The diameter of the isomorphism class of a Banach space

Size: px
Start display at page:

Download "The diameter of the isomorphism class of a Banach space"

Transcription

1 Annals of Mathematcs, 162 (2005), The dameter of the somorphsm class of a Banach space By W. B. Johnson and E. Odell* Dedcated to the memory of V. I. Gurar Abstract We prove that f X s a separable nfnte dmensonal Banach space then ts somorphsm class has nfnte dameter wth respect to the Banach-Mazur dstance. One step n the proof s to show that f X s elastc then X contans an somorph of c 0. We call X elastc f for some K< for every Banach space Y whch embeds nto X, the space Y s K-somorphc to a subspace of X. We also prove that f X s a separable Banach space such that for some K< every somorph of X s K-elastc then X s fnte dmensonal. 1. Introducton Gven a Banach space X, let D(X) be the dameter n the Banach-Mazur dstance of the class of all Banach spaces whch are somorphc to X; that s, D(X) = sup{d(x 1,X 2 ):X 1,X 2 are somorphc to X} where d(x 1,X 2 ) s the nfmum over all somorphsms T from X 1 onto X 2 of T T 1. It s well known that f X s fnte (say, N) dmensonal, then cn D(X) N for some postve constant c whch s ndependent of N. The upper bound s an mmedate consequence of the classcal result (see e.g. [T-J, p. 54]) that d(y,l N 2 ) N for every N dmensonal space Y. The lower bound s due to Gluskn [G], [T-J, p. 283]. It s natural to conjecture that D(X) must be nfnte when X s nfnte dmensonal, but ths problem remans open. As far as we know, ths problem was frst rased n prnt n the 1976 book of J. J. Schäffer [S, p. 99]. The problem was recently brought to the attenton of the authors by V. I. Gurar, who *Johnson was supported n part by NSF DMS , Texas Advanced Research Program , and the U.S.-Israel Bnatonal Scence Foundaton. Odell was supported n part by NSF DMS and was a partcpant n the NSF supported Workshop n Lnear Analyss and Probablty at Texas A&M Unversty.

2 424 W. B. JOHNSON AND E. ODELL checked that every nfnte dmensonal super-reflexve space as well as each of the common classcal Banach spaces has an somorphsm class whose dameter s nfnte. To see these cases, note that f X s nfnte dmensonal and E s any fnte dmensonal space, then t s clear that X s somorphc to E 2 X 0 for some space X 0. Therefore, f D(X) s fnte, then X s fntely complementably unversal; that s, there s a constant C so that every fnte dmensonal space s C-somorphc to a C-complemented subspace of X. Ths mples that X cannot have nontrval type or nontrval cotype or local uncondtonal structure or numerous other structures. In partcular, X cannot be any of the classcal spaces or be super-reflexve. In hs unpublshed 1968 thess [Mc], McGugan conjectured that D(X) must be larger than one when dm X > 1. Schäffer [S, p. 99] derved that D(X) 6/5 when dm X>1as a consequence of other geometrcal results contaned n [S], but one can prove drectly that D(X) 2. Indeed, t s clearly enough to get an approprate lower bound on the Banach-Mazur dstance between X 1 := Y 1 R and X 2 := Y 2 R when Y s a nonzero Banach space. Now X 1 has a one dmensonal subspace for whch every two dmensonal superspace s sometrc to l 2 1. On the other hand, every one dmensonal subspace of X 2 s contaned n a two dmensonal superspace whch s sometrc to l 2 2. It follows that d(x 1,X 2 ) d(l 2 1,l2 2 )= 2. The Man Theorem n ths paper s a soluton to Schäffer s problem for separable Banach spaces: Man Theorem. If X s a separable nfnte dmensonal Banach space, then D(X) =. Part of the work for provng the Man Theorem nvolves showng that f X s separable and D(X) <, then X contans an somorph of c 0. Ths proof s nherently not local n nature, and, strangely enough, local consderatons, such as those mentoned earler whch yeld partal results, play no role n our proof. We do not see how to prove that a nonseparable space X for whch D(X) < must contan an somorph of c 0. Our proof requres Bourgan s ndex theory whch n turn requres separablty. Our method of proof nvolves the concept of an elastc Banach space. Say that X s K-elastc provded that f a Banach space Y embeds nto X then Y must K-embed nto X (that s, there s an somorphsm T from Y nto X wth y Ty K y for all y Y ). Ths s the same (by Lemma 2) as sayng that every space somorphc to X must K-embed nto X. X s sad to be elastc f t s K-elastc for some K<. Obvously, f D(X) < then X as well as every somorph of X s D(X)- elastc. Thus the Man Theorem s an mmedate consequence of

3 THE ISOMORPHISM CLASS OF A BANACH SPACE 425 Theorem 1. If X s a separable Banach space and there s a K so that every somorph of X s K-elastc, then X s fnte dmensonal. A key step n our argument nvolves showng that an elastc space X admts a normalzed weakly null sequence havng a spreadng model not equvalent to ether the unt vector bass of c 0 or l 1. To acheve ths we frst prove (Theorem 7) that f X s elastc then c 0 embeds nto X. It s reasonable to conjecture that an elastc nfnte dmensonal separable Banach space must contan an somorph of C[0, 1]. Theorem 1 would be an mmedate consequence of ths conjecture and the arbtrary dstortablty of C[0, 1] proved n [LP]. Our dervaton of Theorem 1 from Theorem 7 uses deas from [LP] as well as [MR]. Wth the letters X, Y, Z,... we wll denote separable nfnte dmensonal real Banach spaces unless otherwse ndcated. Y X wll mean that Y s a closed (nfnte dmensonal) subspace of X. The closed lnear span of the set A s denoted [A]. We use standard Banach space theory termnology, as can be found n [LT]. The materal we use on spreadng models can be found n [BL]. For smplcty we assume real scalars, but all proofs can easly be adapted for complex Banach spaces. 2. The man result The followng lemma [Pe] shows that the two defntons of elastc mentoned n Secton 1 are equvalent. Lemma 2. Let Y (X, ) and let be an equvalent norm on (Y, ). Then can be extended to an equvalent norm on X. Proof. There exst postve reals C and d wth d y y C y for y Y. Let F CB X be a set of Hahn-Banach extensons of all elements of S (Y, ) to all of X. Forx X defne x = sup { f(x) : f F } d x. Let n N and K <. We shall call a basc sequence (x ) block n-uncondtonal wth constant K f every block bass (y ) n =1 of (x ) s K- uncondtonal; that s, n n ±a y K a y =1 =1 for all scalars (a ) n =1 and all choces of ±. The next lemma s essentally contaned n [LP]. In fact, by usng the slghtly more nvolved argument n [LP], the concluson wth constant 2 can

4 426 W. B. JOHNSON AND E. ODELL be changed to wth constant 1 + ε, whch mples that the constant n the concluson of Lemma 4 can be changed from 2 + ε to 1 + ε. Lemma 3. Let X be a Banach space wth a bass (x ). For every n there s an equvalent norm n on X so that n (X, n ), (x ) s block n-uncondtonal wth constant 2. Proof. Let (P n ) be the sequence of bass projectons assocated wth (x n ). We may assume, by passng to an equvalent norm on X, that (x n ) s bmonotone and hence P j P = 1 for all <j. Let S n be the class of operators S on X of the form S = m k=1 ( 1)k (P nk P nk 1 ) where 0 n 0 < <n m and m n. Defne x n := sup{ Sx : S S n }. Thus x x n n x for x X. It suffces to show that for S S n, S n 2. Let x span (x n ) and Sx n = TSx for some T S n. Then snce TS S 2n S n + S n, TSx 2 x n. Lemma 4. For every separable Banach space X and n N there exsts an equvalent norm on X so that for every ε>0, every normalzed weakly null sequence n X admts a block n-uncondtonal subsequence wth constant 2+ε. Proof. Snce C[0, 1] has a bass, the lemma follows from Lemma 3 and the the classcal fact that every separable Banach space 1-embeds nto C[0, 1]. Lemma 4 s false for some nonseparable spaces. Partngton [P] and Talagrand [T] proved that every somorph of l contans, for every ε>0, a1+ε-sometrc copy of l and hence of every separable Banach space. Our next lemma s an extenson of the Maurey-Rosenthal constructon [MR], or rather the footnote to t gven by one of the authors (Example 3 n [MR]). We frst recall the constructon of spreadng models. If (y n )sa normalzed basc sequence then, gven ε n 0, one can use Ramsey s theorem and a dagonal argument to fnd a subsequence (x n )of(y n ) wth the followng property. For all m n N and (a ) m =1 [ 1, 1], f m 1 < < m and m j 1 < <j m, then m m a k x k a k x jk <ε m. k=1 k=1 It follows that for all m and (a ) m =1 R, m m lm... lm a k x k a k x k 1 m k=1 k=1

5 THE ISOMORPHISM CLASS OF A BANACH SPACE 427 exsts. The sequence ( x ) s then a bass for the completon of (span ( x ), ) and ( x ) s called a spreadng model of (x ). If (x ) s weakly null, then ( x ) s 2-uncondtonal. One shows ths by checkng that ( x ) s suppresson 1-uncondtonal, whch means that for all scalars (a ) m =1 and F {1,...,m}, m a x a x. F Also, (x ) s 1-spreadng, whch means that for all scalars (a ) m =1 and all n(1) < <n(m), m m a x = a x n(). =1 It s not dffcult to see that, when (x ) s weakly null, ( x ) s not equvalent to the unt vector bass of c 0 (respectvely, l 1 ) f and only f lm m m =1 x = (respectvely, lm m m =1 x /m = 0). All of these facts can be found n [BL]. Lemma 5. Let (x n ) be a normalzed weakly null basc sequence wth spreadng model ( x n ). Assume that ( x n ) s not equvalent to ether the unt vector bass of l 1 or the unt vector bass of c 0. Then for all C < there exst n N, a subsequence (y ) of (x ), and an equvalent norm on [(y )] so that (y ) s -normalzed and no subsequence of (y ) s block n-uncondtonal wth constant C for the norm. Proof. Recall that f (e ) n 1 s normalzed and 1-spreadng and bmonotone then n 1 e n 1 e 2n where (e )n 1 s borthogonal to (e ) n 1 [LT, p. 118]. n 1 Thus e = e n 1 n 1 e s normed by f = e n n =1 e, precsely f(e) =1= e, and f 2. These facts allow us to deduce that there s a subsequence (y ) of (x ) so that f F N s admssble (that s, F mn F ) then f F F y ( ) y F satsfes f F 5 and f F (y F ) = 1, where F y F y F y. Indeed, ( x ) s 1-spreadng and suppresson 1-uncondtonal (snce (x ) s weakly null). Gven 1/2 > ε > 0 we can fnd (y ) (x ) so that f F N s admssble then (y ) F s 1+ε-equvalent to ( x ) F =1. Furthermore we can choose (y ) so that f F s admssble then for y = a y, F a y (2 + ε) y (for a proof see [O] or [BL]). Hence f F (2 + ε) f F [y] F < 5 for suffcently small ε by our above remarks. We are ready to produce a Maurey-Rosenthal type renormng. Choose n so that n>7c and let ε>0 satsfy n 2 ε<1. We choose a subsequence =1 =1 F

6 428 W. B. JOHNSON AND E. ODELL M =(m j ) j=1 of N so that m 1 = 1 and for j and for all admssble sets F and G wth F = m and G = m j, a) k F y k k G y k <ε,fm <m j and b) k F y k k G y k m j m <ε,fm >m j. Indeed, we have chosen (y ) so that 1 F F x y 2 x 2 =1 F and smlarly for G. Snce ( x ) s not equvalent to the unt vector bass of c 0 (and s uncondtonal) lm m m 1 x = so that a) wll be satsfed f (m k ) ncreases suffcently rapdly. Furthermore, snce ( x ) s not equvalent to the m unt vector bass of l 1, lm x 1 m m = 0 and so b) can also be acheved. For N set A = {y F : F s admssble and F = m } and A = {f F : F s admssble and F = m }. Let φ be an njecton nto M from the collecton of all (F 1,...,F ) where <nand F 1 <F 2 < <F are fnte subsets of N. Here F<Gmeans max F<mn G. Let { n F = : F 1 < <F n, F 1 = m 1 =1, each F s admssble =1 f F =1 } and F +1 = φ(f 1,...,F ) for 1 <n. For y [(y )] let { } y F = sup f(y) : f F and set y = y F ε y. Ths s an equvalent norm snce for f F, f 5n. Also, y = 1 for all. Note that f f F A and y G A j wth j then f F (y G ) = k F y k ( y k G y ) k k m k F k G y k F y k k m m j k G y. k m If m <m j then f F (y G ) <εby a). If m >m j then f F (y G ) <εby b). It follows that f y = n =1 y F and f = n =1 f F F then y f(y) =n and f z = n =1 ( 1) y F, then for all g = n j=1 f G j F, g(z) 6+n 2 ε<7. Indeed, we may assume that g f and f F 1 G 1 then G j F for all 1 < n and 1 j n and so by a), b), n n ) n n g(z) = f Gj( ( 1) y F f Gj (y F ) <n 2 ε. j=1 =1 j=1 =1

7 THE ISOMORPHISM CLASS OF A BANACH SPACE 429 Otherwse there exsts 1 j 0 <nso that F j = G j for j j 0, F j0+1 = G j0+1 and F G j for j 0 +1<,j n. Usng f Gj0 +1(z) 5+nε we obtan j 0 g(z) f Gj (z) + f n G j0 +1(z) + f Gj (z) j=1 j>j 0+1 < 1+5+nε +(n (j 0 + 1))nε < 6+n 2 ε. Hence z 7 follows and the lemma s proved snce n/7 >Cand such vectors y and z can be produced n any subsequence of (y ). Our next lemma follows from Proposton 3.2 n [AOST]. Lemma 6. Let X be a Banach space. Assume that for all n, (x n ) =1 s a normalzed weakly null sequence n X havng spreadng model ( x n ) whch s not equvalent to the unt vector bass of l 1. Then there exsts a normalzed weakly null sequence (y ) X wth spreadng model (ỹ ) such that (ỹ ) s not equvalent to the unt vector bass of l 1. Moreover, there exsts λ>0 so that for all n, λ2 n a x n a ỹ for all (a ) R. Theorem 7. Let X be elastc, separable and nfnte dmensonal. Then c 0 embeds nto X. We postpone the proof to complete frst the Proof of Theorem 1. Assume that X s nfnte dmensonal and every somorph of X s K-elastc. Then by Theorem 7, c 0 embeds nto X. Choose k n so that 2 n k n. Usng the renormngs of c 0 by { } (a ) n = sup a : F = kn F and that X s K-elastc we can fnd for all n a normalzed weakly null sequence (x n ) =1 X wth spreadng model ( xn ) =1 satsfyng a x n K 1 (a ) n and moreover each ( x n ) s equvalent to the unt vector bass of c 0. Thus by Lemma 6 there exsts a normalzed weakly null sequence (y )nx havng spreadng model (ỹ ) whch s not equvalent to the unt vector bass of l 1 and whch satsfes for all n, k n ỹ λk 1 2 n k n. 1 Thus (ỹ ) s not equvalent to the unt vector bass of c 0 as well.

8 430 W. B. JOHNSON AND E. ODELL By Lemmas 2 and 5, for all C< we can fnd n N and a renormng Y of X so that Y contans a normalzed weakly null sequence admttng no subsequence whch s block n-uncondtonal wth constant C. By the assumpton on X, the space Y must K-embed nto every somorph of X. But f C s large enough ths contradcts Lemma 4. It remans to prove Theorem 7. We shall employ an ndex argument nvolvng l -trees defned on Banach spaces. If Y s a Banach space our trees T on Y wll be countable. For some C the nodes of T wll be elements (y ) n 1 Y wth (y ) n 1 bmonotone basc and satsfyng 1 y and n 1 ±y C for all choces of sgn. Thus (y ) n 1 s C-equvalent to the unt vector bass of ln. T s partally ordered by (x ) n 1 (y ) m 1 f n m and x = y for n. The order o(t ) s gven as follows. If T s not well founded (.e., T has an nfnte branch), then o(t )=ω 1. Otherwse we set for such a tree S, S = {(x ) n 1 S :(x ) n 1 s not a maxmal node}. Set T 0 = T, T 1 = T and n general T α+1 =(T α ) and T α = β<α T β f α s a lmt ordnal. Then o(t ) = nf{α : T α = φ}. By Bourgan s ndex theory [B1], [B2] (see also [AGR]), f X s separable and contans for all β<ω 1 such a tree of ndex at least β, then c 0 embeds nto X. We now complete the Proof of Theorem 7. Wthout loss of generalty we may assume that X Z where Z has a bmonotone bass (z ). Let X be K-elastc. We wll often use sem-normalzed sequences n X whch are a tny perturbaton of a block bass of (z ) and to smplfy the estmates we wll assume below that they are n fact a block bass of (z ). For example, f (y ) s a normalzed basc sequence n X then we call (d ) a dfference sequence of (y )fd = y k(2) y k(2+1) for some k 1 <k 2 <.We can always choose such a (d ) to be a sem-normalzed perturbaton of a block bass of (z ) by frst passng to a subsequence (y )of(y ) so that lm zj (y ) exsts for all j, where (z ) s borthogonal to (z ), and takng (d )tobea sutable dfference sequence of (y ). We wll assume then that (d ) s n fact a block bass of (z ). We nductvely construct for each lmt ordnal β<ω 1, a Banach space Y β that embeds nto X. Y β wll have a normalzed bmonotone bass (y β ) that can be enumerated as (y β ) =1 = {yβ,ρ,n : ρ C β, n N, N} where C β s some countable set. The order s such that (y β,ρ,n ) =1 s a subsequence of (yβ ) for fxed ρ and n. Before statng the remanng propertes of (y β ) we need some termnology. We say that (w )sacompatble dfference sequence of (y β ) of order 1f(w )

9 THE ISOMORPHISM CLASS OF A BANACH SPACE 431 s a dfference sequence of (y β ) that can be enumerated as follows, (w )={w β,ρ,n : ρ C β,n, N} and such that for fxed ρ and n, (w β,ρ,n ) s a dfference sequence of (y β,ρ,n+1 ). If (v ) s a compatble dfference sequence of (w ) of order 1, n the above sense, (v ) wll be called a compatble dfference sequence of (y β ) of order 2, and so on. (y β ) wll be sad to have order 0. Let (v ) be a compatble dfference sequence of (y β ) of some fnte order. We set T ( (v ) ) = { (u ) s 1 : the u s are dstnct elements of {v } 1, possbly n dfferent order, and s 1 } ±u = 1 for all choces of sgn. T ( (v ) ) s then an l -tree as descrbed above wth C = 1. The nductve condton on Y β, or should we say on (y β,ρ,n ), s that for all compatble dfference sequences (v )of(y β,ρ,n ) of fnte order, o(t ((v ))) β. Before proceedng we have an elementary but key Sublemma. Let C< and let (w ) be a block bass of a bmonotone bass (z ) wth 1 w C for all and let A = {F N : F s fnte and F ±w C for all choces of sgn}. Then there exsts an equvalent norm on [(w )] so that (w ) s a bmonotone normalzed bass such that for all F A, ±w =1. Proof. Defne a w = (a ) C 1 a w. F We begn by constructng Y ω. Let (x ) X be a normalzed block bass of (z ). For n N, let n be an equvalent norm on [(x )] gven by the sublemma for C =2 n.thus F ±x n =1f F 2 n. Snce X s K-elastc, for all n, ([(x )], n ) K-embeds nto X. We thus obtan for n N, a sequence (x n ) X wth 1 x n K for all and such that 1 F ±xn K for all F 2n and all choces of sgn. Furthermore

10 432 W. B. JOHNSON AND E. ODELL (x n ) s K-basc. By standard perturbaton and dagonal arguments we may for each n pass to a dfference sequence (d n ) of (x n+1 ) so that enumeratng, (d )={d n : n, N} s a block bass of (z ) wth 1 d K and wth each (d n ) beng a subsequence of (d ). We have that for F 2 n and all sgns, 1 F ±d n K. We renorm [(d )] by the sublemma for C = K and let the ensung space be Y ω. We change the name of (d )to(y ω ) n ths new norm and let (yω,1,n ) = (d n ).(y ω) has the property that f (w ) s a compatble dfference sequence of (y ω) of fnte order, then o(t ((w ))) ω. Indeed, f (w )={w ω,1,n : n N, N} then for F 2 n, F ±wω,1,n =1. Assume that Y β has been constructed for the lmt ordnal β wth bass (y β )={yβ,ρ,n : p C β, n, N} wth the requste propertes above. Let U : Y ω X and V : Y β X be K-embeddngs. Snce n total we are dealng wth a countable set of sequences, namely (y ω,1,n ) for n N and (y β,ρ,n ) for p C β, n N, by dagonalzaton and perturbaton we can fnd a compatble dfference sequence (w ω) of (y ω) of order 1 and a compatble dfference sequence (w β ) of (y β ) of order 1 so that under a sutable reorderng, (d )=(Uw ω) (Vw β ) s a block bass of (z ). Moreover each (Uw ω,1,n ) and (Vw β,ρ,n ) s a subsequence of (d ). Adjon a new pont p 0 to C β and set C β+ω = C β {ρ 0 }. Let d β+ω,ρ,n = Vw β,ρ,n for ρ C β and d β+ω,ρ0,n = Uw ω,1,n.for F 2 n and G C β N N for whch = 1, we have by the trangle nequalty that ( ) (ρ,n,) G F ±w β,ρ,n ±d β+ω,ρ0,n + ±d β+ω,ρ,n (ρ,n,) G 2K. It follows that f we let (y β+ω ) be the bass (d β+ω ), renormed by the sublemma for C =2K, that Y β+ω =[(y β+ω )] has the requred propertes. Indeed ( ) yelds that o(t ((y β+ω ))) β +2 n for n N and so o(t ((y β+ω ))) β + ω. Moreover by our nducton hypothess and choce of (d β+ω,p0,n ) the analogue of ( ) holds for a compatble dfference sequence of order 1 or of any fnte order. Thus o(t (z β+ω )) β + ω for any compatble dfference sequence of (y β+ω ) of fnte order. If β s a lmt ordnal not of the form α + ω we let U α : Y α X be a K-embeddng for each lmt ordnal α<β. We agan dagonalze to form compatble dfference sequences (w α) of (y α ) of order 1 for each such α so that (U α w α) α, s a block bass of (z ) n some order. We let C β be a dsjont

11 THE ISOMORPHISM CLASS OF A BANACH SPACE 433 unon of the C α s and n the manner above obtan Y β. Agan by our nducton hypothess we wll have that o(t ((y β ))) α for all lmt ordnals α<βand the same holds for all compatble dfference sequences of (y β ) of fnte order. 3. Concludng remarks and problems C[0, 1] s, of course, 1-elastc. By vrtue of Lemma 4 (or Theorem 1), for all K, C[0, 1] can be renormed to be elastc but not K-elastc. Are there other examples of separable elastc spaces? Problem 8. Let X be elastc (and separable, say). nto X? Does C[0, 1] embed Usng ndex arguments, we have the followng partal result. Proposton 9. Let X be a separable Banach space, and suppose that Y = X n s a symmetrc decomposton of a space Y nto spaces unformly somorphc to X. If Y s elastc, then C[0, 1] embeds nto X. In partcular, f 1 p< and l p (X) s elastc, then C[0, 1] embeds nto X. Proof. Let us frst observe that f C[0, 1] embeds nto Y, then C[0, 1] embeds nto X. Snce ths s surely well known, we just sketch a proof (whch, ncdentally, uses only that the decomposton Y = X n of Y s uncondtonal): Let P n be the projecton from Y onto X n and let W be a subspace of Y whch s somorphc to C[0, 1]. By a theorem of Rosenthal s [R], t s enough to show that for some n, the adjont P n W of the restrcton of P n to W has nonseparable range. Ths wll be true f there s an m so that S m W has nonseparable range, where S m := m =1 P. Let Z be a subspace of W whch s somorphc to l 1. If no such m exsts, then for every m, the restrcton of S m to Z s strctly sngular (that s, not an somorphsm on any nfnte dmensonal subspace of Z), and t then follows that Z contans a sequence (x n ) of unt vectors whch s an arbtrarly small perturbaton of a sequence (y n ) whch s dsjontly supported. The sequence (y n ), a fortor (x n ), s then easly seen to be equvalent to the unt vector bass of l 1 and ts closed span s complemented n Y snce the decomposton s uncondtonal. It follows that l 1 s somorphc to a complemented subspace of C[0, 1], whch of course s false. To complete the proof of Proposton 9, we assume that Y s K-elastc and prove that C[0, 1] embeds nto Y. The proof s smlar to, but smpler than, the proof of Theorem 7. Frst we recall the defnton of certan canoncal trees T α of order α for α<ω 1 (see e.g. [JO]). These form the frames upon whch we wll hang our bases. The tree T 1 s a sngle node. If T α has been defned, we choose a new node z T α and set T α+1 := T α {z}, ordered by z<tfor

12 434 W. B. JOHNSON AND E. ODELL all t T α and wth T α preservng ts order. If β<ω 1 s a lmt ordnal, we let T β be the dsjont unon of {T α : α<β}. Then f s, t are n T β, we say that s t f and only f s, t are both n T α for some α<βand s t n T α. We shall prove by transfnte nducton that f (x ) =1 s any normalzed bmonotone basc sequence and α<ω 1, there s a Banach space Y α = Y α (x ) wth a normalzed bmonotone bass (yt α ) t Tα so that f (γ ) n =1 s any branch n T α then (yγ α ) n =1 s 1-equvalent to (x ) n =1 and for all scalars (a r) t Tα, n =1 a γ yγ α t T α a t yt α. Furthermore, each Y α wll K-embed nto Y. Just as n the proof of Theorem 7, t then follows from ndex theory that the Banach space spanned by (x ) =1 embeds nto Y. Fx any normalzed bmonotone basc sequence (x ) =1. Suppose that β s a lmt ordnal and Y α = Y α (x ) =1 has been defned for all α<β. In vew of the hypotheses, the space Y has a symmetrc decomposton nto spaces unformly somorphc Y, whch we can ndex as Y = α<β X α. For each α, there s an somorphsm L α from Y α nto X α so that L α = 1 and L 1 α s bounded ndependently of α. We can put an equvalent norm on Y = α<β X α to make each L α an sometry and make the decomposton 1-uncondtonal (but not necessarly 1-symmetrc). Defne Y β to be the closed lnear span of {L α Y α : α<β} n Y wth ts new norm. The space Y β has the desred bass ndexed by T β and Y α must K-embed nto Y wth ts orgnal norm because Y s K-elastc. If β = α +1,weletY β (x ) =1 = R Y α(x ) =2 wth the norm gven by (a, y) := y sup{ ax 1 + n =2 y α γ (y)y α γ : (γ ) n =2 s a branch n T α }. Agan t s clear that Y β must K-embed nto Y and that Y β has the desred bass. Gven metrc spaces X and Y, the Lpschtz dstance d L (X, Y ) between them s defned to be the nfmum of Lp (f) Lp (f 1 ), where the nfmum s taken over all blpschtz mappngs f from X onto Y (the nfmum of the empty set s ). Call a Banach space X Lpschtz K-elastc provded that every somorph of X has Lpschtz dstance at most K to some subset of X, and say that X s Lpschtz elastc f X s Lpschtz K-elastc for some K<. It s nterestng to note that the analogue of Theorem 1 for Lpschtz elastcty s false. Indeed, Aharon [A], [BeL, Theorem 7.11] proved that every separable metrc space has, for each ɛ>0, Lpschtz dstance less than 3 + ɛ to some subset of c 0, whle James [J], [BeL, Proposton 13.6] proved that for each ɛ>0, the space c 0 s 1 + ɛ-equvalent to a subspace of every somorph of c 0. Consequently, any separable Banach space whch contans an somorphc copy of c 0 s Lpschtz 3 + ɛ-elastc.

13 THE ISOMORPHISM CLASS OF A BANACH SPACE 435 Problem 10. If X s a separable, nfnte dmensonal Banach space and there s a K so that every somorph of X s Lpschtz K-elastc, must X contan a subspace somorphc to c 0? We also do not know whether the Man Theorem has a Lpschtz analogue: Problem 11. If X s a separable Banach space and there exsts K< so that d L (X, Y ) <Kfor all spaces Y whch are somorphc (or blpschtz equvalent) to X, then must X be fnte dmensonal? The space c 0 does not have the property mentoned n Problem 11 (use, for example, the fact [GKL] that for every K< there exsts K < so that f a Banach space X s K blpschtz equvalent to c 0 then d(x, c 0 ) K ). Snce Schäffer s problem remans open n the nonseparable settng, we menton t explctly as: Problem 12. Does there exst a nonseparable Banach space X for whch D(X) <? Also open s: Problem 13. Does there exst a nonseparable Banach space X and a K< so that all somorphs of X are K-elastc? Under the generalzed contnuum hypothess, t follows from [E] that for every cardnal ℵ there s a Banach space X of densty character ℵ so that every Banach space of densty character ℵ s sometrcally somorphc to a subspace of X. These provde the only known examples of elastc Banach spaces. Problem 14. If X s elastc and nfnte dmensonal, does X contan an somorph of every Banach space whose densty character s the same as the densty character of X? As was mentoned earler, the space l s, for every ɛ>0, 1+ɛ-somorphc to a subspace of every somorph of tself (see [P] and [T]). However, l s not elastc. To see ths, take a borthogonal system (x α,x α) α<2 ℵ 0 n l wth x α = 1 and x α bounded. Gven a natural number m, renorm l by ( ) 1/2 1 x m := max m x, sup x α(x) 2 σ =m. It s not hard to see that (l, m ) s not better than m-normed by any countable set of norm one functonals, whch mples that d((l, m ),Y) m for any subspace Y of l. A smlar argument shows that l (ℵ) s not elastc for any nfnte cardnal number ℵ. α σ

14 436 W. B. JOHNSON AND E. ODELL Texas A&M Unversty, College Staton, TX E-mal address: The Unversty of Texas at Austn, Austn, TX E-mal address: References [A] I. Aharon, Every separable metrc space s Lpschtz equvalent to a subset of c + 0, Israel J. Math. 19 (1974), [AGR] S. Argyros, G. Godefroy, and H. Rosenthal, Descrptve set theory and Banach spaces, n Handbook of the Geometry of Banach Spaces, Vol. 2 (W. B. Johnson and J. Lndenstrauss, eds.) North-Holland, Amsterdam (2003), [AOST] G. Androulaks, E. Odell, Th. Schlumprecht, and N. Tomczak-Jaegermann, On the structure of the spreadng models of a Banach space, Canadan J. Math. 57 (2005), [BL] B. Beauzamy and J.-T. Lapresté, Modèles Étalés des Espaces de Banach, ntravaux en Cours, Hermann, Pars, [BeL] Y. Benyamn and J. Lndenstrauss, Geometrc Nonlnear Functonal Analyss. Vol. 1, Amer. Math. Soc. Colloq. Publ. 48, A.M.S., Provdence, RI (2000). [B1] J. Bourgan, On convergent sequences of contnuous functons, Bull. Soc. Math. Belg. Ser. B32 (1980), [B2], On separable Banach spaces, unversal for all separable reflexve spaces, Proc. Amer. Math. Soc. 79 (1980), [E] A. S. Esenn-Vol pn, On the exstence of a unversal bcompactum of arbtrary weght, Dokl. Akad. Nauk SSSR 68 (1949), [G] E. D. Gluskn, The dameter of the Mnkowsk compactum s roughly equal to n, Funct. Anal. Appl. 15 (1981), [GKL] G. Godefroy, N. Kalton, and G. Lancen, Subspaces of c 0(N) and Lpschtz somorphsms, Geom. Funct. Anal. 10 (2000), [J] R. C. James, Unformly non-square Banach spaces, Ann. of Math. 80 (1964), [JO] R. Judd and E. Odell, Concernng Bourgan s l 1-ndex of a Banach space, Israel J. Math. 108 (1998), [LP] J. Lndenstrauss and A. Pe lczyńsk, Contrbutons to the theory of the classcal Banach spaces, J. Funct. Anal. 8 (1971), [LT] J. Lndenstrauss and L. Tzafrr, Classcal Banach Spaces I. Sequence Spaces, Sprnger-Verlag New York (1977). [Mc] R. A. McGugan, Jr., Near sometry of Banach spaces and the Banach-Mazur dstance, Ph.D. Thess, Unversty of Maryland, College Park (1968). [MR] B. Maurey and H. P. Rosenthal, Normalzed weakly null sequence wth no uncondtonal subsequence, Studa Math. 61 (1977), [O] E. Odell, On Schreer uncondtonal sequences, n Banach Spaces (Mérda, 1992), Contemp. Math. 144, , A.M.S., Provdence, RI, [P] J. R. Partngton, Subspaces of certan Banach sequence spaces, Bull. London Math. Soc. 13 (1981),

15 THE ISOMORPHISM CLASS OF A BANACH SPACE 437 [Pe] A. Pe lczyńsk, Projectons n certan Banach spaces, Studa Math. 19 (1960), [R] H. P. Rosenthal, On factors of C([0, 1]) wth non-separable dual, Israel J. Math. 13 (1972), [S] J. J. Schäffer, Geometry of Spheres n Normed Spaces, Lecture Notes Pure Appl. Math. 20, Marcel Dekker, Inc., New York (1976). [T] M. Talagrand, Sur les espaces de Banach contenant l 1 (τ), Israel J. Math. 40 (1981), [T-J] N. Tomczak-Jaegermann, Banach-Mazur Dstances and Fnte-dmensonal Operator Ideals, Ptman Monographs and Surveys n Pure and Appled Mathematcs 38, Longman Scentfc & Techncal, Harlow (1989). (Receved September 23, 2003)

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

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

More information

1 Example 1: Axis-aligned rectangles

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

More information

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

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

More information

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

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

More information

Recurrence. 1 Definitions and main statements

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

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

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

More information

Embedding lattices in the Kleene degrees

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

More information

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

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

More information

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

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

More information

Extending Probabilistic Dynamic Epistemic Logic

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

More information

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

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

More information

REGULAR MULTILINEAR OPERATORS ON C(K) SPACES

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

More information

Ring structure of splines on triangulations

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

More information

A Probabilistic Theory of Coherence

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

More information

Support Vector Machines

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

More information

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

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

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

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

More information

Generalizing the degree sequence problem

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

More information

PERRON FROBENIUS THEOREM

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

More information

How To Calculate The Accountng Perod Of Nequalty

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

More information

BERNSTEIN POLYNOMIALS

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

More information

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

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

More information

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

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

More information

Least Squares Fitting of Data

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

More information

COLLOQUIUM MATHEMATICUM

COLLOQUIUM MATHEMATICUM COLLOQUIUM MATHEMATICUM VOL. 74 997 NO. TRANSFERENCE THEORY ON HARDY AND SOBOLEV SPACES BY MARIA J. CARRO AND JAVIER SORIA (BARCELONA) We show that the transference method of Cofman and Wess can be extended

More information

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

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

More information

Calculation of Sampling Weights

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

More information

F-Rational Rings and the Integral Closures of Ideals

F-Rational Rings and the Integral Closures of Ideals Mchgan Math. J. 49 (2001) F-Ratonal Rngs and the Integral Closures of Ideals Ian M. Aberbach & Crag Huneke 1. Introducton The hstory of the Brançon Skoda theorem and ts ensung avatars n commutatve algebra

More information

Fisher Markets and Convex Programs

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

More information

L10: Linear discriminants analysis

L10: Linear discriminants analysis L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss

More information

What is Candidate Sampling

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

More information

The OC Curve of Attribute Acceptance Plans

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

More information

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks

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

More information

FINITE HILBERT STABILITY OF (BI)CANONICAL CURVES

FINITE HILBERT STABILITY OF (BI)CANONICAL CURVES FINITE HILBERT STABILITY OF (BICANONICAL CURVES JAROD ALPER, MAKSYM FEDORCHUK, AND DAVID ISHII SMYTH* To Joe Harrs on hs sxteth brthday Abstract. We prove that a generc canoncally or bcanoncally embedded

More information

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

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

More information

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

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

More information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence 1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

More information

SIMPLE LINEAR CORRELATION

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

More information

Stability, observer design and control of networks using Lyapunov methods

Stability, observer design and control of networks using Lyapunov methods Stablty, observer desgn and control of networks usng Lyapunov methods von Lars Naujok Dssertaton zur Erlangung des Grades enes Doktors der Naturwssenschaften - Dr. rer. nat. - Vorgelegt m Fachberech 3

More information

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia To appear n Journal o Appled Probablty June 2007 O-COSTAT SUM RED-AD-BLACK GAMES WITH BET-DEPEDET WI PROBABILITY FUCTIO LAURA POTIGGIA, Unversty o the Scences n Phladelpha Abstract In ths paper we nvestgate

More information

Complete Fairness in Secure Two-Party Computation

Complete Fairness in Secure Two-Party Computation Complete Farness n Secure Two-Party Computaton S. Dov Gordon Carmt Hazay Jonathan Katz Yehuda Lndell Abstract In the settng of secure two-party computaton, two mutually dstrustng partes wsh to compute

More information

8 Algorithm for Binary Searching in Trees

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

More information

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

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

More information

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

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

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

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

More information

Equlbra Exst and Trade S effcent proportionally

Equlbra Exst and Trade S effcent proportionally On Compettve Nonlnear Prcng Andrea Attar Thomas Marott Franços Salané February 27, 2013 Abstract A buyer of a dvsble good faces several dentcal sellers. The buyer s preferences are her prvate nformaton,

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

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

More information

Do Hidden Variables. Improve Quantum Mechanics?

Do Hidden Variables. Improve Quantum Mechanics? Radboud Unverstet Njmegen Do Hdden Varables Improve Quantum Mechancs? Bachelor Thess Author: Denns Hendrkx Begeleder: Prof. dr. Klaas Landsman Abstract Snce the dawn of quantum mechancs physcst have contemplated

More information

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

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

More information

J. Parallel Distrib. Comput.

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

More information

Sngle Snk Buy at Bulk Problem and the Access Network

Sngle Snk Buy at Bulk Problem and the Access Network A Constant Factor Approxmaton for the Sngle Snk Edge Installaton Problem Sudpto Guha Adam Meyerson Kamesh Munagala Abstract We present the frst constant approxmaton to the sngle snk buy-at-bulk network

More information

General Auction Mechanism for Search Advertising

General Auction Mechanism for Search Advertising General Aucton Mechansm for Search Advertsng Gagan Aggarwal S. Muthukrshnan Dávd Pál Martn Pál Keywords game theory, onlne auctons, stable matchngs ABSTRACT Internet search advertsng s often sold by an

More information

Nonbinary Quantum Error-Correcting Codes from Algebraic Curves

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

More information

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

More information

Loop Parallelization

Loop Parallelization - - Loop Parallelzaton C-52 Complaton steps: nested loops operatng on arrays, sequentell executon of teraton space DECLARE B[..,..+] FOR I :=.. FOR J :=.. I B[I,J] := B[I-,J]+B[I-,J-] ED FOR ED FOR analyze

More information

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP) 6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

An Alternative Way to Measure Private Equity Performance

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

More information

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network 700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

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

More information

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

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

More information

Uniform topologies on types

Uniform topologies on types Theoretcal Economcs 5 (00), 445 478 555-756/000445 Unform topologes on types Y-Chun Chen Department of Economcs, Natonal Unversty of Sngapore Alfredo D Tllo IGIER and Department of Economcs, Unverstà Lug

More information

The Distribution of Eigenvalues of Covariance Matrices of Residuals in Analysis of Variance

The Distribution of Eigenvalues of Covariance Matrices of Residuals in Analysis of Variance JOURNAL OF RESEARCH of the Natonal Bureau of Standards - B. Mathem atca l Scence s Vol. 74B, No.3, July-September 1970 The Dstrbuton of Egenvalues of Covarance Matrces of Resduals n Analyss of Varance

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

Laws of Electromagnetism

Laws of Electromagnetism There are four laws of electromagnetsm: Laws of Electromagnetsm The law of Bot-Savart Ampere's law Force law Faraday's law magnetc feld generated by currents n wres the effect of a current on a loop of

More information

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

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

More information

Natural hp-bem for the electric field integral equation with singular solutions

Natural hp-bem for the electric field integral equation with singular solutions Natural hp-bem for the electrc feld ntegral equaton wth sngular solutons Alexe Bespalov Norbert Heuer Abstract We apply the hp-verson of the boundary element method (BEM) for the numercal soluton of the

More information

Irreversibility for all bound entangled states

Irreversibility for all bound entangled states Irreversblty for all bound entangled states Barbara Synak-Radtke Insttute of Theoretcal Physcs and Astrophyscs Unversty of Gdańsk Cooperaton: D. Yang, M. Horodeck, R.Horodeck [PRL 95, 190501 (2005, quant/ph-

More information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa

More information

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson

More information

Quantization Effects in Digital Filters

Quantization Effects in Digital Filters Quantzaton Effects n Dgtal Flters Dstrbuton of Truncaton Errors In two's complement representaton an exact number would have nfntely many bts (n general). When we lmt the number of bts to some fnte value

More information

The Mathematical Derivation of Least Squares

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

More information

Matrix Multiplication I

Matrix Multiplication I Matrx Multplcaton I Yuval Flmus February 2, 2012 These notes are based on a lecture gven at the Toronto Student Semnar on February 2, 2012. The materal s taen mostly from the boo Algebrac Complexty Theory

More information

Hedging Interest-Rate Risk with Duration

Hedging Interest-Rate Risk with Duration FIXED-INCOME SECURITIES Chapter 5 Hedgng Interest-Rate Rsk wth Duraton Outlne Prcng and Hedgng Prcng certan cash-flows Interest rate rsk Hedgng prncples Duraton-Based Hedgng Technques Defnton of duraton

More information

Rotation Kinematics, Moment of Inertia, and Torque

Rotation Kinematics, Moment of Inertia, and Torque Rotaton Knematcs, Moment of Inerta, and Torque Mathematcally, rotaton of a rgd body about a fxed axs s analogous to a lnear moton n one dmenson. Although the physcal quanttes nvolved n rotaton are qute

More information

Mean Molecular Weight

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

More information

To Fill or not to Fill: The Gas Station Problem

To Fill or not to Fill: The Gas Station Problem To Fll or not to Fll: The Gas Staton Problem Samr Khuller Azarakhsh Malekan Julán Mestre Abstract In ths paper we study several routng problems that generalze shortest paths and the Travelng Salesman Problem.

More information

THE HIT PROBLEM FOR THE DICKSON ALGEBRA

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

More information

AN EFFECTIVE MATRIX GEOMETRIC MEAN SATISFYING THE ANDO LI MATHIAS PROPERTIES

AN EFFECTIVE MATRIX GEOMETRIC MEAN SATISFYING THE ANDO LI MATHIAS PROPERTIES MATHEMATICS OF COMPUTATION Volume, Number, Pages S 5-578(XX)- AN EFFECTIVE MATRIX GEOMETRIC MEAN SATISFYING THE ANDO LI MATHIAS PROPERTIES DARIO A. BINI, BEATRICE MEINI AND FEDERICO POLONI Abstract. We

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Section 5.4 Annuities, Present Value, and Amortization

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

More information

HÜCKEL MOLECULAR ORBITAL THEORY

HÜCKEL MOLECULAR ORBITAL THEORY 1 HÜCKEL MOLECULAR ORBITAL THEORY In general, the vast maorty polyatomc molecules can be thought of as consstng of a collecton of two electron bonds between pars of atoms. So the qualtatve pcture of σ

More information

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

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

More information

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

More information

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

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

More information

Existence of an infinite particle limit of stochastic ranking process

Existence of an infinite particle limit of stochastic ranking process Exstence of an nfnte partcle lmt of stochastc rankng process Kumko Hattor Tetsuya Hattor February 8, 23 arxv:84.32v2 [math.pr] 25 Feb 29 ABSTRAT We study a stochastc partcle system whch models the tme

More information

Bandwdth Packng E. G. Coman, Jr. and A. L. Stolyar Bell Labs, Lucent Technologes Murray Hll, NJ 07974 fegc,stolyarg@research.bell-labs.com Abstract We model a server that allocates varyng amounts of bandwdth

More information

1. Measuring association using correlation and regression

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

More information

Implementation of Deutsch's Algorithm Using Mathcad

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

More information

Calculating the high frequency transmission line parameters of power cables

Calculating the high frequency transmission line parameters of power cables < ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,

More information

ON COMPLETELY CONTINUOUS INTEGRATION OPERATORS OF A VECTOR MEASURE. 1. Introduction

ON COMPLETELY CONTINUOUS INTEGRATION OPERATORS OF A VECTOR MEASURE. 1. Introduction ON COMPLETELY CONTINUOUS INTEGRATION OPERATORS OF A VECTOR MEASURE J.M. CALABUIG, J. RODRÍGUEZ, AND E.A. SÁNCHEZ-PÉREZ Abstract. Let m be a vector measure taking values in a Banach space X. We prove that

More information

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure

More information

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008 Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn

More information

LECTURE 1: MOTIVATION

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

More information

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL

More information

Implied (risk neutral) probabilities, betting odds and prediction markets

Implied (risk neutral) probabilities, betting odds and prediction markets Impled (rsk neutral) probabltes, bettng odds and predcton markets Fabrzo Caccafesta (Unversty of Rome "Tor Vergata") ABSTRACT - We show that the well known euvalence between the "fundamental theorem of

More information

On Leonid Gurvits s proof for permanents

On Leonid Gurvits s proof for permanents On Leond Gurvts s proof for permanents Monque Laurent and Alexander Schrver Abstract We gve a concse exposton of the elegant proof gven recently by Leond Gurvts for several lower bounds on permanents.

More information

Downlink Power Allocation for Multi-class. Wireless Systems

Downlink Power Allocation for Multi-class. Wireless Systems Downlnk Power Allocaton for Mult-class 1 Wreless Systems Jang-Won Lee, Rav R. Mazumdar, and Ness B. Shroff School of Electrcal and Computer Engneerng Purdue Unversty West Lafayette, IN 47907, USA {lee46,

More information