter Discrete Fourier Transform haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd

More information

Introduction to Arithmetic Geometry Fall 2013 Lecture #20 11/14/2013

Introduction to Arithmetic Geometry Fall 2013 Lecture #20 11/14/2013 18.782 Introduction to Arithmtic Gomtry Fall 2013 Lctur #20 11/14/2013 20.1 Dgr thorm for morphisms of curvs Lt us rstat th thorm givn at th nd of th last lctur, which w will now prov. Thorm 20.1. Lt φ:

More information

Mor Tutorial at www.dumblittldoctor.com Work th problms without a calculator, but us a calculator to chck rsults. And try diffrntiating your answrs in part III as a usful chck. I. Applications of Intgration

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 401 Digital Signal Procssing Prof. Mark Fowlr Dtails of th ot St #19 Rading Assignmnt: Sct. 7.1.2, 7.1.3, & 7.2 of Proakis & Manolakis Dfinition of th So Givn signal data points x[n] for n = 0,, -1

More information

Basic Polyhedral theory

Basic Polyhedral theory Basic Polyhdral thory Th st P = { A b} is calld a polyhdron. Lmma 1. Eithr th systm A = b, b 0, 0 has a solution or thr is a vctorπ such that π A 0, πb < 0 Thr cass, if solution in top row dos not ist

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

Addition of angular momentum

Addition of angular momentum Addition of angular momntum April, 0 Oftn w nd to combin diffrnt sourcs of angular momntum to charactriz th total angular momntum of a systm, or to divid th total angular momntum into parts to valuat th

More information

10. Limits involving infinity

10. Limits involving infinity . Limits involving infinity It is known from th it ruls for fundamntal arithmtic oprations (+,-,, ) that if two functions hav finit its at a (finit or infinit) point, that is, thy ar convrgnt, th it of

More information

(1) Then we could wave our hands over this and it would become:

(1) Then we could wave our hands over this and it would become: MAT* K285 Spring 28 Anthony Bnoit 4/17/28 Wk 12: Laplac Tranform Rading: Kohlr & Johnon, Chaptr 5 to p. 35 HW: 5.1: 3, 7, 1*, 19 5.2: 1, 5*, 13*, 19, 45* 5.3: 1, 11*, 19 * Pla writ-up th problm natly and

More information

Suzan Mahmoud Mohammed Faculty of science, Helwan University

Suzan Mahmoud Mohammed Faculty of science, Helwan University Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION

More information

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling Tripl Play: From D Morga to Stirlig To Eulr to Maclauri to Stirlig Augustus D Morga (186-1871) was a sigificat Victoria Mathmaticia who mad cotributios to Mathmatics History, Mathmatical Rcratios, Mathmatical

More information

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black Stf-Boltzm lw stts tht th powr mttd pr ut r of th surfc of blck body s proportol to th fourth powr of th bsolut tmprtur: 4 S T whr T s th bsolut tmprtur d th Stf-Boltzm costt= 5 4 k B 3 5c h ( Clcult 5

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

Estimation Theory. Chapter 4

Estimation Theory. Chapter 4 Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,

More information

Graphs of q-exponentials and q-trigonometric functions

Graphs of q-exponentials and q-trigonometric functions Grahs of -otals ad -trgoomtrc fuctos Amla Carola Saravga To ct ths vrso: Amla Carola Saravga. Grahs of -otals ad -trgoomtrc fuctos. 26. HAL Id: hal-377262 htts://hal.archvs-ouvrts.fr/hal-377262

More information

MA 524 Homework 6 Solutions

MA 524 Homework 6 Solutions MA 524 Homework 6 Solutos. Sce S(, s the umber of ways to partto [] to k oempty blocks, ad c(, s the umber of ways to partto to k oempty blocks ad also the arrage each block to a cycle, we must have S(,

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Frequency hopping sequences with optimal partial Hamming correlation

Frequency hopping sequences with optimal partial Hamming correlation 1 Frqucy hoppg squcs wth optmal partal Hammg corrlato Jgju Bao ad ju J arxv:1511.02924v2 [cs.it] 11 Nov 2015 Abstract Frqucy hoppg squcs (FHSs) wth favorabl partal Hammg corrlato proprts hav mportat applcatos

More information

Volumes of Solids of Revolution via Summation Methods

Volumes of Solids of Revolution via Summation Methods olums of Solds of Rvoluto va Summato Mthods Tlak d Alws talws@slu.du Dpartmt of Mathmats Southastr Lousaa Uvrsty Hammod, LA 70403 USA Astrat: I ths papr, w wll show how to alulat volums of rta solds of

More information

Recall that by Theorems 10.3 and 10.4 together provide us the estimate o(n2 ), S(q) q 9, q=1

Recall that by Theorems 10.3 and 10.4 together provide us the estimate o(n2 ), S(q) q 9, q=1 Chaptr 11 Th singular sris Rcall that by Thorms 10 and 104 togthr provid us th stimat 9 4 n 2 111 Rn = SnΓ 2 + on2, whr th singular sris Sn was dfind in Chaptr 10 as Sn = q=1 Sq q 9, with Sq = 1 a q gcda,q=1

More information

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series Chatr Ifiit Sris Pag of Sctio F Itgral Tst Chatr : Ifiit Sris By th d of this sctio you will b abl to valuat imror itgrals tst a sris for covrgc by alyig th itgral tst aly th itgral tst to rov th -sris

More information

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT Iraa Joral of Mathatcal Chstry Vol No Dcbr 0 09 7 IJMC Two Tys of Gotrc Arthtc dx of V hylc Naotb S MORADI S BABARAHIM AND M GHORBANI Dartt of Mathatcs Faclty of Scc Arak Ursty Arak 856-8-89 I R Ira Dartt

More information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120 Tim : hr. Tst Papr 8 D 4//5 Bch - R Marks : SINGLE CORRECT CHOICE TYPE [4, ]. If th compl umbr z sisfis th coditio z 3, th th last valu of z is qual to : z (A) 5/3 (B) 8/3 (C) /3 (D) o of ths 5 4. Th itgral,

More information

COHORT MBA. Exponential function. MATH review (part2) by Lucian Mitroiu. The LOG and EXP functions. Properties: e e. lim.

COHORT MBA. Exponential function. MATH review (part2) by Lucian Mitroiu. The LOG and EXP functions. Properties: e e. lim. MTH rviw part b Lucian Mitroiu Th LOG and EXP functions Th ponntial function p : R, dfind as Proprtis: lim > lim p Eponntial function Y 8 6 - -8-6 - - X Th natural logarithm function ln in US- log: function

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Lie Groups HW7. Wang Shuai. November 2015

Lie Groups HW7. Wang Shuai. November 2015 Li roups HW7 Wang Shuai Novmbr 015 1 Lt (π, V b a complx rprsntation of a compact group, show that V has an invariant non-dgnratd Hrmitian form. For any givn Hrmitian form on V, (for xampl (u, v = i u

More information

On the irreducibility of some polynomials in two variables

On the irreducibility of some polynomials in two variables ACTA ARITHMETICA LXXXII.3 (1997) On th irrducibility of som polynomials in two variabls by B. Brindza and Á. Pintér (Dbrcn) To th mmory of Paul Erdős Lt f(x) and g(y ) b polynomials with intgral cofficints

More information

Section 11.6: Directional Derivatives and the Gradient Vector

Section 11.6: Directional Derivatives and the Gradient Vector Sction.6: Dirctional Drivativs and th Gradint Vctor Practic HW rom Stwart Ttbook not to hand in p. 778 # -4 p. 799 # 4-5 7 9 9 35 37 odd Th Dirctional Drivativ Rcall that a b Slop o th tangnt lin to th

More information

Combinatorial Networks Week 1, March 11-12

Combinatorial Networks Week 1, March 11-12 1 Nots on March 11 Combinatorial Ntwors W 1, March 11-1 11 Th Pigonhol Principl Th Pigonhol Principl If n objcts ar placd in hols, whr n >, thr xists a box with mor than on objcts 11 Thorm Givn a simpl

More information

Solution to 1223 The Evil Warden.

Solution to 1223 The Evil Warden. Solutio to 1 Th Evil Ward. This is o of thos vry rar PoWs (I caot thik of aothr cas) that o o solvd. About 10 of you submittd th basic approach, which givs a probability of 47%. I was shockd wh I foud

More information

Folding of Regular CW-Complexes

Folding of Regular CW-Complexes Ald Mathmatcal Scncs, Vol. 6,, no. 83, 437-446 Foldng of Rgular CW-Comlxs E. M. El-Kholy and S N. Daoud,3. Dartmnt of Mathmatcs, Faculty of Scnc Tanta Unvrsty,Tanta,Egyt. Dartmnt of Mathmatcs, Faculty

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: Error Correcting Codes Lab March 2, Lecture 8 18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse

More information

On new theta identities of fermion correlation functions on genus g Riemann surfaces

On new theta identities of fermion correlation functions on genus g Riemann surfaces O w thta dtts o rmo corrlato uctos o us Rma suracs A.G. Tsucha. Oct. 7 Last rvsd o ov. 7 Abstract Thta dtts o us Rma suracs whch dcompos smpl products o rmo corrlato uctos wth a costrat o thr varabls ar

More information

Supplementary Materials

Supplementary Materials 6 Supplmntary Matrials APPENDIX A PHYSICAL INTERPRETATION OF FUEL-RATE-SPEED FUNCTION A truck running on a road with grad/slop θ positiv if moving up and ngativ if moving down facs thr rsistancs: arodynamic

More information

Construction of asymmetric orthogonal arrays of strength three via a replacement method

Construction of asymmetric orthogonal arrays of strength three via a replacement method isid/ms/26/2 Fbruary, 26 http://www.isid.ac.in/ statmath/indx.php?modul=prprint Construction of asymmtric orthogonal arrays of strngth thr via a rplacmnt mthod Tian-fang Zhang, Qiaoling Dng and Alok Dy

More information

Discrete Fourier Transform (DFT)

Discrete Fourier Transform (DFT) Discrt Fourir Trasorm DFT Major: All Egirig Majors Authors: Duc guy http://umricalmthods.g.us.du umrical Mthods or STEM udrgraduats 8/3/29 http://umricalmthods.g.us.du Discrt Fourir Trasorm Rcalld th xpotial

More information

A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045)

A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045) FACTA UNIVERSITATIS Srs: Mcacs Automatc Cotrol ad Rootcs Vol 4 N o 6 4 pp 33-39 A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 59645 Prdrag M Raovć Momr S Staovć Slađaa D Marovć 3 Dpartmt

More information

Differentiation of Exponential Functions

Differentiation of Exponential Functions Calculus Modul C Diffrntiation of Eponntial Functions Copyright This publication Th Northrn Albrta Institut of Tchnology 007. All Rights Rsrvd. LAST REVISED March, 009 Introduction to Diffrntiation of

More information

1973 AP Calculus BC: Section I

1973 AP Calculus BC: Section I 97 AP Calculus BC: Scio I 9 Mius No Calculaor No: I his amiaio, l dos h aural logarihm of (ha is, logarihm o h bas ).. If f ( ) =, h f ( ) = ( ). ( ) + d = 7 6. If f( ) = +, h h s of valus for which f

More information

Inner Product Spaces INNER PRODUCTS

Inner Product Spaces INNER PRODUCTS MA4Hcdoc Ir Product Spcs INNER PRODCS Dto A r product o vctor spc V s ucto tht ssgs ubr spc V such wy tht th ollowg xos holds: P : w s rl ubr P : P : P 4 : P 5 : v, w = w, v v + w, u = u + w, u rv, w =

More information

Quasi-Classical States of the Simple Harmonic Oscillator

Quasi-Classical States of the Simple Harmonic Oscillator Quasi-Classical Stats of th Simpl Harmonic Oscillator (Draft Vrsion) Introduction: Why Look for Eignstats of th Annihilation Oprator? Excpt for th ground stat, th corrspondnc btwn th quantum nrgy ignstats

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information