COLLOQUIUM MATHEMATICUM

Size: px
Start display at page:

Download "COLLOQUIUM MATHEMATICUM"

Transcription

1 COLLOQUIUM MATHEMATICUM VOL 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 to Hardy and Sobolev spaces. As an applcaton we obtan the de Leeuw restrcton theorems for multplers.. Introducton. In 977, R. Cofman and G. Wess (see [CW]) proved the transference theorem n the settng of L p spaces for p. As a frst applcaton of ths result, they were able to show the classcal theorem of K. de Leeuw [D] on restrcton of multplers; namely, f m s a nce functon such that m M p (R N ), then ts restrcton (m(n)) n s n M p (Z N ), wth norm bounded by m Mp (R ), where for a general locally compact group G, we N say that m M p (G) f ts nverse Fourer transform K = m s a convoluton p operator on L (Ĝ), wth Ĝ the dual group of G. In ths case, the norm of ths convoluton operator s denoted by ether N p (K) or m Mp (G). Ths theory has been wdely extended by N. Asmar, E. Berkson and T. A. Gllespe n a collecton of papers (see [ABG] and [ABG]) where they carefully study transference for maxmal operators and transference of weak type nequaltes. On the other hand, L. Colzan (see [C]) proved, usng drect arguments, that f m s a multpler on H p (R N ) and m s a contnuous functon, then (m(n)) n s a multpler on H p (T N ), n the sense that the operator M (SP)(x) = m(n)a n e πnx n= M (wth P the trgonometrc polynomal P(x) = M n= M a ne πnx ) can be extended to a bounded operator on H p (T N ). We shall see that ths s a consequence of the fact that the transference method of Cofman and Wess can be appled to a more general class of spaces than L p, ncludng Hardy spaces and Sobolev spaces. 99 Mathematcs Subect Classfcaton: 4B30, 43A5. Ths work has been partally supported by the DGICYT: PB [47]

2 48 M. J. CARRO AND J. SORIA Ths paper s organzed as follows: In Secton, we gve the defnton of transferred space and gve several examples. Secton 3 contans the man result of ths paper for the case p and several applcatons. Secton 4 s devoted to the case 0 < p < and Secton 5 to the case of maxmal operators and maxmal spaces. Although the theory can be developed for amenable groups ([CW]), we shall restrct our attenton to locally compact abelan groups where our theory can go a lttle further and where all of our examples belong. As usual, f(u) = f(u ), (τ v f)(u) = f(uv ), and constants such as C may change from one occurrence to the next.. Transferred space. Let G be a locally compact abelan group and let L 0 (G) denote the set of all measurable functons on G. Consder a sublnear functonal S : A C, where A L 0 (G). Then, for 0 < p, we defne the space H p (S) as the completon of {f L (G) : S(τ. f) L p (G)} wth respect to the quas-norm f H p (S) = S(τ. f) L p (G). Consder now a σ-fnte measure space (M,dx) and let R be a representaton of G on L p (M) such that R s unformly bounded (see [CW]); that s, there exsts a constant A such that, for every f L p (M) and every u G, () R u f Lp (M) A f Lp (M). Defnton.. We defne the transferred space H p (S;R) of H p (S) by the representaton R as the completon of {f L (M) : S( R u f( )) L p (M)} wth respect to the quas-norm f Hp (S;R) = S( R u f( )) Lp (M). Before gong any further, we gve some nterestng examples of transferred spaces. Recall that the transferred operator T K s defned by (see [CW]) (T K f)(x) = G K(u)(R u f)(x)du. Examples.. () If S(f) = f(e), where e s the dentty element, then H p (S) = L p (G), and f R s any representaton of G actng on L p (M), then one can easly check that the transferred space s equal to L p (M). () Consder G = R, M = T, (R u f)(x) = f(x u) and S(f) = f(0) + (Hf)(0) where H s the Hlbert transform. Then H (S) = {f L (R) : Hf L (R)} = H (R),

3 TRANSFERENCE THEORY 49 f(x u) du N u /N u N π cot(πs)f(x s)ds = f(x) + (Cf)(x), N /N u and, followng the computatons n [CW], we fnd that S( R u f(x)) = f(x) + lm = f(x) + lm where Cf s the conugate functon of f. Therefore, H (S;R) = {f L (T) : Cf L (T)} = H (T). Smlarly, usng Myach s theorem (see [M]), we conclude that, for 0 < p, H p (S) = H p (R), and H p (S;R) = H p (T). (3) Consder G = R, M = T, (R u f)(x) = f(x u) and S(f) = f(0) + f (0). Then and H p (S) = {f L p (R) : f L p (R)} = W p, (R), H p (S;R) = {f L p (T) : f L p (T)} = W p, (T). That s, we get Sobolev spaces. Obvously, we can also obtan W p,k (R N ) and W p,k (T N ). (4) Consder G = Z, (R n f)(x) = f(t n x) wth T an ergodc transformaton and S((a n ) n ) = a 0 + n 0 a n/n. Then H (S) = H (Z) and H (S;R) turns out to be an ergodc Hardy space (see [CW] and [CT]) H (S;R) = { f L (M) : n n f(t n x) L (M) (5) If G = R, (R t f)(x) = w(t t x) w(x) f(t t x) wth T an ergodc transformaton on a measure space M and w a weght on M, then for S(f) = f(0) + (Hf)(0), the transferred space H (S;R) s the space of all functons F L (w) such that wf s n the ergodc Hardy space H ; ths space can be consdered as a weghted ergodc Hardy space. (6) Consder G = R N, M = T N, (R u f)(x) = f(x u) and Sf = sup t>0 ϕ t f(0), where ϕ S(R N ) andìϕ =. Then H p (S) = H p (R N ) and H p (S;R) = H p (T N ). (7) Let now G = R, M = R, the Bohr compactfcaton of R (see [HR]), and (R t f)(x) = f(x t). Then one can easly see that the transferred space of the Hardy space H (R) s the space of all functons n L (R) such that t R sgn(t) f(t)e tx s n L (R), whch s H (R). (8) Let G = R n, M = R m wth m < n and let R be the natural representaton defned by (R (x,...,x n )f)(y,...,y m ) = f(y x,...,y m x m ). }.

4 50 M. J. CARRO AND J. SORIA If T R n s the transferred operator of the Resz transform R n ( =,...,n) n R n, then T R n = 0 f = m +,...,n and T R n = R m f =,...,m. Therefore, the transferred space of H p (R n ) by ths representaton s H p (R m ) for every 0 < p. Many other examples can be gven n the settng of Trebel Lzorkn spaces, Besov spaces, etc. 3. Man results for p. Throughout ths secton we shall denote by K the convoluton operator wth kernel K, T K the transferred operator, H p (S) wll be denoted by H p (K) and the transferred space H p (S;R) by H p (T K ), whenever Sf = K f. Case of a fnte famly of kernels and p. Denote by H p ({K } =,...,n ) the completon of {f L (G) : K f L p (G), =,...,n} under the norm K f p, and smlarly for H p ({T K } =,...,n ). Theorem 3.. Let G be a locally compact abelan group and let p. Let K, {K } =,...,n and {K } =,...,m be a collecton of functons n L (G) and assume that K : H p ({K } =,...,n) H p ({K } =,...,m) has the property that there exst postve constants {A } such that m n K K f p A K f p. = Then the transferred operator s bounded, wth = T K : H p ({T K } ) H p ({T K } ) m n T K T K f p BA A T K f p, = = where A s as n () and B depends only on n and m. P r o o f. We prove ths for m =. The proof for m > s smlar. We frst recall that snce K L (G), t s known (see [CW]) that, for every v G and every f L p (M), () (R v T K f)(x) = G K(u)(R vu f)(x)du = (T K R v f)(x), a.e. x M. Also, usng the same dea, one can easly see that T K T K = T K K and therefore, we can assume wthout loss of generalty that H p (K ) = L p (G) and H p (T K ) = L p (M).

5 TRANSFERENCE THEORY 5 Now, snce K and K = K are n L (G) we can approxmate them by functons n L (G) wth compact support and hence standard arguments show that for every ε > 0 we can fnd functons K n, K,n n L (G) wth compact support such that n K n f p A K,n f p + ε f p. = Therefore, we can assume wthout loss of generalty that K and K are compactly supported functons n L (G). Let f L p (M). By (), we have T K f p = R v R v T K f p A R v T K f p. Now, as n [CW], we consder a compact set C such that the dentty element e s n C, suppk C and suppk C for every =,...,n. Also, take a neghborhood V of e such that (3) Now, by (), T K f p p µ(v C C) µ(v ) Ap µ(v ) V = Ap µ(v ) V M = Ap µ(v ) V M Ap µ(v ) M [ G Ap B µ(v ) M + R v T K f p p dv ε max(,(a f p K ) p ). GK(u)(R vu f)(x)du p dxdv GK(u)χ V C (vu )(R vu f)(x)du GK(u)χ V C (vu )(R vu f)(x)du A p χ V C (R. f)(x) p H p (K ) dx, p dxdv p ] where the last nequalty follows by applyng the hypothess to the functon h x (u) = χ V C (u)(r u f)(x). The last step s to show that, for every, (4) χ V C (R. f)(x) M p H p (K ) dx Ap µ(v ) f p H p (T K ) + εµ(v ), from whch we can easly deduce the theorem. To see ths, we observe that χ V C (R. f)(x) Hp (K ) = K h x Lp (G).

6 5 M. J. CARRO AND J. SORIA K h x Now, M p L p (G) dx K (u)χ V C (vu = M [ G G )(R vu f)(x)du p ] K (u)χ V C (vu = M [ V G )(R vu f)(x)du p ] [ K (u)χ V C (vu G )(R vu f)(x)du p ] + M = I + II, V C C\V where the last equalty follows snce V V C C. Let us frst estmate I: snce u C and v V, we have vu V C and therefore I K (u)(r vu f)(x)du = M [ V p ] G K (u)(r u R v f)(x)du = V [ M p ] dx dv G T K R v f V p p dv R v T K f = V p p dv A p µ(v ) T K f p p. To estmate II, we proceed as follows: [ GK (u)χ V C (vu )(R vu f)(x)du p ] II = M V C V \V K p M V C V \V G K p K (u) G [ V C V \V A p f p p K p µ(v C V \ V ). ] K (u) (R vu f)(x) p du R vu f p p dv du Therefore, χ V C (R. f)(x) M p H p (K ) dx A p µ(v ) T K f p p + Ap f p p K p µ(v C C \ V ). Now, snce, for every, µ(v C C \ V ) = µ(v C C) µ(v ) εµ(v ) (A f p K ) p,

7 TRANSFERENCE THEORY 53 we χ V C (R. f)(x) obtan M p H p (K ) dx Ap µ(v ) f p H p (T K ) + εµ(v ), as desred. Remark 3.. We observe that, as t happens n the transference theorem of [CW], the above theorem s not only a boundedness result, but the mportant thng s the norm of the transferred operator. Applcatons. We now apply the prevous results to the settng of Sobolev and Hardy spaces. A. Sobolev spaces. Let K be a functon n L (G) such that K : H p (K ) L p (G), wth norm N p (K), where K s not, n general, n L. Assume that there exsts an approxmaton of the dentty ϕ n such that ϕ n L (G) and K ϕ n s a functon n L (G). Then, f we apply the boundedness hypothess to the functon f ϕ n we get (K ϕ n ) f p N p (K) (K ϕ n ) f p, where the kernels K ϕ n and K ϕ n are functons n L (G) and hence we can transfer to deduce that T K ϕn : H p (T K ϕ n ) L p (M) s bounded wth norm less than or equal to A N p (K). The boundedness of T K from H p (T K ) nto L p (M) can be deduced, n the case of Sobolev spaces, by a lmt process, snce T K ϕ n f converges to T K f n the L p (M) norm, for every p. Theorem A.. Let p and r,s N. If m L loc s a normalzed functon such that K = m has the property that K : W p,r (R N ) W p,s (R N ) s a bounded operator wth norm N p (K), then the transferred operator T K : W p,r (T N ) W p,s (T N ) s gven by T K ( a n e πnx ) = n m(n)a ne πnx and s a bounded operator wth norm less than or equal to CN p (K), wth C only dependng on s and r. P r o o f. We prove ths n the case s = 0. The case s N s smlar. Take ϕ n (ξ) = n N ϕ(nξ) where ϕ D(R N ) s such thatìϕ = and ϕ 0. In ths case K = δ () 0 for r, and hence, K ϕ n = ϕ () n L (R N ). Therefore we get the result n the case K L (R N ). Now, for the general case we proceed as n Lemma 3.5 of [CW]. Snce m s normalzed and m L loc, we see that m n = (K ϕ n ) s also normalzed,

8 54 M. J. CARRO AND J. SORIA m n L and we can fnd a sequence (m k n) k such that m k n(ξ) m n (ξ) for every ξ R N and, f Kn k = m k n, then Kn k L, and N p (Kn) k N p (K). Also, m n (ξ) m(ξ) for every ξ R N. From ths, we deduce that T K = lm n,k T K k n and snce Kn k satsfes the rght hypothess we obtan the desred result. Smlarly, n the context of the Bohr compactfcaton R N of R N, we get the followng result: Theorem A.. Let p and r,s N. If m L loc s a normalzed functon such that for K = m the operator K : W p,r (R N ) W p,s (R N ) s bounded wth norm N p (K), then the transferred operator T K : W p,r (R N ) W p,s (R N ) s gven by T K ( a t e πtx ) = t m(t)a te πtx and s bounded wth norm less than or equal to C r,s N p (K). Theorem A.3. Let p, r,s N. If m L loc s a normalzed functon such that for K = m the operator K : W p,r (R N ) W p,s (R N ) s bounded wth norm N p (K), and K s a convoluton kernel on R M wth M < N and K(x) = m(x,0) where x = (x,x ) R M R N M, then the operator K : W p,r (R M ) W p,s (R M ) s bounded wth norm less than or equal to C r,s N p (K). P r o o f. Observe that W p,r (R M ) s the transferred space of W p,r (R N ) under the representaton of Example. (8), and argue as n Theorem A.. B. Hardy spaces (p = ). Now assume that K s a functon n L (R N ) such that (5) K : H (R N ) H (R N ) s bounded wth norm N (K). The prevous argument cannot be appled to ths case because we cannot fnd an approxmaton of the dentty ϕ n such that Hϕ s n L andìϕ =. However, we obtan the followng result (see [C]). Theorem B.. If K s such that K = m s a normalzed functon and K : H (R N ) H (R N ) s bounded wth norm N (K), then the transferred operator ( ) T K an e πnx = a n m(n)e πnx

9 TRANSFERENCE THEORY 55 can be extended to a bounded operator from H (T N ) nto H (T N ) wth norm less than or equal to N (K). P r o o f. Frst assume that K L and N = (a smlar proof works for N > ). Let P be a trgonometrc polynomal of degree such that P(0) = 0. Let φ H (R) be such that φ(n) = for every 0 < n. Then both K φ and Hφ are functons n L (R) and therefore T K φ P H (T) N (K)( T φ P + T Hφ P ). Snce T K φ = T K T φ, T φ P = P and T Hφ = T H T φ, we obtan the desred result. Fnally, every convoluton kernel on H (R) s also a convoluton kernel on L (R) and therefore m L (R). Moreover, m N (K). Hence, f a(x) =, then (T K )a(x) = m(0) and thus T K a H (T) = m(0) m N (K). To consder the general case K L, we need the followng techncal lemma. Lemma. If K s a convoluton operator on H (R N ) wth norm N (K), then there exsts a sequence (K n ) n of compactly supported functons n L (R N ) such that m n (ξ) = K n (ξ) m(ξ) for every ξ R N and N (K n ) N (K). P r o o f. We prove ths for N =. The general case s smlar. Frst, we know that m s a contnuous functon on R\{0}. Let ϕ S(R) wth compact support and ϕ(ξ) = for every ξ [,]. Defne ϕ k (x) = ϕ(x/k) and ϕ k (x) = ϕ(kx). Set m k (x) = m(x)ϕ k (x)( ϕ k (x)). Then m k (x) m(x) as k for every x 0, and m k s a multpler on H (R) wth norm less than or equal to CN (K), wth C only dependng on ϕ. Choose Ψ S(R) wth compact support such that Ψ(0) = and set Ψ n (ξ) = Ψ(ξ/n). Let φ n (ξ) = e πξ Ψ n (ξ), and consder ( x m n,k (x) = s φ x ) n m k (s) ds ( ) x dt t φ n (t)m k s t t. Then ( ( ) ) x dt m n,k (x) m k (x) = t φ n (t) m k m k (x) t t wth δ to be chosen. = δ + +δ δ s = +, +δ

10 56 M. J. CARRO AND J. SORIA Now, usng the decay of φ n we obtan δ ( ( ) ) x dt t φ n (t) m k m k (x) t t C m δ n M and n tends to nfnty. Smlarly forì +δ t ( δ, + δ), m k (x/t) m k (x) ε and hence +δ ( ( ) x t φ n (t) m k m k ) dt (x) t t Cε δ Now, snce K n,k (x) = m n,k (x) = φ n (sx)m k(s)ds, t M dt, and the above expresson converges to zero whenever M s large enough. For the second term we use the contnuty of m k to deduce that gven ε there exsts δ such that for every φ n (t) dt = Cε. φ n has compact support and m k (s) = 0 n a neghborhood of zero and for s large enough, we nfer that K n,k has compact support and obvously s n L (R). Fnally, K n,k f Rm n,k (ξ) = R f(ξ)e πxξ dξ dx t φ n (t)m k (ξ/t) dt ] f(ξ)e πxξ dξ dx = R R = R R R [ R t φ n (t) m k (ξ/t) R [ R f(ξ)e ] πxξ dξ dt dx φ n Rm k (ξ/t) (t) R f(ξ)e πxξ dξdxdt φ n Rm k (y)t (t) R f(ty)e πxty dy dxdt and hence, K n,k satsfes the lemma. N (K k ) f H (R) CN (K) f H (R), The proof of Theorem B. now follows by standard approxmaton arguments. Smlarly, we get Theorem B.. If m s a normalzed functon such that for K = m the operator K : H (R N ) H (R N )

11 TRANSFERENCE THEORY 57 s bounded wth norm N (K), then the operator T K : H (R N ) H (R N ) defned by T K ( a t e πtx ) = t m(t)a te πtx s bounded wth norm less than or equal to N (K). Theorem B.3. If m s a normalzed functon such that for K = m the operator K : H (R N ) H (R N ) s bounded wth norm N (K), and K s a convoluton kernel on R M wth M < N and K(x) = m(x,0) where x = (x,x ) R M R N M, then the operator K : H (R M ) H (R M ) s bounded wth norm less than or equal to N (K). If we want to use the technques of Theorem B. to cover the case of Example.(4), that s, to transfer the boundedness of a convoluton operator from H (R) to an ergodc Hardy space H (M), we observe that, n general, t s not the case that, for every f n a dense set of H (M), there exsts ϕ H (R) such that T ϕ f = f wth T ϕ the transference operator of the convoluton operator ϕ. Therefore, we can only show that, f N (K) s the norm of the convoluton operator K n H (R), then, for every ϕ H (R), T K ϕ f H (M) N (K)( T ϕ f L (M) + T ϕ T H f L (M)), where T H s the transference operator of the Hlbert transform. From ths, we can deduce that f m = K has compact support away from zero, then T K f H (M) N (K) ϕ ( f L (M) + T H f L (M)), snce, n ths case, there exsts ϕ H (R) such that K ϕ = K. 4. Case of a fnte famly of kernels and 0 < p <. Now consder a σ-fnte measure space (M,dx) and let R be a representaton of G on L p (M) and on L (M) such that R s unformly bounded; that s, there exst constants A and B such that, for every f L p (M) and every u G, and, for every f L (M), R u f Lp (M) A f Lp (M), R u f L (M) B f L (M). Under ths last condton, the transferred operator T K s well defned n a dense subset of the transferred space. We observe that n ths case the boundedness of T K s not trval even n the case of K L wth compact support snce the Mnkowsk ntegral nequalty does not hold.

12 58 M. J. CARRO AND J. SORIA Ths secton s organzed as follows: frst we prove the transference theorem f one of the followng condtons holds: (a) G s compact. (b) G s dscrete. (c) M s of fnte measure. Then, f G and M are ether R N, Z m or T k, we can transfer as n the followng dagram: Z m R N T k Z m and hence t remans to transfer from R N to Z m, or more generally from R N to any measure space M. The next step wll be to show that under some condtons on the representaton we can transfer from R N to any measure space M ether va the factorzaton R N T k M and/or usng the dlaton structure of the group R N. Theorem 4.. Let G be ether a compact or a dscrete abelan group, or let M be of fnte measure, and let 0 < p <. Let K, {K } =,...,n and {K } =,...,m be a collecton of functons n L (G) wth compact support and assume that K : H p ({K } =,...,n ) H p ({K } =,...,m ) has the property that there exst postve constants {A } such that m n K K f p A K f p. = Then the transferred operator s bounded, wth = T K : H p ({T K } ) H p ({T K } ) m n T K T K f p DA A T K f p, = = where A s as n () and D depends only on n and m. P r o o f. As n Theorem 3., we prove ths for m =. (a) Assume frst that G s compact. Then we proceed as n Theorem 3. but, n ths case, we can take V = G and then the term II s zero.

13 TRANSFERENCE THEORY 59 (b) If G s dscrete, and we argue as n Theorem 3., t remans to show that II/µ(V ) can be made small enough. Now, snce p <, [ II K (u)χ V C (vu = M V C V \V G )(R vu f)(x)du p ] [ ] K (u) M p (R vu f)(x) p dudx dv V C C\V G K (u) G p R vu f p p dv du V C C\V A p f p p K p µ(v C C \ V ), µ(c)/(/p) and hence we can choose V n such a way that II/µ(V ) s arbtrarly small. (c) If M s of fnte measure, and we assume that R acts on L (M), then II (m(m)µ(v C C \ V )) /(/p) [ M[ ] ] p K (u)χ V C (vu )(R vu f)(x) du V C C\V G ( m(m)µ(v C C \ V ) ) /(/p) µ(v C V \ V ) p K p R vu f p m(m)µ(v C C \ V ) K p Bp f p, and ths expresson converges to zero on choosng V approprately. Transference from R N to M. Let us now consder the case of transference from R N to a general measure space M. Let R be a representaton from R N nto L p (M). Assume that one of the followng two condtons hold: () For every f n a dense subset of H p ({T K } ), there exsts M > 0 such that R M f = f. Then, f we defne (Rθ Mf)(x) = (R Mθf)(x) for θ [ /,/] N = T N, we fnd that R M s a unformly bounded representaton of T N n L p (M). If (R M f)(x) = f(s M x), then M may also depend on f and x. () For every f n a dense subset of H p ({T K } ), there exst C > 0 and M 0 > 0 such that, for every M M 0, M ( M N (R u f)(x) du ( M,M) N ) p dx C. In the frst case, consder a kernel K L (R N ) and set K M (x) = M N K(x/M). Let K M (θ) = m Z N K M (θ + m)

14 = 60 M. J. CARRO AND J. SORIA be the perodc extenson. Then for the transferred operator we have (T K RM f)(x) K M M (θ)(r θ M f)(x)dθ T N M N K(Mθ + Mm)(R Mθ f)(x)dθ T N m = K(u + Mm)(R u f)(x)du [ M/,M/] N m = K(u)(R u f)(x)du [ M/,M/] N + K(u + Mm)(R u f)(x)du [ M/,M/] N m 0 = I M + II M. Now, snce the representaton R acts on L (M) we see that, by the Mnkowsk ntegral nequalty, II M A f K(u + Mm) du [ M/,M/] N m 0 = A f K(u) du, u M/ and therefore II M converges to zero as M tends to nfnty. Therefore, there exsts a subsequence M k such that II Mk converges to zero almost everywhere. Snce I M converges to the transferred operator TK R, we get (T R Kf)(x) = lm k (T RM k K Mk f)(x). From ths, we can deduce the followng result, Theorem 4.. Let G = R N and let M be a σ-fnte measure space. Let 0 < p <. Let K, {K } =,...,n and {K } =,...,m be a collecton of functons n L (G) wth compact support and assume that K : H p ({K } =,...,n ) H p ({K } =,...,m ) has the property that there exst postve constants {A } such that m n K K f p A K f p. = If the representaton R satsfes condton () then the transferred operator = T K : H p ({T K } ) H p ({T K } )

15 TRANSFERENCE THEORY 6 s bounded, wth m T K T K f p CA = n A T K f p, = where A s as n () and C depends only on n and m. P r o o f. As always, take m = and H p ({K } =,...,m) = L p. Usng the dlaton structure of R N we see that, for every M > 0, n K M f p A (K ) M f p. = Snce T N s a measure space of fnte measure, we can apply Theorem 4. to deduce that we can transfer the boundedness of K M to L p (T N ) va the natural representaton (S u f)(θ) = f(θ u). Hence, for every M, TK S M s a bounded operator wth TK S M f p CA A T (K ) M f p. But, snce K and K have compact support, for M large enough we have K M (x) = K M (x) for every x T N and smlarly for the kernels K. Now, snce T S KM = K M, we see that f we take M large enough such that ths condton holds and also that R M f = f, we get T K f p p = K(y)(R y f)( )dy p K M (y)(r p= yf)( )dy M p p R N T N = KM (y)(r y M f)( )dy p A p (K ) M(y)(R y M f)( )dy p p p T N T N = A p K (y)(r yf)( )dy p p. R N Theorem 4.3. Under the hypothess of Theorem 4., f the representaton R satsfes condton (), then the transferred operator s bounded, wth T K : H p ({T K } ) H p ({T K } ) m n T K T K f p CA A T K f p, = = where A s as n () and C depends only on n and m. P r o o f. We follow the same steps as for Theorem 3.. Let f L p (M). Take M large enough such that the supports of the functons K M and (K ) M = (K ) M are contaned n ( ε,ε) N for ε > 0 and

16 6 M. J. CARRO AND J. SORIA () holds. Then, f V = (,) N, we get T K f p p = K(y)(R y f)( )dy p = p R N Ap K M (y)(rv y M µ(v f)(x)dy p dxdv ) V M ( ε,ε) N = Ap µ(v ) V M Ap [ ( ε,ε) N K M (y)(r M yf)( )dy K M (y)χ ( ε,+ε) N(v y)(r M v yf)(x)dy p dxdv ( ε,ε) N µ(v ) M (,) N [ [ = M R N [ M (,) N + M V ε [ = I + II. Ap C µ(v ) M K M (y)χ ( ε,+ε) N(v y)(rv y M f)(x)dy p ] ( ε,ε) N A p χ ( ε,+ε) N(R Ṃ f)(x) p H p ((K ) M ) dx. Now, f we take h x (y) = χ ( ε,+ε) N(y)(Ry Mf)(x) and V ε = ( ε, + ε) N \ (,) N, we get (K ) M h x M p L p (R N ) dx ] (K ) M (y)χ ( ε,+ε) N(v y)(r M v yf)(x)dy p dv ( ε,ε) N (K ) M (y)(rv y M f)(x)dy p ] ( ε,ε) N (K ) M (y) (R M v yf)(x) dy p ] ( ε,ε) N To estmate I we proceed as n Theorem 3., and for the second term, II V ε p M (K V ε ] p ) M (y) (Rv y M f)(x) dy ( ε,ε) ( N ) p Cε N K p (Ry M M f)(x) dy dx (,) ( N ) p = Cε N K p M M N (R y f)(x) dy dx C(f) K p ( M,M) N Lettng ε tend to zero, we are done. p p dx εn.

17 TRANSFERENCE THEORY 63 For the examples, t wll be very convenent to get rd of the hypothess of K beng wth compact support. Because of the lack of the Mnkowsk ntegral nequalty, we cannot argue as n the case p. However, we are gong to show that whenever M s of fnte measure we do not need that condton on K. Then we shall prove that under certan condtons on the representaton we can restrct ourselves to ths case. Assume then that M s of fnte measure. Then, f K n s a sequence of functons n L (G) such that K n has compact support and K n converges to K n the L norm, we have Now, T K f p p T K Kn f p p + T Kn f p p D T K Kn f p + T K n f p p Dε f + T Kn f p p. T Kn f p p D µ(v ) V M K n (u)χ V C (vu G )(R vu f)(x)du p dxdv D G(K n K)(u)χ µ(v ) V C (vu [ M V )(R vu f)(x)du p K(u)χ V C (vu G )(R vu f)(x)du p ] + M V D ( M µ(v ) µ(v ) p K n K p (R u f)(x)du dx V C + D µ(v ) A p M G K (u)χ V C (vu G )(R vu f)(x)du p. Followng the deas n Theorem 3. and usng the fact that, by densty, we can consder f L (M), we get the result by lettng ε tend to zero. Defnton 4.4. We say that R acts locally on L p (M) f the followng condton holds: Gven a compact set C, and gven ε > 0, there exsts V such that µ(v C ) ( + ε)µ(v ) and, for every fnte famly {K } of kernels n L, there exsts a postve constant B such that, gven any measurable set M n M of fnte measure and gven any u G, there exsts a measurable set M u such that R u f Lp (M) B f Lp (M u ) for every f n a dense subset of H p (T K ) and, for every neghborhood V of the dentty there exsts another measurable set M V such that M v M V for every v V and M V p µ(v C C \ V ) µ(v ) ε. In ths case, we can reduce ourselves to the case of M of fnte measure ) p

18 64 M. J. CARRO AND J. SORIA and therefore we do not need the hypothess of K beng of compact support. To see ths we ust have to start computng T K f Lp (M) for any M of fnte measure. Then T K f p L p (M) Ap R v T K f p µ(v L ) V p (M v ) dv A p R v T K f p µ(v L ) V p (M V ) dv, and the rest of the proof follows as usual. One can easly check that f R s the representaton of Example.(8), then R acts locally on L p (R m ), and therefore, we can transfer from R N to R m (m < N) wth 0 < p <. C. Hardy spaces (p < ). Let K be such that m = K s a normalzed functon wth m(0) = 0. Assume that K f p C Hf p, for some p <. As n Theorem B., let P be a trgonometrc polynomal of degree such that P(0) = 0. Let φ H (R N ) be such that φ(n) = for every 0 < n. Take φ n convergng to φ n the L norm and such that Hφ n has compact support for every n. Then K φ n and Hφ n are functons n L (R N ) and the latter has compact support. Therefore T K φn P Hp (T N ) C T Hφn P p. But, snce m s normalzed, we have T K φn = T K T φn. Takng the lmt as n and usng the fact that T φ P = P, we get the followng result: Proposton C.. Let K be such that m = K s a normalzed functon wth m(0) = 0. If K f p C Hf p, then m(n)an e πnx C sgn(n)an e Lp(T) πnx. Lp(T) 5. Maxmal operators and maxmal spaces. In ths secton, we consder the case where the operator S s determned by an nfnte collecton of kernels K n L (G) wth compact support; namely Sf = sup K f(0). In ths case, we wrte H p (S) = H p ({K } ). Hence, f we have two collectons of functons satsfyng the above condtons, {K } and {K }, and K L (G) has the property that the convoluton operator K : H p ({K } ) H p ({K } )

19 TRANSFERENCE THEORY 65 s bounded wth norm less than or equal to N p (K), then the maxmal operator sup K : H p ({K } ) L p (G) s bounded wth norm less than or equal to N p (K). Therefore, we can reduce ourselves to the case of a maxmal operator actng on a maxmal space. Obvously ths maxmal space can be L p (G) and then our case wll nclude the maxmal transference of [ABG]. For that reason, throughout ths secton we consder only representatons such that () R u s separaton-preservng for every u G, () there exsts B such that R u f Hp ({T K } ) B f Hp ({T K } ) for every u G and every f L p (M), and () f 0 < p <, then the representaton R also acts nto L (M). Theorem 5.. Let G be a compact abelan group and let 0 < p <. Let K and K be two collectons of functons n L (G) and let N p (K) be the norm of the convoluton operator sup K : H p ({K } ) L p (G). If R s a representaton from G nto L p (M) satsfyng () (), then the transferred operator sup T K : H p ({T K } ) L p (M) s bounded, wth norm less than or equal to ABN p (K), where A s as n () and B as n (). P r o o f. Snce R s separaton-preservng, we get (see [ABG]) and therefore sup R v (sup (T K f)(x) ) sup (T K R v f)(x), T K f p p A p G sup T K R v f p p dv = A p G M sup K G (u)(r vu f)(x)du p dxdv A p M [ G sup G K (u)(r vu f)(x)du p dv ]dx (AN p (K)) p M (R. f)(x) p H p ({K } ) dx,

20 66 M. J. CARRO AND J. SORIA where the last nequalty follows by applyng the hypothess to the functon h x (u) = (R u f)(x). The last step s to show that (R. f)(x) M p H p ({K } ) dx Bp f p H p ({T K } ). Now, (R. f)(x) M p H p ({K } dx sup K ) = M [ G G (u)(r vu f)(x)du p ] sup K G (u)(r u R v f)(x)du p ] dx dv G [ M = G R v f p H p ({T K } ) dv Bp f p H p ({T K } ), where the last nequalty follows by (). If the group G s not compact, the proof s not so clear. Moreover, the natural extenson of Theorem 3. does not work n general snce condton (4) fals. However, we can formulate a qute general result that wll be useful for our purpose. Theorem 5.. Let G be a locally compact abelan group and let 0<p<. Let K and K be two collectons of compactly supported functons n L (G) and let N p (K) be the norm of the convoluton operator sup K : Hp ({K } ) L p (G). Let R be a representaton from G nto L p (M) satsfyng () (). Let f H p ({T K } ) satsfy the followng condton: there exsts B > 0 so that, for every compact E G large enough, there exsts ϕ E such that ϕ E (u) = for every u E, and (6) M ϕ E (R. f)(x) p H p ({K } ) dx Bp µ(e) f p H p ({T K } ). Then wth B as n (6) and A as n (). sup T K f p BA f H p ({T K } ), P r o o f. Frst, by Fatou s lemma, t s enough to estmate the norm sup =,...,N T K f p. Consder C such that suppk C for every =,...,N. Then we can adapt the proof of Theorem 3. qute easly to get

21 TRANSFERENCE THEORY 67 sup =,...,N Ap µ(v ) V T K f p sup =,...,N = Ap sup µ(v =,...,N ) V M = Ap sup µ(v =,...,N ) V M Ap µ(v ) M [ G sup T K R v f p H p ({T K } ) dv G K (u)(r vu f)(x)du p dxdv K G (u)ϕ V C (vu )(R vu f)(x)du p dxdv K G (u)ϕ V C (vu )(R vu f)(x)du p ] (AN p(k)) p µ(v ) M ϕ V C (R. f)(x) p H p ({K } ) dx, where the last nequalty follows by applyng the hypothess to the functon h x (u) = ϕ V C (u)(r u f)(x). The last step s to show that ϕ V C (R. f)(x) M p H p ({K }) dx Bp A p µ(v )( + ε) f p H p ({T K } ), but ths follows by (6) and the choce of V such that µ(v C )/µ(v ) + ε. As n Sectons and 4, f p or f M s of fnte measure (or t can be reduced to ths case) and p <, we do not need the hypothess on the support of K but we do need t for the support of K (see also [ABG]). D. Maxmal spaces and maxmal operators. We start wth the result of [C] we mentoned n the ntroducton. Theorem D.. Let 0 < p <. If K s such that K = m s a normalzed functon and the operator K : H p (R N ) H p (R N ) s bounded wth norm N p (K), then the operator T K ( an e πx) = a n m(n)e πx wth m = K can be extended to a bounded operator from H p (T N ) nto H p (T N ) wth norm less than or equal to N p (K). P r o o f. As for the case p =, frst assume that K L.

22 68 M. J. CARRO AND J. SORIA Snce K s a convoluton kernel n L, we see by nterpolaton that K s also a convoluton kernel n H. Now, snce we are transferrng to a measure space of fnte measure, we do not need any condton on the support of K but, f we want to apply Theorem 4., we need to have that restrcton on the kernels that defne the space H p (R N ). Snce these kernels do not have compact supports, we are forced to use Theorem 5.. Take a to be an atom n H p (T N ). Then ether a = or a s a (p,q)-atom. For the frst case, we proceed as n Theorem B., snce T K a Hp (T N ) = m(0) m N p (K), and for a general atom we observe that f T N = (,) N, then the functon χ ( M,M) Na s an atom n H p (R N ) and χ ( M,M) Na Hp (R N ) CM N a Hp (T N ) and therefore condton (6) holds. Hence T K a Hp (T N ) N p (K) a Hp (T N ). For the general case of a normalzed multpler we argue as n Theorem B.. Take a trgonometrc polynomal P n H p (T N ) wth P(0) = 0 and degree and let φ be as n B.. Then K φ s n L and snce T φ P = P and P satsfes condton (6), we can apply Theorem 5. to obtan the result. Smlarly, we can obtan the analogue to Theorem B.3 for 0 < p < observng that f a H p (R) s a (p, )-atom and for every compact E R we defne ϕ E (u,u ) = whenever (u,u ) E and ϕ E (u,u ) = 0 f (u,u ) Ẽ, where Ẽ = E + (0,n) s such that E Ẽ =, then ϕ E (u,u )a(x u ) s a (p, )-atom of H p (R ) satsfyng condton (6). A fnal applcaton we want to menton s the followng: Assume that we have two equvalent norms n a fxed space H p. Then we can try to transfer the dentty operator to obtan two equvalent norms n the transferred space. We llustrate ths stuaton wth the followng example. Let ϕ L wth compact support and consder the atomc and maxmal versons of the space H p (R N ) whch of course are equvalent. Then, f H p (R N ) denotes the atomc space, the operator sup ϕ t : H p (R N ) L p (R N ) t s bounded. Now, snce the atomc verson of H p (T N ) satsfes condton (6) we can transfer ths maxmal operator to obtan sup t ϕ t F Lp (T N ) C F Hp (T N ), whch s a well-known result.

23 TRANSFERENCE THEORY 69 REFERENCES [ABG] N.Asmar,E.BerksonandT.A.Gllespe,Transferenceofstrongtype maxmal nequaltes by separaton-preservng representatons, Amer. J. Math. 3(99), [ABG],,, Transference of weak type maxmal nequaltes by dstrbutonally bounded representatons, Quart. J. Math. Oxford 43(99), [CT] R.CaballeroandA.dela Torre,AnatomctheoryforergodcH p spaces, Studa Math. 8(985), [CW] R.CofmanandG.Wess,TransferenceMethodsnAnalyss,CBMSRegonalConf.Ser.nMath.3,Amer.Math.Soc.,977. [CW],,MaxmalfunctonsandH p spacesdefnedbyergodctransformatons, Proc. Nat. Acad. Sc. U.S.A. 70(973), [C] L.Colzan,FourertransformofdstrbutonsnHardyspaces,Boll.Un.Mat. Ital.A(6)(98), [D] K.de Leeuw,OnL pmultplers,ann.ofmath.8(965), [HR] E.HewttandK.A.Ross,AbstractHarmoncAnalyss,Vol.I,Sprnger,963. [M] A.Myach,OnsomeFourermultplersforH p (R n ),J.Fac.Sc.Unv.Tokyo 7(980), Departament de Matemàtca Aplcada Anàls Unverstat de Barcelona 0807 Barcelona, Span E-mal: carro@cerber.mat.ub.es sora@cerber.mat.ub.es Receved 9 Aprl 996; revsed7august996and7november996

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

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

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

More information

Recurrence. 1 Definitions and main statements

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

More information

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

Extending Probabilistic Dynamic Epistemic Logic

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

More information

BERNSTEIN POLYNOMIALS

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

More information

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

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

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

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

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

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

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

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

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

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

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

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

Global stability of Cohen-Grossberg neural network with both time-varying and continuous distributed delays

Global stability of Cohen-Grossberg neural network with both time-varying and continuous distributed delays Global stablty of Cohen-Grossberg neural network wth both tme-varyng and contnuous dstrbuted delays José J. Olvera Departamento de Matemátca e Aplcações and CMAT, Escola de Cêncas, Unversdade do Mnho,

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

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

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

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

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.

Lecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression. Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook

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

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

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

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

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

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

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

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

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

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

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

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

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

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

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!

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

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

Pricing Overage and Underage Penalties for Inventory with Continuous Replenishment and Compound Renewal Demand via Martingale Methods

Pricing Overage and Underage Penalties for Inventory with Continuous Replenishment and Compound Renewal Demand via Martingale Methods Prcng Overage and Underage Penaltes for Inventory wth Contnuous Replenshment and Compound Renewal emand va Martngale Methods RAF -Jun-3 - comments welcome, do not cte or dstrbute wthout permsson Junmn

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

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

Section 5.3 Annuities, Future Value, and Sinking Funds

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

More information

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

MATH 425, PRACTICE FINAL EXAM SOLUTIONS.

MATH 425, PRACTICE FINAL EXAM SOLUTIONS. MATH 45, PRACTICE FINAL EXAM SOLUTIONS. Exercise. a Is the operator L defined on smooth functions of x, y by L u := u xx + cosu linear? b Does the answer change if we replace the operator L by the operator

More information

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

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

More information

NPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6

NPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6 PAR TESTS If a WEIGHT varable s specfed, t s used to replcate a case as many tmes as ndcated by the weght value rounded to the nearest nteger. If the workspace requrements are exceeded and samplng has

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

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

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

An Overview of Financial Mathematics

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

More information

BANACH AND HILBERT SPACE REVIEW

BANACH AND HILBERT SPACE REVIEW BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but

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

) of the Cell class is created containing information about events associated with the cell. Events are added to the Cell instance

) of the Cell class is created containing information about events associated with the cell. Events are added to the Cell instance Calbraton Method Instances of the Cell class (one nstance for each FMS cell) contan ADC raw data and methods assocated wth each partcular FMS cell. The calbraton method ncludes event selecton (Class Cell

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

Solving Factored MDPs with Continuous and Discrete Variables

Solving Factored MDPs with Continuous and Discrete Variables Solvng Factored MPs wth Contnuous and screte Varables Carlos Guestrn Berkeley Research Center Intel Corporaton Mlos Hauskrecht epartment of Computer Scence Unversty of Pttsburgh Branslav Kveton Intellgent

More information

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits

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

More information

Lecture 2: Single Layer Perceptrons Kevin Swingler

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

More information

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

+ + + - - 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

Nordea G10 Alpha Carry Index

Nordea G10 Alpha Carry Index Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and

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

Trivial lump sum R5.0

Trivial lump sum R5.0 Optons form Once you have flled n ths form, please return t wth your orgnal brth certfcate to: Premer PO Box 2067 Croydon CR90 9ND. Fll n ths form usng BLOCK CAPITALS and black nk. Mark all answers wth

More information

Area distortion of quasiconformal mappings

Area distortion of quasiconformal mappings Area dstorton of quasconformal mappngs K. Astala 1 Introducton A homeomorphsm f : Ω Ω between planar domans Ω and Ω s called K-quasconformal f t s contaned n the Sobolev class W2,loc 1 (Ω) and ts drectonal

More information

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

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

More information

Energies of Network Nastsemble

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

More information

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

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

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

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

Let H and J be as in the above lemma. The result of the lemma shows that the integral

Let H and J be as in the above lemma. The result of the lemma shows that the integral Let and be as in the above lemma. The result of the lemma shows that the integral ( f(x, y)dy) dx is well defined; we denote it by f(x, y)dydx. By symmetry, also the integral ( f(x, y)dx) dy is well defined;

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

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

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

More information

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

Application of Quasi Monte Carlo methods and Global Sensitivity Analysis in finance

Application of Quasi Monte Carlo methods and Global Sensitivity Analysis in finance Applcaton of Quas Monte Carlo methods and Global Senstvty Analyss n fnance Serge Kucherenko, Nlay Shah Imperal College London, UK skucherenko@mperalacuk Daro Czraky Barclays Captal DaroCzraky@barclayscaptalcom

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

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

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

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES

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

More information

Faraday's Law of Induction

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

More information

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

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

Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering

Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering Out-of-Sample Extensons for LLE, Isomap, MDS, Egenmaps, and Spectral Clusterng Yoshua Bengo, Jean-Franços Paement, Pascal Vncent Olver Delalleau, Ncolas Le Roux and Mare Oumet Département d Informatque

More information

Addendum to: Importing Skill-Biased Technology

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

More information

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

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

Systems with Persistent Memory: the Observation Inequality Problems and Solutions

Systems with Persistent Memory: the Observation Inequality Problems and Solutions Chapter 6 Systems with Persistent Memory: the Observation Inequality Problems and Solutions Facts that are recalled in the problems wt) = ut) + 1 c A 1 s ] R c t s)) hws) + Ks r)wr)dr ds. 6.1) w = w +

More information

arxiv:1311.2444v1 [cs.dc] 11 Nov 2013

arxiv:1311.2444v1 [cs.dc] 11 Nov 2013 FLEXIBLE PARALLEL ALGORITHMS FOR BIG DATA OPTIMIZATION Francsco Facchne 1, Smone Sagratella 1, Gesualdo Scutar 2 1 Dpt. of Computer, Control, and Management Eng., Unversty of Rome La Sapenza", Roma, Italy.

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

Properties of BMO functions whose reciprocals are also BMO

Properties of BMO functions whose reciprocals are also BMO Properties of BMO functions whose reciprocals are also BMO R. L. Johnson and C. J. Neugebauer The main result says that a non-negative BMO-function w, whose reciprocal is also in BMO, belongs to p> A p,and

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

Finite Math Chapter 10: Study Guide and Solution to Problems

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

More information

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

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

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

Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio

Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio Vascek s Model of Dstrbuton of Losses n a Large, Homogeneous Portfolo Stephen M Schaefer London Busness School Credt Rsk Electve Summer 2012 Vascek s Model Important method for calculatng dstrbuton of

More information

Subexponential time relations in the class group of large degree number fields

Subexponential time relations in the class group of large degree number fields Subexponental tme relatons n the class group of large degree number felds Jean-Franços Basse Unversty of Calgary 2500 Unversty Drve NW Calgary, Alberta, Canada T2N 1N4 Abstract Hafner and McCurley descrbed

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