FORBIDDING RAINBOW-COLORED STARS

Size: px
Start display at page:

Download "FORBIDDING RAINBOW-COLORED STARS"

Transcription

1 FORBIDDING RAINBOW-COLORED STARS CARLOS HOPPEN, HANNO LEFMANN, KNUT ODERMANN, AND JULIANA SANCHES Abstract. W cosidr a xtrmal problm motivatd by a papr of Balogh [J. Balogh, A rmark o th umbr of dg colorigs of graphs, Europa Joural of Combiatorics 7, 006, ], who cosidrd dg-colorigs of graphs avoidig fixd subgraphs with a prscribd colorig. Mor prcisly, giv r t, w look for -vrtx graphs that admit th maximum umbr of r-dg-colorigs such that at most t 1 colors appar i dgs icidt with ach vrtx. For larg, w show that, with th xcptio of th cas t =, th complt graph K is always th uiqu xtrmal graph. W also cosidr gralizatios of this problm. 1. Itroductio W cosidr dg-colorigs of graphs that satisfy a crtai proprty. Giv a umbr r of colors ad a graph F, a r-pattr P of F is a partitio of its dg st ito r (possibly mpty) classs. A dg-colorig (ot cssarily propr) of a host graph H is said to b (F, P )-fr if H dos ot cotai a copy of F i which th partitio of th dg st iducd by th colorig is isomorphic to P. If at most r colors ar usd, w call it a (F, P )-fr r-colorig of H. For xampl, if th pattr of F cosists of a sigl class, o moochromatic copy of F should aris i H. W ask for th -vrtx host graphs H (amog all -vrtx graphs) which allow th largst umbr of (F, P )-fr r-colorigs. Qustios of this typ hav b first cosidrd by Erdős ad Rothschild [6], who askd whthr cosidrig dg-colorigs avoidig a moochromatic copy of F would lad to xtrmal cofiguratios that ar substatially diffrt from thos of th Turá problm. Idd, F -fr graphs o vrtics ar atural cadidats for admittig a larg umbr of colorigs, sic ay r-colorig of thir dg st obviously dos ot produc a moochromatic copy of F (or a copy of F with ay giv pattr, for that mattr), so that (Turá) F -xtrmal graphs admit r x(,f ) such colorigs, whr, as usual, x(, F ) is th maximum umbr of dgs i a -vrtx F -fr graph. Erdős ad Rothschild [6] cojcturd that, for vry l 3 ad > 0 (l), ay -vrtx graphs with th largst umbr of K l -fr -colorigs is isomorphic to th (l 1)-partit Turá graph, which was prov for l = 3 by Yustr [13] ad for l 4 by Alo, Balogh, Kvash, ad Sudakov [1], who also showd that th sam coclusio holds i th cas r = 3. Howvr, for r 4 colors, th Turá graph for K l is o logr optimal, ad th situatio bcoms much mor complicatd; i fact, xtrmal cofiguratios ar ot kow ulss r = 4 ad F {K 3, K 4 }, s Pikhurko ad Yilma [11]. A similar phomo, i which (Turá) xtrmal graphs admit th largst umbr of r-colorigs if r {, 3}, but do ot for r 4, has b obsrvd for a fw othr classs of graphs ad hyprgraphs, such as th 3-uiform Fao pla [10]. (S [8] for a mor dtaild accout of istacs whr this phomo holds.) Balogh [] was th first to cosidr colorigs avoidig fixd pattrs that ar ot moochromatic. Mor prcisly, h showd that th (l 1)-partit Turá graph is still optimal for r = colors wh forbiddig ay -pattr of K l. O th othr had, h obsrvd that this dos This work was partially supportd by CAPES ad DAAD via Probral (CAPES Proc. 408/13 ad DAAD 56677). Th first author also ackowldgs th support by CNPq (Proc /01-0 ad /01-). Th fourth author was fudd y CNPq. 1

2 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES ot hold i gral for r = 3 colors ad arbitrary 3-pattrs of K l. Idd, cosidr F = K 3 ad lt P b a partitio of K 3 ito thr classs cotaiig o dg ach, so that w ar lookig for 3-colorigs with o raibow triagl. If w color th complt graph K with ay two of th thr colors availabl, thr is o raibow copy of K 3, which givs at last 3 ( ) 3 distict (K3, P )-fr colorigs, ad is mor tha 3 x(,k 3) = 3 /4+O(1). This suggsts that th study of colorigs that avoid gral pattrs, ad i particular raibow pattrs, dsrvs mor atttio. I coctio with this, w should mtio that it was rctly prov that th complt graph is idd optimal for raibow triagls [4]. Thr has also b a xtsiv dscriptio of xtrmal graphs wh o forbids matchigs with various forbidd pattrs [9], which icluds all raibow cass. Stars hav playd a importat rôl i ths dvlopmts. Moochromatic stars F = S t with t 3 dgs wr th first istacs for which it was show [8] that F -xtrmal graphs (i this cas, (t 1)-rgular graphs for v) do ot admit th largst umbr of r-colorigs with o moochromatic copy of F for ay fixd r. I particular, this implis that this trasitio btw th cass r {, 3} ad r 4 dscribd abov dos ot hold for arbitrary graphs F. O th othr had, xtrmal -vrtx graphs for forbidd moochromatic S t ar ot yt kow for ay r ad t 3. I this papr, our iitial motivatio was to study r-colorigs that avoid raibow-colord stars S t, that is, w lt F = S t ad w cosidr th pattr whr ach dg is i a diffrt class (i particular, r t). For t = ad ay giv umbr of colors r, it is asy to s that a matchig of siz / yilds th largst umbr of r-colorigs with o raibow S, as this rstrictio implis that ay colorig must hav moochromatic compots (for odd, both a additioal isolatd vrtx ad a coctd compot with thr vrtics grat a xtrmal cofiguratio). Th sam xtrmal cofiguratio had b obsrvd for moochromatic S wh r =, but ot for largr valus of r. Not that th st of r-colorigs avoidig a moochromatic S is prcisly th st of propr r-dg-colorigs of a graph, ad hc this problm cosists of fidig -vrtx graphs with th largst umbr of propr colorigs. Howvr, i cotrast to th moocromatic cas, w maagd to fid th optimal cofiguratio for larg ad vry fixd r, t 3, which, i all cass, turs out b th complt graph K. Sic th tchiqus usd to prov this rsult may b adaptd to othr pattrs, w stat our rsults i gratr grality. I particular, to driv this gralizatio, w show that i ay graph with may dgs, thr is a almost spaig subgraph with a larg umbr of subgraphs of ay boudd dgr squc satisfyig a dsity costrait, which sms to b of idpdt itrst (s Lmma 3.1 for a prcis formulatio). A dg-colord star S tl with tl dgs such that t distict colors ar ach assigd to xactly l dgs is calld a raibow-s t,l. Giv itgrs r, t ad l 1, ad a graph G, a raibow-s t,l -fr r-colorig of G is a dg-colorig of G with colors i [r] = {1,..., r} for which thr is o raibow-s t,l. Clarly, if l = 1, w forbid raibow stars S t, ad w call such colorigs raibow-s t -fr. For ay graph G, lt C r,t,l (G) b th st of all raibow-s t,l -fr r-colorigs of G. W writ c r,t,l () = max { C r,t,l (G) : V (G) = }, ad w say that a -vrtx graph G is C r,t,l -xtrmal if C r,t,l (G) = c r,t,l (). W prov th followig rsult. Thorm 1.1. For all r, t 3 ad l 1, thr xists 0 such that, for all 0, w hav c r,t,l () = C r,t,l (K ). Morovr, th complt graph K is th sigl C r,t,l -xtrmal graph o vrtics.

3 FORBIDDING RAINBOW-COLORED STARS 3 Th rmaidr of this work is orgaizd as follows. I Sctio, w dal with som asy cass ad w prov our rsult for raibow stars, which givs a ovrviw of th gral cas. Th proof for gral l is th subjct of Sctio 3.. Colorigs avoidig a raibow star Th mai objctiv of this sctio is to prov Thorm 1.1 i th cas l = 1. This cas was th mai motivatio for our work ad, as it turs out, its proof givs a accurat ovrviw of th gral cas. Bfor doig this, w first dal with som straightforward cass. Rcall that, for t = ad l = 1, th r-colorigs of a graph G avoidig a raibow-s ar such that adjact dgs hav th sam color. I such a graph dgs i th sam compot hav to b colord th sam, but dgs i diffrt compots might b colord diffrtly. Thus, if th graph G has j compots cotaiig at last o dg, w hav C r,,1 (G) = r j. I ordr to maximiz this, j has to b as larg as possibl. Hc, th umbr of such colorigs is at most C r,,1 (M), whr M is a maximum matchig i G. So th oly C r,,1 -xtrmal graphs o vrtics ar for v a matchig of siz /, ad, for odd, a matchig of siz ( 1)/ ad a isolatd vrtx, or a matchig of siz ( 3)/ ad vrtx-disjoit coctd compot o thr vrtics. Clarly i this cas w hav c r,,1 () = r. For r < t, o r-colorig ca produc a raibow-s t,l, so that c r,t,l () = r ( ), ad K is th oly C r,t,l -xtrmal graph. Usig this argumt mor carfully, w may xtd this coclusio for som additioal valus of r ad t. Lmma.1. Lt t 3 r t 3, l 1 ad b positiv itgrs. Th c r,t,l () = C r,t,l (K ) ad th complt graph K is uiqu with this proprty amog all -vrtx graphs. Proof. Lt G b a -vrtx graph whr som dg = {v, w} is missig. Cosidr a fixd raibow-s t,l -fr r-colorig of G. W show that w ca xtd to a colorig of G = G +. Lt S v ad S w b th sts of colors occurig o at last l dgs icidt with v ad w, rspctivly, hc S v, S w t 1. If S v S w, th w ca xtd to G by colorig with ay color i S v S w. Now lt S v S w =, i particular S v + S w r t 3. If t 1 = S v > S w, w ca color with ay color i S v. If S v, S w < t 1, th w ca color with ay color. I coclusio, ca b xtdd to a colorig of G. To fiish th proof, w show that at last o of th colorigs of G may b xtdd to G i mor tha o way. As t 3, ay moochromatic colorig of G ca b xtdd to a colorig of G + by colorig with ay color, so that C r,t,l (G) < C r,t,l (G + ). Rmark: Th proof of Lmma.1 also yilds th followig for ay r t 3 ad l. If r(l 1)/ + l(t 1) + 1, th it is possibl to xtd ay colorig to a missig dg {v, w}, v if S v S w = ad S v = S w = t 1. Idd, if a colorig caot b xtdd udr such coditios, th colors i S v must hav b assigd to xactly l 1 dgs icidt with w, ad vic-vrsa. Morovr, ay color i S v S w must appar at l 1 dgs icidt with v or l 1 dgs icidt with w, othrwis it could b usd to xtd th colorig. Howvr, th dgrs of v ad w (which ar at most ) ar too small for all of ths coditios to hold bcaus of our boud o. W ow focus o th proof of Thorm 1.1 i th cas l = 1. Th gral ida of th proof is as follows. Cosidr a fixd raibow-s t -fr r-dg colorig of a graph G. By dfiitio, for vry vrtx v of G, th umbr of colors apparig o dgs icidt with v is at most (t 1). For sts S 1,..., S [r], lt C r,t,(s1,...,s )(G) dot th st of all dg-colorigs of G whr o dgs icidt with vrtx v i ar assigd colors from th st [r] \ S i, for all i = 1...,.

4 4 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES Th th st C r,t (G) of all raibow-s t,l -fr r-colorigs of G satisfis C r,t (G) = (S 1,...,S ) S i =t 1,,..., C r,t,(s1,...,s )(G). (1) Obsrv that th uio is ot disjoit, as fwr tha t 1 colors could appar i dgs icidt with som vrtx. Bfor procdig, ot that this dcompositio ca b asily gralizd to l. Th diffrc, for a fixd raibow-s t,l -fr r-dg colorig of a graph G, is that th sts S i cotai th colors that appar at last l tims i dgs icidt with v i, which w call ordiary colors with rspct to v i, whil th rmaiig colors ar said to b rar for v i. I aalogy to th abov cas, C r,t,l,(s1,...,s )(G) dots th st of all dg-colorigs of G whr fwr tha l dgs icidt with vrtx v i ar assigd ach color from th st [r] \ S i, for all i = 1...,. As i (1), th st C r,t,l (G) of all raibow-s t,l -fr r-colorigs of G satisfis C r,t,l (G) = (S 1,...,S ) S i =t 1,,..., C r,t,l,(s1,...,s )(G). () Our proof cosists of four stps. W first show that ay xtrmal graph must hav a lot of dgs, as othrwis it caot bat th umbr of colorigs achivd by th complt graph. Nxt w prov that most colorigs i (1) aris from th cass wh almost all sts S i ar th sam. Usig ths facts, w ca prov that xtrmal graphs hav larg miimum dgr, which, i th last stp, allows us to prov that ay xtrmal graph coicids with K. Th followig lmma is th first stp i th abov dscriptio, which may b asily provd for gral l. Lmma.. For r t 3 ad l 1, thr is a costat D > 0 such that if G = (V, E) is a C r,t,l -xtrmal graph o tl + 1 vrtics, th E(G) ( ) D log t 1. Proof. Fix r t 3 ad lt G = (V, E) b a -vrtx C r,t,l -xtrmal graph with V = {v 1,..., v }. Not that G has at last (t 1) ( ) (3) S t,l -fr r-dg colorigs, as th complt graph K has at last ths may colorigs: choos a fixd (t 1)-subst S of [r] ad assig colors i S to all dgs of K. W cosidr th dcompositio i (), ad fix sts S 1,..., S. Colorigs i C r,t,l,(s1,...,s )(G) may b producd as follows: for ach vrtx v i w choos at most (r t + 1)(l 1) icidt dgs to b assigd colors that ar ot i S i ad color thm with ths colors. Th rmaiig dgs {v i, v j } E ar assigd colors i S i S j. For sufficitly larg, this implis that C r,t,l(s1,...,s )(G) (r t+1)(l 1) j=0 ( ) 1 r j j {v i,v j } E S i S j (r t+1)(l 1) r (r t+1)(l 1) (t 1) E. (4)

5 As (S 1,..., S ) ca b chos i ( r FORBIDDING RAINBOW-COLORED STARS 5 C r,t,l (G) t 1) ways, (S 1,...,S ) S i =t 1,,..., ( r t 1 C r,t,l(s1,...,s )(G) ) (r t+1)(l 1) (t 1) E (t 1) D log t 1 (t 1) E, (5) ( whr D = log r t 1 t 1) + (r t + 1)(l 1) is a costat. Combiig (3) ad (5), w hav ( ) (t 1) D log t 1 (t 1) E (t 1) ( ) = E D log t 1, as rquird. To prform th scod stp of th proof, for a costat A > 0, lt S A dot th st of all collctios (S 1,..., S ) of (t 1)-substs of [r] whr o st S i appars mor tha ( A log t 1 ) tims, i {1,..., }. W prov that w may fid A for which th umbr of colorigs i S A is gligibl. As i th prvious rsult, w prov this for gral l, as thr is littl additioal work. Lmma.3. Lt r t 3 ad l 1 b itgrs. For all D > 0 thr xists a positiv costat A with th followig proprty. Giv ε > 0 thr is a costat 0 such that, for all 0, ay -vrtx graph G = (V, E) with at last ( ) D logt 1 dgs satisfis C r,t,l,(s1,...,s )(G) (S 1,...,S ) S A ε(t 1) E(G). Proof. With forsight, fix { } 3(r t + 1)(l 1) A > max 1 log t 1 (t ), D, ad lt B b a itgr satisfyig / ( r t 1) B A logt 1, whr will b chos sufficitly larg latr i th proof. Giv a -vrtx graph G = (V, E) with E ( ) D log t 1, w provid a uppr boud o th umbr of raibow-s t,l -fr r-dg colorigs i a st C r,t,l,(s1,...,s )(G) such that max S {v V : S v = S, S = t 1} = B. To grat ths colorigs, w choos a st U V such that U = B ad a (t 1)-subst S of [r] which is assigd to all vrtics i U. W th assig othr (t 1)-substs to th rmaiig ( B) vrtics of G. Lt E(U, V \ U) dot th st of dgs with o vrtx i U ad th othr i V \ U. As i th proof of Lmma. (s (4)), for ach vrtx v i w choos at most (r t + 1)(l 1) dgs i at most (r t+1)(l 1) ) i=0 (r t+1)(l 1) ways for sufficitly larg. Ths dgs ar assigd colors that ar ot i S i i at most r (r t+1)(l 1) ways. Th rmaiig dgs {v i, v j } E ar assigd colors i S i S j. Ay such dg i E(U, V \U) may b assigd at most (t ) colors, sic th sts assigd to thir dvrtics ar distict. Hc th umbr of raibow-s t,l -fr r-colorigs of G is boudd abov by = ( B ( B ) ) ( 1 i ( ) r B+1 (r) (r t+1)(l 1) (t ) E(U,V \U) E(G) E(U,V \U) (t 1) t 1 ) B+1 (r) (r t+1)(l 1) (t 1) E(G) ( r t 1 ( ) t E(U,V \U). (6) t 1

6 6 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES Not that E(U, V \ U) ( ) D log t 1 = D log t 1 + B B { D log t 1 + mi ( ) B = (A D) log t 1 A log t 1 ( ) B A log t 1 A log t 1, ( r t 1 } ) ) for larg. As a cosquc, sic A > D ad is sufficitly larg (i particular dpds o A, r, l ad t), w obtai E(U, V \ U) (A log t 1 )/. If w sum (6) ovr all possibl valus of B, w obtai at most ( ) r ( ) r (r) (r t+1)(l 1) (t 1) E(G) t 1 t 1 + log ( r t 1) (t 1) E(G) +(r t+1)(l 1) log t 1 (r) ε(t 1) E(G) ( t t 1 ( t t 1 ) A log t 1 ( r t 1 ) A log t 1 raibow-s t,l -fr r-colorigs of G, whr ε > 0 is arbitrary as log as w choos sufficitly larg, sic w hav A > 3(r t+1)(l 1) 1 log t 1 (t ). Th xt stp i our proof of Thorm 1.1 for l = 1 is provig that ay xtrmal graph has larg miimum dgr. Ulik th prvious stps, w shall ow dal xclusivly with th cas l = 1, as tratig rar colors will rquir cosidrably mor work. Lmma.4. For all itgrs r t 3 thr is a 0 such that th miimum dgr of G satisfis δ(g) 3/4 1 for all C r,t,1 -xtrmal graphs G with 0 vrtics. Proof. Assum that a -vrtx C r,t,1 -xtrmal graph G has a vrtx v with dgr d(v) < (3/4 1). Lt w 1,..., w /4 b vrtics i G that ar ot adjact to v. Dfi th graph G by addig th dgs {v, w 1 },..., {v, w /4 } to G. Th basic ida of th proof is to show that G admits mor raibow S t -fr r-colorigs tha G, ad w do this by showig that, if w compar th umbr of colorigs cratd ad lost with th additio of th w dgs, thr ar mor of th formr. To b mor prcis, giv a collctio (S 1,..., S ) of (t 1)-substs of [r], it is clar that w may xtd all colorigs i C (S1,...,S )(G) to C (S1,...,S )(G ) whvr S v S wi for all i {1,..., /4 }, as w may assig ay color i th corrspodig itrsctio to {v, w i } without producig a raibow star S t. Morovr, this xtsio may b do i svral ways, dpdig o th sizs of th itrsctios, which lads to w colorigs of G, as opposd to colorigs that ar i o-to-o corrspodc with colorigs of G. O th othr had, colorigs of G for which S v S wi = for som i may ot b xtdd i this way, ad w say that ths colorigs ar lost wh th w dgs ar addd. To fid a lowr boud o th umbr of colorigs cratd, cosidr oly thos dg colorigs of G whr vry dg is assigd a color from a fixd (t 1)-st S i [r]. Each such colorig ca b xtdd to at last (t 1) /4 (t 1) E(G) raibow-s t -fr colorigs of G by assigig a arbitrary color of S to ach w dg. This crats at last ( (t 1) /4 1 ) (t 1) E(G) w colorigs. O th othr had, th raibow-s t -fr r-colorigs of G that caot b xtdd to colorigs of G ar thos whr th sts of colors availabl at v ad at w i do ot itrsct, for som

7 FORBIDDING RAINBOW-COLORED STARS 7 i {1,..., /4 }. By Lmma.3 with ε = 1, th umbr of colorigs of G whr vry (t 1)-st of colors is assigd to at most A log t 1 vrtics of G is at most (t 1) E(G). Hc w coctrat o colorigs whr som (t 1)-st S appars at last A log t 1 tims. Th umbr of such colorigs is at most ( ) ( ) ( ) ( ) ( ) r r (t 1) r r A logt 1 (t 1) E(G), (7) t 1 t 1 t 1 A log t 1 t 1 sic thr ar ( r t 1) ways to choos Sv, a o-ighbor w i ca b chos i at most ways ad it is assigd a st S wi of colors with S wi S v =, which ca b do i ( ) r (t 1) t 1 ways. Th st S ca b chos i ( r t 1) ways, th vrtics which ar assigd th st S ca b chos i at most ( ) ( A log t 1 = A log t 1 ) ways ad vry rmaiig vrtx is associatd with som arbitrary (t 1)-st of colors. (Not that this uppr boud taks car of all th colorigs whr th st S is assigd to m vrtics, whr A log t 1 m.) Clarly, w hav ( A log t 1 ) A log t 1, ad, for larg ( ) ( ) ( ) ( ) r r (t 1) r r A logt 1 < A log t 1. t 1 t 1 t 1 t 1 W coclud from (7) that, for sufficitly larg, th umbr of raibow-s t -fr r-colorigs of G that caot b xtdd to such colorigs of G is at most A log t 1 (t 1) E(G) + (t 1) E(G) ((t 1) /4 1) (t 1) E(G). I othr words, by addig th dgs {v, w 1 },..., {v, w /4 } to G, w icras th total umbr of colorigs, which cotradicts th choic of G. W rmark that th prvious proof may b asily adaptd so that α = 3/4 is rplacd by ay fixd 0 < α < 1. W ar ow rady to prform th last stp i th proof of Thorm 1.1 for l = 1, which shows that, i a xtrmal graph G o dg may b missig. Thorm.5. For r t 3, thr xists 0 such that c r,t,1 () = C r,t,1 (K ) holds for 0. Morovr, K is th uiqu -vrtx C r,t,1 -xtrmal graph. Proof. Assum that thr is a C r,t,1 -xtrmal graph G = (V, E) o vrtics with at last two o-adjact vrtics x ad y. As i th proof of Lmma.4, w prov that G = G + {x, y} has mor raibow-s t -fr r-colorigs tha G if is sufficitly larg. By Lmma. w kow that may b chos so that E(G) ( ) D logt 1, whr D is a costat. Evry colorig of G for which oly (t 1) colors ar usd ca b xtdd, assigig ay of ths (t 1) colors to {x, y}, to a colorig of G, which icrass th total umbr of colorigs by (t ) (t 1) E(G). (8) W show that th umbr of all raibow-s t,1 -fr r-colorigs of G that caot b xtdd to a colorig of G is smallr tha (8). By Lmma.3 with A = A(r, t, l, D) ad ε = 1/, w kow that w may choos 0 such that th umbr of colorigs associatd with assigmts i S A is at most 1 (t 1) E(G). Thrfor, i th followig w oly d to cosidr colorigs from th st A = C (S1,...,S )(G). (9) (S 1,...,S ) S A Th oly colorigs of G that caot b xtdd to colorigs of G ar thos whr th color sts S x ad S y assigd to x ad y, rspctivly, ar disjoit, so that w ar uabl to assig

8 8 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES a color to {x, y}. Fix (S 1,..., S ) such that S is assigd to at last ( A log t 1 ) vrtics of G. Rcall that both vrtics x, y hav dgr at last 3/4 1 by Lmma.4. Th coditio o th dgrs implis that th commo ighbourhood N({x, y}) of x ad y has siz at last /. For ay vrtx w i N({x, y}) w hav S w (S x S y ) t 1. Mor prcisly, w hav S w S x = a w ad S w S y t 1 a w, so that thr ar at most a w (t 1 a w ) ((t 1)/) ways to assig colors to th dgs {x, w} ad {y, w}. Hc all dgs btw {x, y} ad thir commo ighbourhood N({x, y}) may b colord i at most ((t 1)/) N({x,y}) ways. This lads to th followig uppr boud o th umbr of lmts i (9) that caot b xtdd to a colorig of G. Th st S may b chos i ( r t 1) ways ad, for larg, thr ar ( A log t 1 ) < /4 ways of choosig A log t 1 vrtics which ar assigd S. For sufficitly larg, th rmaiig vrtics may b assigd color sts i at most ( r A logt 1 t 1) < /4 ways, ad w ifr that ( ) ( ) ( ) r r A logt 1 A A log t 1 t 1 t 1 ( ) ( r A log t 1 t 1 ( ) r / (t 1) E(G) t 1 1 ( ) r (t 1) E(G). t 1 ) A logt 1 +1 (t 1) E(G) (t 1) E(G) 4 N({x,y}) Altogthr, th umbr of all colorigs of G that caot b xtdd by addig dg {x, y} to th graph G is o mor tha 1 (t 1) E(G) + 1 ( ) r (t 1) E(G), t 1 which is smallr tha (8) for sufficitly larg. 3. Colorigs avoidig a raibow S t,l I this sctio, w cosidr th proof Thorm 1.1 for gral l. Although w oly prst it for l >, th cas l = may b tratd similarly, but with asir calculatios, as w just d to aalys a grdy algorithm. As w rmarkd bfor, th stratgy for achivig this rsult is xactly th sam as for th cas l = 1, but th prsc of rar colors will mak th argumts mor tchical. Rcall that first ad scod mai stps of th proof, amly showig that xtrmal graphs hav a larg umbr of dgs, ad that most colorigs hav th proprty that almost all vrtics hav th sam st of ordiary colors, hav alrady b provd for gral l (Lmmas. ad.3). To prform th rmaiig stps, w us th stratgy mployd i Lmma.4, ad show that th umbr of colorigs cratd xcds th umbr of colorigs lost wh dgs ar addd. To rach this coclusio, w d a lowr boud o th umbr of colorigs cratd, ad a uppr boud o th umbr of colorigs lost, with th proprty that th lowr boud is largr tha th uppr boud. Howvr, valuatig ths bouds will b hardr i this cas bcaus of th rar colors. To dscrib th mai igrdit dd to trat rar colors, first cosidr colorigs for which S 1 = = S, so that th rar colors ar th sam for all vrtics. I ay S t,l -fr r-colorig i C (S1,...,S )(G), th graph iducd by ach rar color has maximum dgr lss tha l, so w d to cout th umbrs of subgraphs of G of this typ. A classical rsult

9 FORBIDDING RAINBOW-COLORED STARS 9 of Bdr ad Cafild [3] implis that th umbr N (d) of subgraphs of K with dgr squc d = (d 1,..., d ), whr th compots d i ar boudd abov by som absolut costat d, satisfis N (d) (m)! xp( λ ( ) λ ) m m m! m d i! d i! xp( λ λ ), (10) whr m = d i ad λ = 1 ( di ) m, ad whr th asymptotics ar i (th scod approximatio uss Stirlig s formula). Hr A() B() mas that lim A()/B() = 1. For simplicity, ad giv that thr is ough room i our approximatios, w oft writ that ( ) m m N (d) = d i! xp( λ λ ) for larg. As it turs out, this rsult for K is sufficit for th uppr boud, as w may assum that ay dg dd i our costructio lis i th graph. Howvr, this may ot b do for th lowr boud, whr w d a approximat vrsio of Bdr ad Cafild s rsult. I th followig, giv a graph H o vrtics ad a itgr squc d = (d 1,..., d ), lt N H (d) b th umbr of subgraphs with dgr squc d i H. Mor grally, giv a array d = (d 1,..., d k ), lt N H ( d) b th umbr of ways of slctig a k-tupl (H 1,..., H k ) of dg-disjoit subgraphs of H such that ach subgraph H i has dgr squc d i, for all i {1,..., k}. I th followig, w say that a itgr squc d = (d 1,..., d ) is ds if d i. Lmma 3.1. Giv positiv itgrs d ad k, ad a costat D > 0, thr xist positiv costats 0, M ad α satisfyig th followig proprty for all 0. For vry graph H with V (H) = ad E(H) ( ) D l, thr xists W V (H) with W M l such that, for all ds dgr squcs d 1,..., d k {0,..., d} W, whr k k, w hav N H[W ] (d 1,..., d k ) α N W (d i ). Ituitivly, this lmma stats that, i ay graph with may dgs, thr is a almost spaig subgraph with a larg umbr of subgraphs of ay boudd dgr squc that is sufficitly ds. Not that this would trivially fail if w rquird W = V, as a my would b abl to isolat vrtics wh rmovig D log t 1 dgs of K to produc G, so that N G (d) = 0 for ay positiv squc d = (d 1,..., d ). Th proof of Lmma 3.1, which adapts idas of Gao [7], lis i Sctio 4. Lmma 3.. For all itgrs r t 3 ad l 1, thr is 0 such that ay C r,t,l -xtrmal graph G o 0 vrtics satisfis δ(g) 3/4 1. Proof. Assum that a C r,t,l -xtrmal -vrtx graph G = (V, E) has a vrtx v with dgr d(v) < 3/4 1. W suppos that is sufficitly larg for all stps i th proof to hold. Lt w 1,..., w /4 b /4 vrtics i G that ar ot adjact to v, ad lt G b th graph obtaid by addig all th dgs {v, w i } to G, i = 1,..., /4. Lt D > 0 such that G has at last ( ) D logt 1 dgs (Lmma.) ad fix A > 0 with th proprty of Lmma.3. W show that th umbr N of w colorigs of G obtaid by xtdig colorigs of G is largr tha th umbr N of colorigs of G that caot b xtdd to colorigs of G. By Lmma.3 with ε = 1/3, th umbr of colorigs i (S 1,...,S ) S A C (S1,...,S )(G) is at most (t 1) E(G) /3 for sufficitly larg. Lt N A b th umbr of w colorigs of G associatd with -tupls (S 1,..., S ) i S A, ad lt N A b th umbr of colorigs associatd with such collctios that caot b xtdd. I th rmaidr of th proof, w fid a lowr boud o N A ad a uppr boud k

10 10 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES o N A to show that N A N A. Morovr, it turs out that N A (t ) (t 1) E(G) (s (16)), so that ( ) ( N N N A (t 1) E(G) (t ) N A + 1 ) (t 1) E(G) > 0, 3 3 as rquird. Bfor procdig, lt 0, M ad α giv by Lmma 3.1 applid for D, d = l 1 ad k = r l + 1 (adjustig th costats so that th logarithms i th statmt of th lmma hav bas t 1), ad fix a st W V with W M log t 1 such that G[W ] satisfis th coclusio of th lmma. Uppr boud: W giv a uppr boud o th umbr N A of raibow-s t,l -fr r-colorigs of G that ar associatd with collctios (S 1,..., S ) S A ad caot b xtdd to a colorig of G. If S v S wi for vry i, th colorigs of C r,t,l,(s1,...,s )(G) ca b asily xtdd to colorigs of C r,t,l,(s1,...,s )(G ) usig ordiary colors for ach w dg, so that w may assum that th sts of colors availabl at v ad availabl at w i do ot itrsct for som i {1,..., /4 }. Howvr, ot that S v S wi = dos ot imply that thr is o color availabl for th dg {v, w i }, sic it could possibly b colord with o of th rar colors. For th sak of simplicity, ad sic w ar lookig for a uppr boud, w shall igor this fact ad assum that S v S wi = always maks it impossibl to color th dg {v, w i }. To costruct colorigs of this typ w do th followig. First, w fix a collctio (S 1,..., S ) S A with th rquird proprtis: (i) choos a (t 1)-subst S v [r]; (ii) choos a vrtx w that is ot adjact to v; (iii) choos a (t 1)-subst st S w [r], which is disjoit from S v, to b assigd to w; (iv) choos th (t 1)-subst S [r] that is assigd to at last A log t 1 vrtics of G; (v) choos A log t 1 vrtics that ar assigd this st S; (vi) assig ay (t 1)-substs i [r] to th A log t 1 rmaiig vrtics. Not that stps (i), (ii) ad (vi) allow us to choos S, so it might wll b that mor tha A log t 1 vrtics ar assigd S. Th umbr of choics for th sts S v, S w, S abov is at most ( r 3, t 1) whil is a uppr boud o th umbr of choics of w. Stps (v) ad (vi) may b prformd i ( ) ( A log t 1 r A logt 1 t 1) ways, so that a uppr boud o th umbr of ways of fixig a collctio (S 1,..., S ) with th rquird proprtis is ( ) r 3 ( ) ( ) r A logt 1 ( ) r A logt 1 +1 A log t 1. (11) t 1 A log t 1 t 1 t 1 Now assum that such a collctio (S 1,..., S ) is fixd. Lt Y = {u V : S u = S} ad lt H = G[W Y ]. W ow costruct colorigs i C (S1,...,S )(G). To this d, w procd as follows: (i) color dgs icidt with vrtics u V \ Y with rar colors with rspct to u; (ii) color dgs icidt with th rmaiig vrtics of (V \ W ) Y with rar colors; (iii) color dgs icidt with V \ Y with ordiary colors (with rspct to som dpoit i V \ Y ); (iv) color dgs i H with rar colors (with rspct to S); (v) color dgs icidt with vrtics with both ds i Y with ordiary colors (with rspct to S). Obsrv that th dgs = {u, v} such that u is assigd S, but v is ot, may b colord i (i) if is assigd a rar color with rspct to v or i (iii), if is assigd a ordiary color with rspct to v. For simplicity, w shall assum that dgs colord i (i) may b rcolord i (iii).

11 FORBIDDING RAINBOW-COLORED STARS 11 For stp (i), thr ar at most ( (l 1)(r t+1) ) A log t 1 ways of choosig (l 1) dgs icidt with ach such vrtx w for ach of th (r t + 1) rar colors. Not that w do ot d to cosidr th possibility that fwr dgs ar assigd such colors bcaus, with our stimats, th dgs colord at this poit could b rcolord i latr stps. Stp (ii) may b prformd i at most (l 1)(r t+1)m log t 1, whil stp (iii) may b prformd i at most (t 1) γ ways, whr γ is th umbr of dgs icidt with vrtics i V \ Y. To assig rar colors (with rspct to S) to th dgs of H, w us th followig procdur. Procdur 3.3. Suppos w hav a q-vrtx iput graph H = (V, E) ad a st T ( [r] t 1). Assum that H q,0 is a st of isolatd vrtics labld by V. For i {1,..., r t + 1}, choos a graph H q,i i E \ i 1 j=1 E(H q,j) with th dgr squc d i = (d 1 i,..., dq i ), whr dj i l 1 ad assig th ith color i [r] \ T to th dgs of H q,i. As w ar lookig for a uppr boud, w shall assum that W Y = W, possibly big abl to rcolor som dgs that hav b colord i prvious stps). For simplicity, assum that W = p. For i {1,..., r t + 1}, fix dgr squcs d i = (d 1 i,..., dp i ), whr th ith dgr squc is associatd with th ith rar color: w fid r t + 1 dg-disjoit subgraphs H 1,..., H r t+1 of H such that H i has dgr squc d i, i = 1,..., r t + 1. Not that N H (d 1,..., d r t+1 ) is th umbr of ways i which this ca b do. Th umbr of ways of prformig stps (iv) ad (v) is boudd abov by d N H ( d) (t 1) E(G) u( d) γ, whr th sum rags ovr th arrays d = (d 1,..., d r t+1 ), whr ach d i = (d 1 i,..., dp i ) has compots boudd by l 1. Morovr, w dot u( d) = 1 r t+1 p j=1 dj i. As a cosquc, th umbr of colorigs costructd abov is boudd abov by (l 1)(r t+1)a log t 1 (l 1)(r t+1)m log t 1 (t 1) γ d N H ( d) (t 1) E(G) u( d) γ. (1) It is asy to s that, choosig sufficitly larg, w may choos a arbitrarily small costat δ > 0 such that th product of quatios (11) ad (1) is at most (1 + δ) N H ( d) (t 1) E(G) u( d) = (1 + δ) N H ( d) (t 1) E(G) u( d), (13) d s d+ whr s: [r t + 1] {0, 1} is a fuctio that, for ach i, idicats whthr th dgr squc d i is ds (that is, wh s(i) = 1, th compots of d i sum to p or mor) or spars (that is, wh s(i) = 0, th compots of d i sum to lss tha p), whil d + ad d ar th arrays of ds ad spars dgr squcs, rspctivly, that crat a array d with th distributio dtrmid by s. W split quatio (13) accordig to whthr all rar colors grat ds graphs, or whthr this dos ot hold. For simplicity, w writ d + to say =j that w sum ovr arrays of lgth j whos dgr squcs ar all ds. W obtai (1 + δ) N H ( d + ) (t 1) E(G) u( d+) + N H ( d) (t 1) E(G) u( d).(14) d + =r t+1 s 1 d d+ d

12 1 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES Obsrv that th scod summatio may b stimatd as N H ( d) (t 1) E(G) u( d) d+ s 1 d r t ( p ) p(r t+1 j)/ r t+1 l p(r t+1) (r t+1)/ r t l p(r t+1) p p(r t+1 j)/ j=1 j=1 d + =j (t 1) E(G) u( d +) d + =j (t 1) E(G) u( d +) N H ( d + ) j N Kp (d i +). (15) To s why th first iquality is tru, ot that s rags i a st with at most r t+1 lmts, th umbr of arrays d = (d 1,..., d r t+1 j ) is boudd abov by l p(r t+1 j) ad N H ( d ) r t+1 j N Kp (d i ) ( ( p ) ) p/ r t+1 j (r t+1)/ p(r t+1 j)/ pp(r t+1 j)/, whr N Kp (d i ) is boudd usig (10) ad th fact that j dj i p. Lowr boud: Nxt w giv a lowr boud o th umbr N A of raibow-s t,l-fr r-colorigs w gai by addig th dgs {v, w 1 },..., {v, w /4 } to our xtrmal graph G. Th ida hr is that, if w cosidr colorigs i C r,t,l,(s,...,s) (G) for som fixd (t 1)-subst S of [r], thos colorigs ca b xtdd to colorigs of G by assigig ay of ths (t 1) colors to ach of th dgs {v, w i }, i = 1,... /4. Hc th umbr of colorigs of this typ for G is at last (t 1) /4 C r,t,l,(s,...,s) (G). Rmovig colorigs that ar xtsios of th corrspodig colorigs of G, th t gai of colorigs is ( ) (t 1) /4 1 C r,t,l,(s,...,s) (G). W ow fid a lowr boud o C r,t,l,(s,...,s) (G). To assig rar colors to dgs of G, w apply Procdur 3.3 to H = G[W ] ad T = S. Sic w ow d a lowr boud, w may ot suppos that G[W ] = K p, but Lmma 3.1 guarats that, for all k-tupls (d 1,..., d k ) such that k r t + 1 ad p j=1 dj i p, for all i, w hav N G[W ](d 1,..., d k ) α k N K p (d i ). With this, a lowr boud o th umbr of colorigs gaid by addig th dgs {v, w 1 },..., {v, w /4 } to G is ((t 1) /4 1) d (t 1) E(G) u( d) N G[W ] ( d), (16) whr w agai sum ovr arrays d = (d 1,..., d r t+1 ), whr ach d i = (d 1 i,..., dp i ) has compots boudd by l 1. To coclud our argumt, w compar th uppr boud (14) ad th lowr boud (16). W hav (14) (16) (1 + δ) d + =r t+1 N H( d + ) (t 1) E(G) u( d + ) ((t 1) /4 1) d (t 1) E(G) u( d) N G[W ] ( (17) d) + (1 + δ) l p(r t+1) j pp(r t+1 j)/ d+ (t 1) E(G) u( d + ) j [l] j N K p (d i +) ((t 1) /4 1) d (t 1) E(G) u( d) N G[W ] (.(18) d)

13 FORBIDDING RAINBOW-COLORED STARS 13 For th first trm i th sum, w us that, for positiv umbrs a 1, b 1,..., a s, b s with a i /b i Z for i = 1,..., s, th iquality a a s b b s Z. holds. By our choic of W, th trm (17) bcoms (1 + δ) d + =r t+1 N H( d + ) (t 1) E(G) u( d + ) ((t 1) /4 1) d (t 1) E(G) u( d) N G[W ] ( d) (1 + δ) d + =r t+1 N H( d + ) (t 1) E(G) u( d + ) ((t 1) /4 1) d + =r t+1 (t 1) E(G) u( d + ) N G[W ] ( d + ) (1 + δ) r t+1 N Kp (d i ) ((t 1) /4 1) α r t+1 N Kp (d i ) = α (1 + δ) ((t 1) /4 1) 1 4, (19) for sufficitly larg. For th scod trm (18), w writ (1 + δ) l p(r t+1) j pp(r t+1 j)/ d+ (t 1) E(G) u( d + ) j N K p (d i +) = (1 + δ) l p(r t+1) (t 1) /4 1 (1 + δ) l p(r t+1) (t 1) /4 1 ((t 1) /4 1) d (t 1) E(G) u( d) N G[W ] ( d) r t j=0 d + =j r t j=0 d + =j p p(r t+1 j)/ (t 1) E(G) u( d + ) j N K p (d i +) d (t 1) E(G) u( d) N G[W ] ( d) p p(r t+1 j)/ (t 1) E(G) u( d + ) j N K p (d i +) (t 1) E(G) u( d + ) (r t+1 j)p(l 1)/ N G[W ] ( d +, d ), (0) whr d is a array of r t+1 j dgr squcs qual to d = (l 1,..., l 1) (o of th trms l 1 may b rplacd by l i ths squcs to dal with parity costraits). Not that, to rach (0), w rplacd th domiator withi th sums by a sigl trm, which dpds o ach particular trm big addd. Our choic of W implis that, wh d + = j, j r t+1 j N G[W ] ( d +, d ) α N Kp (d i +) N Kp (d ), α ( (l 1)! p α p p(l 1)(r t+1 j)/ ((l 1)!) (l 1)p(r t+1) ( ) p(l 1) p(l 1)/ ( ) ) r t+1 j 1 (l 1) j xp N Kp (d i 4 +) j N Kp (d i +). (1) To fid a uppr boud o (0), w combi (1) with th fact that th umbr of trms i th sums is at most (r t + 1)l p(r t+1) ad that, for l, th trm p (r t+1 j)p( l)/ is maximizd for j = r t, which lads to th followig uppr boud o (0): α (1 + δ) l p(r t+1) (r t + 1) ( (t 1) (l 1)!) (r t+1)(l 1)p = (t 1) O() p ( l)p/ < 1 4 ((t 1) /4 1)p (l )p/

14 14 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES for larg, as p M log t 1. Thrfor, w obtai (14) (16) < 1, () for t 3 ad sufficitly larg, which implis that N A N A, as rquird. Thorm 3.4. For itgrs r t 3 ad l, thr xists 0 such that c r,t,l () = C r,t,l (K ) holds for 0. Morovr, K is th uiqu -vrtx C r,t,l -xtrmal graph. Proof. Fix r t 3 ad l. Assum that thr is a C r,t,l -xtrmal graph G o -vrtics with at last two o-adjact vrtics x ad y. W prov that G = G + {x, y} has mor colorigs tha G if is sufficitly larg. Our proof uss th stratgy mployd for Lmma 3.. By Lmma. w kow that 0 may b chos so that E(G) ( ) D logt 1, whr D = D(r, t) is a costat. Fix A > 0 with th proprty of Lmma.3. Lt 0, M ad α giv by Lmma 3.1 applid for D, d = l 1 ad k = r l + 1 (adjustig th costats so that logarithms i th statmt of th lmma hav bas t 1), ad fix a st W V with W M log t 1 = p such that G[W ] satisfis th coclusio of th lmma. W show that th umbr N of w colorigs of G obtaid by xtdig colorigs of G is largr tha th umbr N of colorigs of G that caot b xtdd to colorigs of G. By Lmma.3 with ε = 1/3, th umbr of colorigs i (S 1,...,S ) S A C (S1,...,S )(G) is at most (t 1) E(G) /3 for sufficitly larg. Lt N A b th umbr of w colorigs of G associatd with -tupls (S 1,..., S ) i S A, ad lt N A b th umbr of colorigs associatd with such collctios that caot b xtdd. I th rmaidr of th proof, w fid a lowr boud o N A ad a uppr boud o N A to show that N A N A. Oc agai w hav N A (t ) (t 1) E(G) (s (3)), which lads to th dsird rsult: ( N N N A N A + ) ( (t 1) E(G) (t ) 1 ) (t 1) E(G) > With th argumts usd for (16), w may show that vry colorig of G for which vry vrtx is assigd th sam st of (t 1) colors ca b xtdd to colorigs of G, which icrass th total umbr of raibow-s t,l -fr r-colorigs by at last (t ) d N G[W ] ( d) (t 1) E(G) u( d), (3) whr w agai sum ovr arrays d = (d 1,..., d r t+1 ), whr ach d i = (d 1 i,..., dp i ) has compots boudd by l 1. Rcall that u( d) = 1 r t+1 p j=1 dj i. W show xt that th umbr of all colorigs of G that caot b xtdd to a colorig of G is smallr tha (3). W giv a uppr boud o th umbr N A of raibow-s t,l -fr r-colorigs of G that ar associatd with collctios (S 1,..., S ) S A ad caot b xtdd to a colorig of G. All such colorigs hav th proprty that th color sts S x ad S y assigd to x ad y, rspctivly, ar disjoit. Fix (S 1,..., S ) for which this holds, ad whr S is assigd ( to at last ( A log t 1 ) vrtics of G. Th (t 1)-subst S [r] may chos i r ) ( t 1 ways ad thr ar A log t 1 ) ways of choosig ( A logt 1 ) vrtics which ar assigd S. Th rmaiig vrtics may b assigd (t 1)-lmt color sts i at most ( r A logt 1 t 1) ways. As a cosquc, ( ) ( ) ( ) r r A logt 1 ( ) r A logt 1 +1 A log t 1 (4) t 1 A log t 1 t 1 t 1

15 FORBIDDING RAINBOW-COLORED STARS 15 is a uppr boud o th umbr of ways of fixig a collctio (S 1,..., S ) with th rquird proprtis. W driv a uppr boud o th umbr of colorigs i C (S1,...,S )(G) that caot b xtdd to G. Sic x, y hav dgr at last 3/4 by Lmma 3., thir commo ighbourhood N({x, y}) has siz at last /. For ay vrtx w i N({x, y}) w hav S w (S x S y ) t 1. Mor prcisly, w hav S w S x = a w ad S w S y t 1 a w, so that thr ar at most a w (t 1 a w ) ((t 1)/) ways to assig ordiary colors to th dgs {x, w} ad {y, w}. Hc all dgs btw {x, y} ad thir commo ighbourhood N({x, y}) may b colord i at most ((t 1)/) N({x,y}) ways with ordiary colors (with rspct to both ds). As i th proof of Lmma 3., w procd as follows: lt Y = {u V : S u = S} ad lt H = G[W Y ], (i) color dgs icidt with vrtics u V \ Y with rar colors with rspct to u; (ii) color dgs icidt with vrtics of (V \ W ) Y with rar colors; (iii) color dgs icidt with V \ Y with ordiary colors (with rspct to som dpoit i V \ Y ); (iv) color dgs i H with rar colors (with rspct to S); (v) color dgs icidt with ach vrtx i Y with ordiary colors (with rspct to S). W obtai th followig uppr boud o th umbr of colorigs of G that caot b xtdd to G : ( ) r A logt 1 +1 A log t 1 (l 1)(r t+1)a log t 1 (l 1)(r t+1)m log t 1 t 1 ( ) N H ( t 1 N(x,y) d) (t 1) E(G) u( d) N(x,y). (5) d It is asy to s that, choosig sufficitly larg, quatio (5) is at most / ( ) N H ( t 1 N(x,y) d) (t 1) E(G) u( d) N(x,y), (6) d which may b rwritt as / s d d+ N H ( d) (t 1) E(G) u( d) N(x,y), (7) whr, as i (13), s: [r t + 1] {0, 1} is a fuctio that, for ach i, idicats whthr th dgr squc d i is ds or spars, whil d + ad d ar th arrays of ds ad spars dgr squcs, rspctivly, that crat a array d with th distributio dtrmid by s. W split quatio (7) accordig to whthr all rar colors grat ds graphs, or whthr this dos ot hold, which lads to / d + =r t+1 d + =r t+1 N H ( d + ) (t 1) E(G) u( d + ) N(x,y) + s 1 N H ( d + ) (t 1) E(G) u( d + ) / + s 1 d d d+ N H ( d) d+ N H ( d) (t 1) E(G) u( d) N(x,y) (t 1) E(G) u( d) /. (8)

16 16 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES Usig th stimat (15) w hav s 1 d d+ N H ( d) r t l p(r t+1) p p(r t+1 j)/ j=1 (t 1) E(G) u( d) / d + =j (t 1) E(G) u( d + ) / j N Kp (d i +). (9) To coclud our argumt, w compar th uppr boud (8) ad th lowr boud (3). W hav (8) d + =r t+1 N H( d + ) (t 1) E(G) u( d + ) (3) / (t ) d (t 1) E(G) u( d) N G[W ] ( (30) d) + lp(r t+1) j pp(r t+1 j)/ d + =j (t 1) E(G) u( d +) j N K p (d i +) / (t ) d (t 1) E(G) u( d) N G[W ] (. (31) d) Usig th stimats of (19), th trm (30) may b boudd by d + =r t+1 N H( d + ) (t 1) E(G) u( d + ) / (t ) d + =r t+1 (t 1) E(G) u( d +) N G[W ] ( d + ) α / (t ) 1 4 For th scod trm (31), w rpat th argumts usd i th proof of Lmma 3.. Thrfor, w obtai for t 3 ad sufficitly larg, as rquird. (8) (3) < 1, (3) 4. Auxiliary rsults To coclud our papr, w prov Lmma 3.1. To do this, w d som prlimiary dfiitios ad rsults, which ar basd o th idas of Gao [7]. Lt (H ) 1 b a squc of graphs o vrtics ad lt (H ) dot th umbr of dgs i H. Color all dgs i H blu ad all dgs i its complmt H rd, so that K is th complt graph o vrtics with a -colorig K = H H. For ay rd dg x = uv, dfi th rd dgr of x d r (x) = d r (u) + d r (v), whr d r (u) dots th umbr of rd dgs icidt with u. Lt r (H ) = max x E(K) d r (x), whr w maximiz ovr rd dgs. Fix a dgr squc d = (d 1,..., d ), whr max d i d for som absolut costat d N, ad lt F b a graph with dgr squc d = (d 1,..., d ) chos uiformly at radom from all subgraphs of K with this dgr squc. Lt X = X (F ) b th radom variabl that accouts for th umbr of rd dgs i F. Rcall that Lmma 3.1 couts th umbr of subgraphs (with a giv dgr squc) of a complt graph that rmai aftr a my dlts a crtai umbr of dgs to produc a subgraph G. I th currt sttig, dgs that rmai ar rprstd by blu, whil dgs dltd ar rprstd by rd. As a cosquc, tratig subgraphs of th complt graph that ar ot affctd by th dltio of dgs is th sam as tratig colord subgraphs of a -colorig of K whos dgs ar all blu.

17 FORBIDDING RAINBOW-COLORED STARS 17 Th followig rsult is th mai igrdit i th proof of Lmma 3.1, as it givs a lowr boud o th probability of choosig a subgraph with o rd dgs for ay -colorig such that th rd dgr is small. Lmma 4.1. Fix a ds squc d = (d 1,..., d ) such that max d i d. Lt H b a graph o vrtics ad dot t = ( ) (H ). Cosidr th radom variabl X dfid abov. Assum a = lim sup r (H )/ < 1/d. Th, providd that t / 0 for, ( 9d ) t P(X = 0) xp. (1 da) Bfor provig this rsult, w show that it implis Lmma 3.1. First, giv a positiv itgr k, lt Y = Y (F ) dot th umbrs of rd dgs cotaid i a k-tupl F = (F, 1..., F k ) of dg-disjoit subgraphs of H such that F i has dgr squc d i boudd by d, i = 1,..., k. W assum that ths graphs ar chos squtially, uiformly at radom from th rmaiig graph. Corollary 4.. Fix a positiv itgr d ad ds squcs d 1 = (d 1 1,..., d1 ),..., d k = (d k 1,..., dk ) such that max d j i d. Lt H b a graph o vrtics ad lt t = ( ) (H ). Cosidr th radom variabl Y dfid abov. Assum a = lim sup r (H )/ < 1/d. Th, providd that t / 0 for, ( 9d ) t k P(Y = 0) xp. (1 da) Proof. W itrat Lmma 4.1. Lt H 1 = H b a graph satisfyig th hypothss, ad choos F 1 uiformly at radom from all subgraphs of K = H 1 H 1 with dgr squc d 1, so that, by Lmma 4.1, ( ) P(X 1 9d t (1) = 0) xp, (1 da) whr X 1 = X (F) 1 ad t (1) = t. Assum that such a graph F 1 has b chos so that X 1 = 0. Cosidr th graph H = H 1 \ E(F) 1 ad lt t () = ( ) (H ). I othr words, w cosidr a w colorig of K i which th dgs i F 1 ar rcolord rd. Not that t () t +d/ ad r (H) r (H)+d. 1 Thrfor t () / is sufficitly small for larg ad a = lim sup r (H)/ = a bcaus d/ 0 wh. As a cosquc, if w choos F uiformly at radom from all subgraphs of K = H H with dgr squc d, w may apply Lmma 4.1 W apply it to th w colorig iducd by H, which w hav show to satisfy th hypothss of Lmma 4.1. to obtai ( ) P(X = 0 X 1 9d t () = 0) xp, (1 da) whr X = X (F). Obsrv that th probability obtaid by th lmma is a coditioal probability, bcaus w choos F aftr of F 1 has alrady b chos. Itrativly, for 3 i k, w coditio upo havig chos F, 1..., F i 1 with th corrspodig dgr squcs, all of which cotaiig o rd dgs of th origial graph K. W lt H i = H i 1 \ E(F i 1 ), t (i) = ( ) (H i ) ad a i = lim sup r (H)/ i = a, ad w choos F i uiformly at radom from all subgraphs of K = H i H i with dgr squc d i. If w lt X i = X (F), i w obtai P ( X i = 0 X 1 =... = X i 1 = 0 ) xp ( 9d t (i) (1 da) ).

18 18 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES As a cosquc, P(Y = 0) = P as rquird. ( ) X 1 =... = X k = 0 = P(X 1 = 0) P(X = 0 X 1 = 0)... P(X k = 0 X 1 =... = X k 1 = 0) ( 9d t 1 ) ( 9d t k ) xp xp (1 da) (1 da) ( 9d ) t k xp, (33) (1 da) Usig th ida that th rd dgs ar th dgs missig from th graph G to tur it ito a complt graph, w prov Lmma 3.1, which w ow rstat. Lmma 3.1. Giv positiv itgrs d ad k, ad a costat D > 0, thr xist positiv costats 0, M ad α satisfyig th followig proprty for all 0. For vry graph H with V (H) = ad E(H) ( ) D l, thr xists W V (H) with W M l such that, for all ds dgr squcs d 1,..., d k {0,..., d} W, whr k k, w hav N H[W ] (d 1,..., d k ) α N W (d i ). Proof. Fix d, k, ad D as i th statmt of th lmma, ad lt H b a graph o vrtics with E(H) ( ) D l. As bfor, w shall assum that sufficitly larg. Cosidr th st { A = v V (G): d(v) 3d 1 } 3d, so that A 3d 1 + ( A ) ( 1) ( 1) D l 3d = A ( ) 3d + 1 D l k 6Dd l = A M l 3d for M = 7dD ad sufficitly larg. Lt W = V (H)\A ad fix boudd dgr squcs d 1,..., d k {0,..., d} W whr k k. W wish to apply Corollary 4. to th graph H W = H[W ] o W vrtics, whr th dgs i H W ar prcisly th blu dgs of K W. To this d, lt a = lim sup W r (H W )/ W ad t W = ( ) W (H W ). W d to prov that a < 1/d ad that t W / W 0 as W. Obsrv that, for ay vrtx v i W, th umbr of rd dgs icidt with v i K W is prcisly th umbr of dgs icidt with v i th complmt of H[W ] (with rspct to K W ), which is at most th umbr of dgs icidt with v i th complmt of H (with rspct to K ). By our choic of W, this is at most /(3d), ad w hav r ( W ) /(3d). Thus for larg, r (H W ) /(3d) W M l 4 5d < 1 d. Morovr, (H W ) ( ) D logt 1 M log t 1 = ( ) (D + M) logt 1, for larg. W hav ( ) W t W = (H W ) (D + M) log t 1,

19 FORBIDDING RAINBOW-COLORED STARS 19 so that t W / W 0 ad Corollary 4. applis to H W ad th dgr squcs d 1,..., d k. Sic k k, ( ) 9kd t W P(Y = 0) xp. (1 da) W Bcaus, W M log t 1 ad t W (D + M) log t 1, aftr som straighforward calculatios, w obtai P(Y = 0) β, for larg, whr β = 51(D+M)d k l(t 1), w obtai 10(D+M)d k (1 da) l(t 1) 50(D+M)d k l(t 1), as a 4/(5d). Hc, with α = N H[W ] (d 1,..., d k ) α N W (d i ). To coclud this sctio, w prov Lmma 4.1. Th followig switchig opratios will b particularly usful. W us th otatio itroducd at th bgiig of this sctio, whr w hav a complt graph K whos dg st is -colord with rspct of a giv subgraph H. (i) r-switchig: Giv a graph F cotaiig at last o rd dg, choos a rd dg x F, labl its d vrtics u ad v, ad choos a blu dg x F that is ot icidt with x, labl its d vrtics u ad v. Rplac ths two dgs by uu ad vv. Th r-switchig is applicabl if ad oly if uu ad vv ar blu dgs ad ar ot i F. (ii) ivrs r-switchig: Giv a graph F cotaiig at last two blu dgs, choos a blu dg i F ad labl its d vrtics u ad u, th choos aothr blu dg i F that is ot icidt with uu ad labl its d vrtics v ad v. Rplac ths two dgs by uv ad u v. Th ivrs r-switchig is applicabl if ad oly if uv ad u v ar ot i F ad uv is rd ad u v is blu. Lt d = (d 1,..., d ) b a array of ds squcs such that max d i d. Lt R(l) b th st of all subgraphs F of K with dgr squc d ad l rd dgs. Not that, for vry l 1, a r-switchig opratio covrts a graph F R(l) ito a graph F R(l 1). O th othr had, a ivrs r-switchig covrts a F R(l 1) ito a F R(l). Lt N(F ) b th umbr of r-switchigs applicabl o F ad N (F ) th umbr of ivrs r-switchig applicabl o F. Lt t = ( ) (H ) ad lt c = t /. Proof of Lmma 4.1. For th calculatios prstd hr, w furthr suppos that m = i d i. Th cas m < / must b tratd sparatly. Lt b = (1 da)/8. By th dfiitio of lim sup, thr xists 0 > 0 such that, for all 0, r () < a + 1 da d k = 1 4b, ad b 4d. d Claim 4.3. For all l such that m/d l/d 4d r > 0, ad giv F R(l) ad F R(l 1), w hav l(m l 4d d r ) N(F ) 4lm, 0 N (F ) d c. Proof. Giv F R(l), th umbr of ways to choos th rd dg x ad labl its d vrtics is l. Th umbr of ways to choos th blu dg y ad labl its vrtics is at most m. So N(F ) 4lm. For th lowr boud, oc x has b chos ad its vrtics hav

20 0 C. HOPPEN, H. LEFMANN, K. ODERMANN, AND J. SANCHES b labld, th umbr of ways to choos y ad labl its d vrtics such that y is a blu dg that is ot icidt with x, ad whr uu ad vv ar both blu dgs that do ot li i F, is at last m l 4d d r. Idd, havig chos th rd dg uv, th vrtics u, v caot b choos from ay of th at most r dpoits of th rd dgs lavig u or v. Morovr, w caot choos th dpoits of th at most d dgs i F lavig u or v. Thus, w caot choos at most d( r + d) dgs to sur th right coctio btw u, v ad th pair {u, v }. Th boud follows wh w tak ito accout that w d to labl u ad v ad that u v caot b o of th l rd dgs. Giv F R(l 1), th umbr of ways to choos th rd dg x ad labl its dvrtics is at most c. Morovr, thr ar at most d ways to choos u ad d ways to choos v, so that N (F ) d c. I our cas, th assumptio that m / implis with l b that ( ) 1 4b m l 4d d r m b b d b. d Giv l b, cosidr th auxiliary bipartit graph with bipartitio R(l) R(l 1) whr a lmt i R(l) is adjact to a lmt i R(l 1) if thy ca b obtaid from ach othr by switchig opratios. By coutig th umbr of dgs i this auxiliary graph, usig Claim 4.3, w obtai lb R(l) d c R(l 1), so that R(l) R(l 1) d c bl, ad hc ( R(l) d ) l R(0) c 1 b l!. W shall also mak us of th followig fact about th probability of F havig may rd dgs. Claim 4.4. P(X b) = o(1). Bcaus m s=0 P(X = s) = 1 ad P(X b) = o(1) by th abov claim, w hav s=0 P(X = s) = 1 o(1). Thus, b 1 P(X = 0) = (1 + o(1)) (1 + o(1)) (1 + o(1)) b s=0 b s=0 b s=0 (1 + o(1)) xp P(X = s) P(X = 0) R(s) R(0) ( d ) l c 1 b s! ( d c b Not that this implis th validity of Lmma 4.1, as P(X = 0) 1 ( ) xp d c = 1 ( 8d ) b xp t (1 da) for larg. ). (34) ( 9d ) t > xp (1 da)

21 FORBIDDING RAINBOW-COLORED STARS 1 Proof of Claim 4.4. Thr ar at most c rd dgs i K, so thr ar at most ( c ) b ways to choos b rd dgs to iclud i F. W d to choos th m b rmaiig dgs to form F. To fid a uppr boud o th umbr of ways i which this ca b do, w cosidr th followig way of producig subgraphs of K with dgr squc (d 1,..., d ), whr d i = m: cosidr bis labld 1,..., ad put (labld) balls ito ths bis i such a way that th ith bi cotais d i balls. A pairig of th st of balls is giv by P = {a 1,..., a m } such that ach a i is a uordrd pair of balls, ad ach ball is i prcisly o pair a i. Ay such pairig givs ris to a (multi)graph with dgr squc (d 1,..., d ) by thikig of th ith bi as a vrtx v i, ad statig that v i ad v j ar adjact whvr balls i ths bis ar paird to ach othr. Th raso why w d to mtio multigraphs is that, i a pairig, two balls i th sam bi could b paird (which would produc a loop), or two or mor balls i o bi could b paird to balls i som othr bi (which would produc multipl dgs). It is ot difficult to s that ach (simpl) graph with dgr squc (d 1,..., d ) corrspods to th sam umbr of pairigs (amly d 1! d!), which is th basic ida i th cofiguratio modl for radom rgular graphs (s [5, 1] for a dtaild xplaatio.) I our cotxt, sic w hav alrady chos b rd dgs to iclud i F, w may assum that, wh producig F usig pairigs, w hav paird b balls, so that w d to fid a pairig of th m b rmaiig balls. As th umbr of ways of doig this is th umbr of prfct matchigs i K m b, w coclud that it may b do i at most ( ) (m b)! m b m b m b (m b)! = (1 + o(1)) ways. Th approximatio usd Stirlig s formula. Cosqutly, th umbr of subgraphs of K with dgr squc d that cotai at last b rd dgs is for larg at most ( ) ( ) c m b m b. b is W kow that th umbr of such subgraphs with o rstrictio o th umbr of rd dgs N (d) = d i! ( ) m m xp( λ λ ). With d i d, i = 1,..., ad λ = (1/(m)) ) i < d w ifr that ( ) m m N (d) > (d!) xp( d d 4 ). Thrfor, usig ( k) (/k) k, w hav P(X b) ( c b ( di ) ( m b N (d) ) m b ) m b ( ) c b ( b m b ) m xp( d d 4 ) (d!) ( m ( c ) b b (d!) xp(d + d 4 ) ( m ) b = ( ) b c (d!) xp(d + d 4 ), bm

EDGE-COLORINGS AVOIDING RAINBOW STARS

EDGE-COLORINGS AVOIDING RAINBOW STARS EDGE-COLORINGS AVOIDING RAINBOW STARS CARLOS HOPPEN, HANNO LEFMANN, KNUT ODERMANN, AND JULIANA SANCHES Abstract. We cosider a extremal problem motivated by a paper of Balogh [J. Balogh, A remark o the

More information

z 1+ 3 z = Π n =1 z f() z = n e - z = ( 1-z) e z e n z z 1- n = ( 1-z/2) 1+ 2n z e 2n e n -1 ( 1-z )/2 e 2n-1 1-2n -1 1 () z

z 1+ 3 z = Π n =1 z f() z = n e - z = ( 1-z) e z e n z z 1- n = ( 1-z/2) 1+ 2n z e 2n e n -1 ( 1-z )/2 e 2n-1 1-2n -1 1 () z Sris Expasio of Rciprocal of Gamma Fuctio. Fuctios with Itgrs as Roots Fuctio f with gativ itgrs as roots ca b dscribd as follows. f() Howvr, this ifiit product divrgs. That is, such a fuctio caot xist

More information

A Simple Proof that e is Irrational

A Simple Proof that e is Irrational Two of th most bautiful ad sigificat umbrs i mathmatics ar π ad. π (approximatly qual to 3.459) rprsts th ratio of th circumfrc of a circl to its diamtr. (approximatly qual to.788) is th bas of th atural

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

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

H2 Mathematics Arithmetic & Geometric Series ( )

H2 Mathematics Arithmetic & Geometric Series ( ) H Mathmatics Arithmtic & Gomtric Sris (08 09) Basic Mastry Qustios Arithmtic Progrssio ad Sris. Th rth trm of a squc is 4r 7. (i) Stat th first four trms ad th 0th trm. (ii) Show that th squc is a arithmtic

More information

APPENDIX: STATISTICAL TOOLS

APPENDIX: STATISTICAL TOOLS I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.

More information

NEW VERSION OF SZEGED INDEX AND ITS COMPUTATION FOR SOME NANOTUBES

NEW VERSION OF SZEGED INDEX AND ITS COMPUTATION FOR SOME NANOTUBES Digst Joural of Naomatrials ad Biostructurs Vol 4, No, March 009, p 67-76 NEW VERSION OF SZEGED INDEX AND ITS COMPUTATION FOR SOME NANOTUBES A IRANMANESH a*, O KHORMALI b, I NAJAFI KHALILSARAEE c, B SOLEIMANI

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

Restricted Factorial And A Remark On The Reduced Residue Classes

Restricted Factorial And A Remark On The Reduced Residue Classes Applid Mathmatics E-Nots, 162016, 244-250 c ISSN 1607-2510 Availabl fr at mirror sits of http://www.math.thu.du.tw/ am/ Rstrictd Factorial Ad A Rmark O Th Rducd Rsidu Classs Mhdi Hassai Rcivd 26 March

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

Statistics 3858 : Likelihood Ratio for Exponential Distribution

Statistics 3858 : Likelihood Ratio for Exponential Distribution Statistics 3858 : Liklihood Ratio for Expotial Distributio I ths two xampl th rjctio rjctio rgio is of th form {x : 2 log (Λ(x)) > c} for a appropriat costat c. For a siz α tst, usig Thorm 9.5A w obtai

More information

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges.

Option 3. b) xe dx = and therefore the series is convergent. 12 a) Divergent b) Convergent Proof 15 For. p = 1 1so the series diverges. Optio Chaptr Ercis. Covrgs to Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Divrgs 8 Divrgs Covrgs to Covrgs to Divrgs Covrgs to Covrgs to Covrgs to Covrgs to 8 Proof Covrgs to π l 8 l a b Divrgt π Divrgt

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

On the approximation of the constant of Napier

On the approximation of the constant of Napier Stud. Uiv. Babş-Bolyai Math. 560, No., 609 64 O th approximatio of th costat of Napir Adri Vrscu Abstract. Startig from som oldr idas of [] ad [6], w show w facts cocrig th approximatio of th costat of

More information

DTFT Properties. Example - Determine the DTFT Y ( e ) of n. Let. We can therefore write. From Table 3.1, the DTFT of x[n] is given by 1

DTFT Properties. Example - Determine the DTFT Y ( e ) of n. Let. We can therefore write. From Table 3.1, the DTFT of x[n] is given by 1 DTFT Proprtis Exampl - Dtrmi th DTFT Y of y α µ, α < Lt x α µ, α < W ca thrfor writ y x x From Tabl 3., th DTFT of x is giv by ω X ω α ω Copyright, S. K. Mitra Copyright, S. K. Mitra DTFT Proprtis DTFT

More information

Chapter Five. More Dimensions. is simply the set of all ordered n-tuples of real numbers x = ( x 1

Chapter Five. More Dimensions. is simply the set of all ordered n-tuples of real numbers x = ( x 1 Chatr Fiv Mor Dimsios 51 Th Sac R W ar ow rard to mov o to sacs of dimsio gratr tha thr Ths sacs ar a straightforward gralizatio of our Euclida sac of thr dimsios Lt b a ositiv itgr Th -dimsioal Euclida

More information

Session : Plasmas in Equilibrium

Session : Plasmas in Equilibrium Sssio : Plasmas i Equilibrium Ioizatio ad Coductio i a High-prssur Plasma A ormal gas at T < 3000 K is a good lctrical isulator, bcaus thr ar almost o fr lctros i it. For prssurs > 0.1 atm, collisio amog

More information

Law of large numbers

Law of large numbers Law of larg umbrs Saya Mukhrj W rvisit th law of larg umbrs ad study i som dtail two typs of law of larg umbrs ( 0 = lim S ) p ε ε > 0, Wak law of larrg umbrs [ ] S = ω : lim = p, Strog law of larg umbrs

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

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

07 - SEQUENCES AND SERIES Page 1 ( Answers at he end of all questions ) b, z = n

07 - SEQUENCES AND SERIES Page 1 ( Answers at he end of all questions ) b, z = n 07 - SEQUENCES AND SERIES Pag ( Aswrs at h d of all qustios ) ( ) If = a, y = b, z = c, whr a, b, c ar i A.P. ad = 0 = 0 = 0 l a l

More information

Linear Algebra Existence of the determinant. Expansion according to a row.

Linear Algebra Existence of the determinant. Expansion according to a row. Lir Algbr 2270 1 Existc of th dtrmit. Expsio ccordig to row. W dfi th dtrmit for 1 1 mtrics s dt([]) = (1) It is sy chck tht it stisfis D1)-D3). For y othr w dfi th dtrmit s follows. Assumig th dtrmit

More information

Digital Signal Processing, Fall 2006

Digital Signal Processing, Fall 2006 Digital Sigal Procssig, Fall 6 Lctur 9: Th Discrt Fourir Trasfor Zhg-Hua Ta Dpartt of Elctroic Systs Aalborg Uivrsity, Dar zt@o.aau.d Digital Sigal Procssig, I, Zhg-Hua Ta, 6 Cours at a glac MM Discrt-ti

More information

On Deterministic Finite Automata and Syntactic Monoid Size, Continued

On Deterministic Finite Automata and Syntactic Monoid Size, Continued O Dtrmiistic Fiit Automata ad Sytactic Mooid Siz, Cotiud Markus Holzr ad Barbara Köig Istitut für Iformatik, Tchisch Uivrsität Müch, Boltzmastraß 3, D-85748 Garchig bi Müch, Grmay mail: {holzr,koigb}@iformatik.tu-much.d

More information

ln x = n e = 20 (nearest integer)

ln x = n e = 20 (nearest integer) H JC Prlim Solutios 6 a + b y a + b / / dy a b 3/ d dy a b at, d Giv quatio of ormal at is y dy ad y wh. d a b () (,) is o th curv a+ b () y.9958 Qustio Solvig () ad (), w hav a, b. Qustio d.77 d d d.77

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

The Equitable Dominating Graph

The Equitable Dominating Graph Intrnational Journal of Enginring Rsarch and Tchnology. ISSN 0974-3154 Volum 8, Numbr 1 (015), pp. 35-4 Intrnational Rsarch Publication Hous http://www.irphous.com Th Equitabl Dominating Graph P.N. Vinay

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

Figure 2-18 Thevenin Equivalent Circuit of a Noisy Resistor

Figure 2-18 Thevenin Equivalent Circuit of a Noisy Resistor .8 NOISE.8. Th Nyquist Nois Thorm W ow wat to tur our atttio to ois. W will start with th basic dfiitio of ois as usd i radar thory ad th discuss ois figur. Th typ of ois of itrst i radar thory is trmd

More information

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei.

They must have different numbers of electrons orbiting their nuclei. They must have the same number of neutrons in their nuclei. 37 1 How may utros ar i a uclus of th uclid l? 20 37 54 2 crtai lmt has svral isotops. Which statmt about ths isotops is corrct? Thy must hav diffrt umbrs of lctros orbitig thir ucli. Thy must hav th sam

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

10. Joint Moments and Joint Characteristic Functions

10. Joint Moments and Joint Characteristic Functions 0 Joit Momts ad Joit Charactristic Fctios Followig sctio 6 i this sctio w shall itrodc varios paramtrs to compactly rprst th iformatio cotaid i th joit pdf of two rvs Giv two rvs ad ad a fctio g x y dfi

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

Chapter (8) Estimation and Confedence Intervals Examples

Chapter (8) Estimation and Confedence Intervals Examples Chaptr (8) Estimatio ad Cofdc Itrvals Exampls Typs of stimatio: i. Poit stimatio: Exampl (1): Cosidr th sampl obsrvatios, 17,3,5,1,18,6,16,10 8 X i i1 17 3 5 118 6 16 10 116 X 14.5 8 8 8 14.5 is a poit

More information

INTRODUCTION TO SAMPLING DISTRIBUTIONS

INTRODUCTION TO SAMPLING DISTRIBUTIONS http://wiki.stat.ucla.du/socr/id.php/socr_courss_2008_thomso_econ261 INTRODUCTION TO SAMPLING DISTRIBUTIONS By Grac Thomso INTRODUCTION TO SAMPLING DISTRIBUTIONS Itro to Samplig 2 I this chaptr w will

More information

MONTGOMERY COLLEGE Department of Mathematics Rockville Campus. 6x dx a. b. cos 2x dx ( ) 7. arctan x dx e. cos 2x dx. 2 cos3x dx

MONTGOMERY COLLEGE Department of Mathematics Rockville Campus. 6x dx a. b. cos 2x dx ( ) 7. arctan x dx e. cos 2x dx. 2 cos3x dx MONTGOMERY COLLEGE Dpartmt of Mathmatics Rockvill Campus MATH 8 - REVIEW PROBLEMS. Stat whthr ach of th followig ca b itgratd by partial fractios (PF), itgratio by parts (PI), u-substitutio (U), or o of

More information

2617 Mark Scheme June 2005 Mark Scheme 2617 June 2005

2617 Mark Scheme June 2005 Mark Scheme 2617 June 2005 Mark Schm 67 Ju 5 GENERAL INSTRUCTIONS Marks i th mark schm ar plicitly dsigatd as M, A, B, E or G. M marks ("mthod" ar for a attmpt to us a corrct mthod (ot mrly for statig th mthod. A marks ("accuracy"

More information

Probability & Statistics,

Probability & Statistics, Probability & Statistics, BITS Pilai K K Birla Goa Campus Dr. Jajati Kshari Sahoo Dpartmt of Mathmatics BITS Pilai, K K Birla Goa Campus Poisso Distributio Poisso Distributio: A radom variabl X is said

More information

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation.

Section 6.1. Question: 2. Let H be a subgroup of a group G. Then H operates on G by left multiplication. Describe the orbits for this operation. MAT 444 H Barclo Spring 004 Homwork 6 Solutions Sction 6 Lt H b a subgroup of a group G Thn H oprats on G by lft multiplication Dscrib th orbits for this opration Th orbits of G ar th right costs of H

More information

Discrete Fourier Transform. Nuno Vasconcelos UCSD

Discrete Fourier Transform. Nuno Vasconcelos UCSD Discrt Fourir Trasform uo Vascoclos UCSD Liar Shift Ivariat (LSI) systms o of th most importat cocpts i liar systms thory is that of a LSI systm Dfiitio: a systm T that maps [ ito y[ is LSI if ad oly if

More information

STIRLING'S 1 FORMULA AND ITS APPLICATION

STIRLING'S 1 FORMULA AND ITS APPLICATION MAT-KOL (Baja Luka) XXIV ()(08) 57-64 http://wwwimviblorg/dmbl/dmblhtm DOI: 075/МК80057A ISSN 0354-6969 (o) ISSN 986-588 (o) STIRLING'S FORMULA AND ITS APPLICATION Šfkt Arslaagić Sarajvo B&H Abstract:

More information

A GENERALIZED RAMANUJAN-NAGELL EQUATION RELATED TO CERTAIN STRONGLY REGULAR GRAPHS

A GENERALIZED RAMANUJAN-NAGELL EQUATION RELATED TO CERTAIN STRONGLY REGULAR GRAPHS #A35 INTEGERS 4 (204) A GENERALIZED RAMANUJAN-NAGELL EQUATION RELATED TO CERTAIN STRONGLY REGULAR GRAPHS B d Wgr Faculty of Mathmatics ad Computr Scic, Eidhov Uivrsity of Tchology, Eidhov, Th Nthrlads

More information

Technical Support Document Bias of the Minimum Statistic

Technical Support Document Bias of the Minimum Statistic Tchical Support Documt Bias o th Miimum Stattic Itroductio Th papr pla how to driv th bias o th miimum stattic i a radom sampl o siz rom dtributios with a shit paramtr (also kow as thrshold paramtr. Ths

More information

BOUNDS FOR THE COMPONENTWISE DISTANCE TO THE NEAREST SINGULAR MATRIX

BOUNDS FOR THE COMPONENTWISE DISTANCE TO THE NEAREST SINGULAR MATRIX SIAM J. Matrix Aal. Appl. (SIMAX), 8():83 03, 997 BOUNDS FOR THE COMPONENTWISE DISTANCE TO THE NEAREST SINGULAR MATRIX S. M. RUMP Abstract. Th ormwis distac of a matrix A to th arst sigular matrix is wll

More information

Further Results on Pair Sum Graphs

Further Results on Pair Sum Graphs Applid Mathmatis, 0,, 67-75 http://dx.doi.org/0.46/am.0.04 Publishd Oli Marh 0 (http://www.sirp.org/joural/am) Furthr Rsults o Pair Sum Graphs Raja Poraj, Jyaraj Vijaya Xavir Parthipa, Rukhmoi Kala Dpartmt

More information

CDS 101: Lecture 5.1 Reachability and State Space Feedback

CDS 101: Lecture 5.1 Reachability and State Space Feedback CDS, Lctur 5. CDS : Lctur 5. Rachability ad Stat Spac Fdback Richard M. Murray ad Hido Mabuchi 5 Octobr 4 Goals: Di rachability o a cotrol systm Giv tsts or rachability o liar systms ad apply to ampls

More information

Chapter Taylor Theorem Revisited

Chapter Taylor Theorem Revisited Captr 0.07 Taylor Torm Rvisitd Atr radig tis captr, you sould b abl to. udrstad t basics o Taylor s torm,. writ trascdtal ad trigoomtric uctios as Taylor s polyomial,. us Taylor s torm to id t valus o

More information

CDS 101: Lecture 5.1 Reachability and State Space Feedback

CDS 101: Lecture 5.1 Reachability and State Space Feedback CDS, Lctur 5. CDS : Lctur 5. Rachability ad Stat Spac Fdback Richard M. Murray 7 Octobr 3 Goals: Di rachability o a cotrol systm Giv tsts or rachability o liar systms ad apply to ampls Dscrib th dsig o

More information

Review Exercises. 1. Evaluate using the definition of the definite integral as a Riemann Sum. Does the answer represent an area? 2

Review Exercises. 1. Evaluate using the definition of the definite integral as a Riemann Sum. Does the answer represent an area? 2 MATHEMATIS --RE Itgral alculus Marti Huard Witr 9 Rviw Erciss. Evaluat usig th dfiitio of th dfiit itgral as a Rima Sum. Dos th aswr rprst a ara? a ( d b ( d c ( ( d d ( d. Fid f ( usig th Fudamtal Thorm

More information

UNIT 2: MATHEMATICAL ENVIRONMENT

UNIT 2: MATHEMATICAL ENVIRONMENT UNIT : MATHEMATICAL ENVIRONMENT. Itroductio This uit itroducs som basic mathmatical cocpts ad rlats thm to th otatio usd i th cours. Wh ou hav workd through this uit ou should: apprciat that a mathmatical

More information

LECTURE 13 Filling the bands. Occupancy of Available Energy Levels

LECTURE 13 Filling the bands. Occupancy of Available Energy Levels LUR 3 illig th bads Occupacy o Availabl rgy Lvls W hav dtrmid ad a dsity o stats. W also d a way o dtrmiig i a stat is illd or ot at a giv tmpratur. h distributio o th rgis o a larg umbr o particls ad

More information

Blackbody Radiation. All bodies at a temperature T emit and absorb thermal electromagnetic radiation. How is blackbody radiation absorbed and emitted?

Blackbody Radiation. All bodies at a temperature T emit and absorb thermal electromagnetic radiation. How is blackbody radiation absorbed and emitted? All bodis at a tmpratur T mit ad absorb thrmal lctromagtic radiatio Blackbody radiatio I thrmal quilibrium, th powr mittd quals th powr absorbd How is blackbody radiatio absorbd ad mittd? 1 2 A blackbody

More information

Lectures 9 IIR Systems: First Order System

Lectures 9 IIR Systems: First Order System EE3054 Sigals ad Systms Lcturs 9 IIR Systms: First Ordr Systm Yao Wag Polytchic Uivrsity Som slids icludd ar xtractd from lctur prstatios prpard by McCllla ad Schafr Lics Ifo for SPFirst Slids This work

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

SCHUR S THEOREM REU SUMMER 2005

SCHUR S THEOREM REU SUMMER 2005 SCHUR S THEOREM REU SUMMER 2005 1. Combinatorial aroach Prhas th first rsult in th subjct blongs to I. Schur and dats back to 1916. On of his motivation was to study th local vrsion of th famous quation

More information

CPSC 665 : An Algorithmist s Toolkit Lecture 4 : 21 Jan Linear Programming

CPSC 665 : An Algorithmist s Toolkit Lecture 4 : 21 Jan Linear Programming CPSC 665 : An Algorithmist s Toolkit Lctur 4 : 21 Jan 2015 Lcturr: Sushant Sachdva Linar Programming Scrib: Rasmus Kyng 1. Introduction An optimization problm rquirs us to find th minimum or maximum) of

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

On a problem of J. de Graaf connected with algebras of unbounded operators de Bruijn, N.G.

On a problem of J. de Graaf connected with algebras of unbounded operators de Bruijn, N.G. O a problm of J. d Graaf coctd with algbras of uboudd oprators d Bruij, N.G. Publishd: 01/01/1984 Documt Vrsio Publishr s PDF, also kow as Vrsio of Rcord (icluds fial pag, issu ad volum umbrs) Plas chck

More information

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple 5/24/5 A PROOF OF THE CONTINUED FRACTION EXPANSION OF / Thomas J Oslr INTRODUCTION This ar givs aothr roof for th rmarkabl siml cotiud fractio = 3 5 / Hr is ay ositiv umbr W us th otatio x= [ a; a, a2,

More information

5.1 The Nuclear Atom

5.1 The Nuclear Atom Sav My Exams! Th Hom of Rvisio For mor awsom GSE ad lvl rsourcs, visit us at www.savmyxams.co.uk/ 5.1 Th Nuclar tom Qustio Papr Lvl IGSE Subjct Physics (0625) Exam oard Topic Sub Topic ooklt ambridg Itratioal

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 Minimum Cut Problem

1 Minimum Cut Problem CS 6 Lctur 6 Min Cut and argr s Algorithm Scribs: Png Hui How (05), Virginia Dat: May 4, 06 Minimum Cut Problm Today, w introduc th minimum cut problm. This problm has many motivations, on of which coms

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

The Interplay between l-max, l-min, p-max and p-min Stable Distributions

The Interplay between l-max, l-min, p-max and p-min Stable Distributions DOI: 0.545/mjis.05.4006 Th Itrplay btw lma lmi pma ad pmi Stabl Distributios S Ravi ad TS Mavitha Dpartmt of Studis i Statistics Uivrsity of Mysor Maasagagotri Mysuru 570006 Idia. Email:ravi@statistics.uimysor.ac.i

More information

Mixing time with Coupling

Mixing time with Coupling Mixig im wih Couplig Jihui Li Mig Zhg Saisics Dparm May 7 Goal Iroducio o boudig h mixig im for MCMC wih couplig ad pah couplig Prsig a simpl xampl o illusra h basic ida Noaio M is a Markov chai o fii

More information

NET/JRF, GATE, IIT JAM, JEST, TIFR

NET/JRF, GATE, IIT JAM, JEST, TIFR Istitut for NET/JRF, GATE, IIT JAM, JEST, TIFR ad GRE i PHYSICAL SCIENCES Mathmatical Physics JEST-6 Q. Giv th coditio φ, th solutio of th quatio ψ φ φ is giv by k. kφ kφ lφ kφ lφ (a) ψ (b) ψ kφ (c) ψ

More information

8(4 m0) ( θ ) ( ) Solutions for HW 8. Chapter 25. Conceptual Questions

8(4 m0) ( θ ) ( ) Solutions for HW 8. Chapter 25. Conceptual Questions Solutios for HW 8 Captr 5 Cocptual Qustios 5.. θ dcrass. As t crystal is coprssd, t spacig d btw t plas of atos dcrass. For t first ordr diffractio =. T Bragg coditio is = d so as d dcrass, ust icras for

More information

Motivation. We talk today for a more flexible approach for modeling the conditional probabilities.

Motivation. We talk today for a more flexible approach for modeling the conditional probabilities. Baysia Ntworks Motivatio Th coditioal idpdc assuptio ad by aïv Bays classifirs ay s too rigid spcially for classificatio probls i which th attributs ar sowhat corrlatd. W talk today for a or flibl approach

More information

On spanning trees and cycles of multicolored point sets with few intersections

On spanning trees and cycles of multicolored point sets with few intersections On spanning trs and cycls of multicolord point sts with fw intrsctions M. Kano, C. Mrino, and J. Urrutia April, 00 Abstract Lt P 1,..., P k b a collction of disjoint point sts in R in gnral position. W

More information

CS 361 Meeting 12 10/3/18

CS 361 Meeting 12 10/3/18 CS 36 Mting 2 /3/8 Announcmnts. Homwork 4 is du Friday. If Friday is Mountain Day, homwork should b turnd in at my offic or th dpartmnt offic bfor 4. 2. Homwork 5 will b availabl ovr th wknd. 3. Our midtrm

More information

In its simplest form the prime number theorem states that π(x) x/(log x). For a more accurate version we define the logarithmic sum, ls(x) = 2 m x

In its simplest form the prime number theorem states that π(x) x/(log x). For a more accurate version we define the logarithmic sum, ls(x) = 2 m x THREE PRIMES T Hardy Littlwood circl mtod is usd to prov Viogradov s torm: vry sufficitly larg odd itgr is t sum of tr prims Toy Forbs Nots for LSBU Matmatics Study Group Fbruary Backgroud W sall closly

More information

International Journal of Advanced and Applied Sciences

International Journal of Advanced and Applied Sciences Itratioal Joural of Advacd ad Applid Scics x(x) xxxx Pags: xx xx Cotts lists availabl at Scic Gat Itratioal Joural of Advacd ad Applid Scics Joural hompag: http://wwwscic gatcom/ijaashtml Symmtric Fuctios

More information

Derivation of a Predictor of Combination #1 and the MSE for a Predictor of a Position in Two Stage Sampling with Response Error.

Derivation of a Predictor of Combination #1 and the MSE for a Predictor of a Position in Two Stage Sampling with Response Error. Drivatio of a Prdictor of Cobiatio # ad th SE for a Prdictor of a Positio i Two Stag Saplig with Rspos Error troductio Ed Stak W driv th prdictor ad its SE of a prdictor for a rado fuctio corrspodig to

More information

Theoretical Analysis of Cross-Validation for Estimating the Risk of the k-nearest Neighbor Classifier

Theoretical Analysis of Cross-Validation for Estimating the Risk of the k-nearest Neighbor Classifier Joural of Machi Larig Rsarch 18 018 1-54 Submittd 09/15; Rvisd 07/18; Publishd 11/18 Thortical Aalysis of Cross-Validatio for Estimatig th Ris of th -Narst Nighbor Classifir Alai Cliss Laboratoir d Mathématiqus

More information

MATH 681 Notes Combinatorics and Graph Theory I. ( 4) n. This will actually turn out to be marvelously simplifiable: C n = 2 ( 4) n n + 1. ) (n + 1)!

MATH 681 Notes Combinatorics and Graph Theory I. ( 4) n. This will actually turn out to be marvelously simplifiable: C n = 2 ( 4) n n + 1. ) (n + 1)! MATH 681 Nots Combiatorics ad Graph Thory I 1 Catala umbrs Prviously, w usd gratig fuctios to discovr th closd form C = ( 1/ +1) ( 4). This will actually tur out to b marvlously simplifiabl: ( ) 1/ C =

More information

Narayana IIT Academy

Narayana IIT Academy INDIA Sc: LT-IIT-SPARK Dat: 9--8 6_P Max.Mars: 86 KEY SHEET PHYSIS A 5 D 6 7 A,B 8 B,D 9 A,B A,,D A,B, A,B B, A,B 5 A 6 D 7 8 A HEMISTRY 9 A B D B B 5 A,B,,D 6 A,,D 7 B,,D 8 A,B,,D 9 A,B, A,B, A,B,,D A,B,

More information

Chapter 4 - The Fourier Series

Chapter 4 - The Fourier Series M. J. Robrts - 8/8/4 Chaptr 4 - Th Fourir Sris Slctd Solutios (I this solutio maual, th symbol,, is usd for priodic covolutio bcaus th prfrrd symbol which appars i th txt is ot i th fot slctio of th word

More information

KISS: A Bit Too Simple. Greg Rose

KISS: A Bit Too Simple. Greg Rose KI: A Bit Too impl Grg Ros ggr@qualcomm.com Outli KI radom umbr grator ubgrators Efficit attack N KI ad attack oclusio PAGE 2 O approach to PRNG scurity "A radom umbr grator is lik sx: Wh it's good, its

More information

Gaps in samples of geometric random variables

Gaps in samples of geometric random variables Discrt Mathmatics 37 7 871 89 Not Gaps i sampls of gomtric radom variabls William M.Y. Goh a, Pawl Hitczko b,1 a Dpartmt of Mathmatics, Drxl Uivrsity, Philadlphia, PA 1914, USA b Dpartmts of Mathmatics

More information

Calculus & analytic geometry

Calculus & analytic geometry Calculus & aalytic gomtry B Sc MATHEMATICS Admissio owards IV SEMESTER CORE COURSE UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION CALICUT UNIVERSITYPO, MALAPPURAM, KERALA, INDIA 67 65 5 School of Distac

More information

A Note on Quantile Coupling Inequalities and Their Applications

A Note on Quantile Coupling Inequalities and Their Applications A Not o Quatil Couplig Iqualitis ad Thir Applicatios Harriso H. Zhou Dpartmt of Statistics, Yal Uivrsity, Nw Hav, CT 06520, USA. E-mail:huibi.zhou@yal.du Ju 2, 2006 Abstract A rlatioship btw th larg dviatio

More information

A Review of Complex Arithmetic

A Review of Complex Arithmetic /0/005 Rviw of omplx Arithmti.do /9 A Rviw of omplx Arithmti A omplx valu has both a ral ad imagiary ompot: { } ad Im{ } a R b so that w a xprss this omplx valu as: whr. a + b Just as a ral valu a b xprssd

More information

(Reference: sections in Silberberg 5 th ed.)

(Reference: sections in Silberberg 5 th ed.) ALE. Atomic Structur Nam HEM K. Marr Tam No. Sctio What is a atom? What is th structur of a atom? Th Modl th structur of a atom (Rfrc: sctios.4 -. i Silbrbrg 5 th d.) Th subatomic articls that chmists

More information

Problem Set 6 Solutions

Problem Set 6 Solutions 6.04/18.06J Mathmatics for Computr Scinc March 15, 005 Srini Dvadas and Eric Lhman Problm St 6 Solutions Du: Monday, March 8 at 9 PM in Room 3-044 Problm 1. Sammy th Shark is a financial srvic providr

More information

Hadamard Exponential Hankel Matrix, Its Eigenvalues and Some Norms

Hadamard Exponential Hankel Matrix, Its Eigenvalues and Some Norms Math Sci Ltt Vol No 8-87 (0) adamard Exotial al Matrix, Its Eigvalus ad Som Norms İ ad M bula Mathmatical Scics Lttrs Itratioal Joural @ 0 NSP Natural Scics Publishig Cor Dartmt of Mathmatics, aculty of

More information

Empirical Study in Finite Correlation Coefficient in Two Phase Estimation

Empirical Study in Finite Correlation Coefficient in Two Phase Estimation M. Khoshvisa Griffith Uivrsity Griffith Busiss School Australia F. Kaymarm Massachustts Istitut of Tchology Dpartmt of Mchaical girig USA H. P. Sigh R. Sigh Vikram Uivrsity Dpartmt of Mathmatics ad Statistics

More information

Bipolar Junction Transistors

Bipolar Junction Transistors ipolar Juctio Trasistors ipolar juctio trasistors (JT) ar activ 3-trmial dvics with aras of applicatios: amplifirs, switch tc. high-powr circuits high-spd logic circuits for high-spd computrs. JT structur:

More information

Homework #3. 1 x. dx. It therefore follows that a sum of the

Homework #3. 1 x. dx. It therefore follows that a sum of the Danil Cannon CS 62 / Luan March 5, 2009 Homwork # 1. Th natural logarithm is dfind by ln n = n 1 dx. It thrfor follows that a sum of th 1 x sam addnd ovr th sam intrval should b both asymptotically uppr-

More information

Folding of Hyperbolic Manifolds

Folding of Hyperbolic Manifolds It. J. Cotmp. Math. Scics, Vol. 7, 0, o. 6, 79-799 Foldig of Hyprbolic Maifolds H. I. Attiya Basic Scic Dpartmt, Collg of Idustrial Educatio BANE - SUEF Uivrsity, Egypt hala_attiya005@yahoo.com Abstract

More information

(Upside-Down o Direct Rotation) β - Numbers

(Upside-Down o Direct Rotation) β - Numbers Amrican Journal of Mathmatics and Statistics 014, 4(): 58-64 DOI: 10593/jajms0140400 (Upsid-Down o Dirct Rotation) β - Numbrs Ammar Sddiq Mahmood 1, Shukriyah Sabir Ali,* 1 Dpartmnt of Mathmatics, Collg

More information

Propositional Logic. Combinatorial Problem Solving (CPS) Albert Oliveras Enric Rodríguez-Carbonell. May 17, 2018

Propositional Logic. Combinatorial Problem Solving (CPS) Albert Oliveras Enric Rodríguez-Carbonell. May 17, 2018 Propositional Logic Combinatorial Problm Solving (CPS) Albrt Olivras Enric Rodríguz-Carbonll May 17, 2018 Ovrviw of th sssion Dfinition of Propositional Logic Gnral Concpts in Logic Rduction to SAT CNFs

More information

DFT: Discrete Fourier Transform

DFT: Discrete Fourier Transform : Discrt Fourir Trasform Cogruc (Itgr modulo m) I this sctio, all lttrs stad for itgrs. gcd m, = th gratst commo divisor of ad m Lt d = gcd(,m) All th liar combiatios r s m of ad m ar multils of d. a b

More information

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia Part B: Trasform Mthods Chaptr 3: Discrt-Tim Fourir Trasform (DTFT) 3. Discrt Tim Fourir Trasform (DTFT) 3. Proprtis of DTFT 3.3 Discrt Fourir Trasform (DFT) 3.4 Paddig with Zros ad frqucy Rsolutio 3.5

More information

Solution of Assignment #2

Solution of Assignment #2 olution of Assignmnt #2 Instructor: Alirza imchi Qustion #: For simplicity, assum that th distribution function of T is continuous. Th distribution function of R is: F R ( r = P( R r = P( log ( T r = P(log

More information

How many neutrino species?

How many neutrino species? ow may utrio scis? Two mthods for dtrmii it lium abudac i uivrs At a collidr umbr of utrio scis Exasio of th uivrs is ovrd by th Fridma quatio R R 8G tot Kc R Whr: :ubblcostat G :Gravitatioal costat 6.

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

BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES. 1. Statement of results

BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES. 1. Statement of results BINOMIAL COEFFICIENTS INVOLVING INFINITE POWERS OF PRIMES DONALD M. DAVIS Abstract. If p is a prim and n a positiv intgr, lt ν p (n dnot th xponnt of p in n, and u p (n n/p νp(n th unit part of n. If α

More information

Discrete Mathematics and Probability Theory Fall 2014 Anant Sahai Homework 11. This homework is due November 17, 2014, at 12:00 noon.

Discrete Mathematics and Probability Theory Fall 2014 Anant Sahai Homework 11. This homework is due November 17, 2014, at 12:00 noon. EECS 70 Discrt Mathmatics ad Probability Thory Fall 2014 Aat Sahai Homwork 11 This homwork is du Novmbr 17, 2014, at 12:00 oo. 1. Sctio Rollcall! I your slf-gradig for this qustio, giv yourslf a 10, ad

More information

ONLINE SUPPLEMENT Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand

ONLINE SUPPLEMENT Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand Submittd to Maufacturig & Srvic Opratios Maagmt mauscript MSOM 5-4R2 ONLINE SUPPLEMENT Optimal Markdow Pricig ad Ivtory Allocatio for Rtail Chais with Ivtory Dpdt Dmad Stph A Smith Dpartmt of Opratios

More information