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1.
In this paper, the problem of phase reconstruction from magnitude of multidimensional band-limited functions is considered. It is shown that any irreducible band-limited function f(z1…,zn), zi ? C, i=1, …, n, is uniquely determined from the magnitude of f(x1…,xn): | f(x1…,xn)|, xi ? R, i=1,…, n, except for (1) linear shifts: i(α1z1+…+αn2n+β), β, αi?R, i=1,…, n; and (2) conjugation: f1(z11,…,zn1).  相似文献   

2.
Let X1, X2,… be a sequence of i.i.d. random variables and Sn their partial sums. Necessary and sufficient conditions are given for {n?1qSn}1 to have uniformly bounded pth moments, 0<p<q?2.Some of the results are generalized to martingle differences.  相似文献   

3.
4.
Let xi ≥ 0, yi ≥ 0 for i = 1,…, n; and let aj(x) be the elementary symmetric function of n variables given by aj(x) = ∑1 ≤ ii < … <ijnxiixij. Define the partical ordering x <y if aj(x) ≤ aj(y), j = 1,… n. We show that x $?y ? xα$?yα, 0 $?α ≤ 1, where {xα}i = xαi. We also give a necessary and sufficient condition on a function f(t) such that x <y ? f(x) <f(y). Both results depend crucially on the following: If x <y there exists a piecewise differentiable path z(t), with zi(t) ≥ 0, such that z(0) = x, z(1) = y, and z(s) <z(t) if 0 ≤ st ≤ 1.  相似文献   

5.
A single serving queueing model is studied where potential customers are discouraged at the rate λn = λqn, 0 < q < 1, n is the queue length. The serving rate is μn = μ(1 ? qn), n = 0, 1,…. The spectral function is computed and the corresponding set of orthogonal polynomials is studied in detail. The slightly more general model with λn = λqn(1 + bqn), μn = μ(1 ? qn)(1 + bqn) and the analogous orthogonal polynomials are also investigated. In both cases a method developed by Pollaczek is used which has been used very successfully to study new sets of orthogonal polynomials by Askey and Ismail.  相似文献   

6.
Let L be a finite-dimensional normed linear space and let M be a compact subset of L lying on one side of a hyperplane through 0. A measure of flatness for M is the number D(M) = inf{supf(x)f(y): x, y ? M}, where the infimum is over all f in L1 which are positive on M. Thus D(M) = 1 if M is flat, but otherwise D(M) > 1. On the other hand, let E(M) be a second measure on M defined as follows: If M is linearly independent, E(M) = 1. If M is linearly dependent, then (1) let Z be a minimal, linearly dependent subset of M; (2) partition Z into mutually exclusive subsets U = {u1, …, up} and V = {v1, …, vq} such that there exist positive coefficients ai and bi for which Σi = 1paiui = Σi = 1qbivi; (3) let r = max{Σi = 1p aiΣi = 1q bi, Σi = 1p biΣi = 1q ai}; (4) let E(M) be the supremum of all ratios r which can be formed by steps (1), (2) and (3). The main result of this paper is that these two measures are the same: D(M) = E(M). This result is then used to obtain results concerning the Banach distance-coefficient between an arbitrary finite-dimensional normed linear space and Hilbert space.  相似文献   

7.
Let π = (a1, a2, …, an), ? = (b1, b2, …, bn) be two permutations of Zn = {1, 2, …, n}. A rise of π is pair ai, ai+1 with ai < ai+1; a fall is a pair ai, ai+1 with ai > ai+1. Thus, for i = 1, 2, …, n ? 1, the two pairs ai, ai+1; bi, bi+1 are either both rises, both falls, the first a rise and the second a fall or the first a fall and the second a rise. These possibilities are denoted by RR, FF, RF, FR. The paper is concerned with the enumeration of pairs π, p with a given number of RR, FF, RF, FR. In particular if ωn denotes the number of pairs with RR forbidden, it is proved that 0ωnznn!n! = 1?(z), ?(z) = ∑0(-1) nznn!n!. More precisely if ω(n, k) denotes the number of pairs π, p with exactly k occurences of RR(or FF, RF, FR) then 1 + ∑n=1znn!n!n?1k=0 ω(n, k)xk = (1 ? x)(?(z(1 ? x)) ? x).  相似文献   

8.
Sharp inequalities are derived for certain (polynomial-like) functions of the real variables pi (i = 1(1)σ) by interpreting pi as the probabilities that various switches be thrown in certain directions. Parameters mv in the inequalities are at first taken to be integers; later the inequalities are established when mv are arbitrary real numbers. The side condition ∑pi = 1 occurs throughout analysis, so there are many corollaries. Examples of the inequalities established are
i=1σ (1?pim)m>K?1,
valid ifm>1
j=0rnjpjm(1?pm)m?j+1?j=0rnjpj(1?p?s)n?jm > 1+smax[m,n]
valid if m > 1, n > r + 1, 0 < p, s, p + s ? 1, and also valid if 0 < m < 1, 0 < n < r + 1 (1 ? x)u + x1u < 1, if12 < x < 1, u > 1. (1.03)  相似文献   

9.
Let A be an n-square normal matrix over C, and Qm, n be the set of strictly increasing integer sequences of length m chosen from 1,…, n. For α,βQm, n denote by A[α|β] the submatrix obtained from A by using rows numbered α and columns numbered β. For k∈{0,1,…,m} write z.sfnc;αβ|=k if there exists a rearrangement of 1,…,m, say i1,…,ik, ik+1,…,im, such that α(ij)=β(ij), j=1,…,k, and {α(ik+1),…,α(im)};∩{β(ik+1),…,β(im)}=ø. Let
be the group of n-square unitary matrices. Define the nonnegative number
?k(A)= maxU∈|det(U1AU) [α|β]|
, where |αβ|=k. Theorem 1 establishes a bound for ?k(A), 0?k<m?1, in terms of a classical variational inequality due to Fermat. Let A be positive semidefinite Hermitian, n?2m. Theorem 2 leads to an interlacing inequality which, in the case n=4, m=2, resolves in the affirmative the conjecture that
?m(A)??m?1(A)????0(A)
.  相似文献   

10.
For a sequence A = {Ak} of finite subsets of N we introduce: δ(A) = infm?nA(m)2n, d(A) = lim infn→∞ A(n)2n, where A(m) is the number of subsets Ak ? {1, 2, …, m}.The collection of all subsets of {1, …, n} together with the operation a ∪ b, (a ∩ b), (a 1 b = a ∪ b ? a ∩ b) constitutes a finite semi-group N (semi-group N) (group N1). For N, N we prove analogues of the Erdös-Landau theorem: δ(A+B) ? δ(A)(1+(2λ)?1(1?δ(A>))), where B is a base of N of the average order λ. We prove for N, N, N1 analogues of Schnirelmann's theorem (that δ(A) + δ(B) > 1 implies δ(A + B) = 1) and the inequalities λ ? 2h, where h is the order of the base.We introduce the concept of divisibility of subsets: a|b if b is a continuation of a. We prove an analog of the Davenport-Erdös theorem: if d(A) > 0, then there exists an infinite sequence {Akr}, where Akr | Akr+1 for r = 1, 2, …. In Section 6 we consider for N∪, N∩, N1 analogues of Rohrbach inequality: 2n ? g(n) ? 2n, where g(n) = min k over the subsets {a1 < … < ak} ? {0, 1, 2, …, n}, such that every m? {0, 1, 2, …, n} can be expressed as m = ai + aj.Pour une série A = {Ak} de sous-ensembles finis de N on introduit les densités: δ(A) = infm?nA(m)2m, d(A) = lim infn→∞ A(n)2nA(m) est le nombre d'ensembles Ak ? {1, 2, …, m}. L'ensemble de toutes les parties de {1, 2, …, n} devient, pour les opérations a ∪ b, a ∩ b, a 1 b = a ∪ b ? a ∩ b, un semi-groupe fini N, N ou un groupe N1 respectivement. Pour N, N on démontre l'analogue du théorème de Erdös-Landau: δ(A + B) ? δ(A)(1 + (2λ)?1(1?δ(A))), où B est une base de N d'ordre moyen λ. On démontre pour N, N, N1 l'analogue du théorème de Schnirelmann (si δ(A) + δ(B) > 1, alors δ(A + B) = 1) et les inégalités λ ? 2h, où h est l'ordre de base. On introduit le rapport de divisibilité des enembles: a|b, si b est une continuation de a. On démontre l'analogue du théorème de Davenport-Erdös: si d(A) > 0, alors il existe une sous-série infinie {Akr}, où Akr|Akr+1, pour r = 1, 2, … . Dans le Paragraphe 6 on envisage pour N, N, N1 les analogues de l'inégalité de Rohrbach: 2n ? g(n) ? 2n, où g(n) = min k pour les ensembles {a1 < … < ak} ? {0, 1, 2, …, n} tels que pour tout m? {0, 1, 2, …, n} on a m = ai + aj.  相似文献   

11.
For fixed p (0 ≤ p ≤ 1), let {L0, R0} = {0, 1} and X1 be a uniform random variable over {L0, R0}. With probability p let {L1, R1} = {L0, X1} or = {X1, R0} according as X112(L0 + R0) or < 12(L0 + R0); with probability 1 ? p let {L1, R1} = {X1, R0} or = {L0, X1} according as X112(L0 + R0) or < 12(L0 + R0), and let X2 be a uniform random variable over {L1, R1}. For n ≥ 2, with probability p let {Ln, Rn} = {Ln ? 1, Xn} or = {Xn, Rn ? 1} according as Xn12(Ln ? 1 + Rn ? 1) or < 12(Ln ? 1 + Rn ? 1), with probability 1 ? p let {Ln, Rn} = {Xn, Rn ? 1} or = {Ln ? 1, Xn} according as Xn12(Ln ? 1 + Rn ? 1) or < 12(Ln ? 1 + Rn ? 1), and let Xn + 1 be a uniform random variable over {Ln, Rn}. By this iterated procedure, a random sequence {Xn}n ≥ 1 is constructed, and it is easy to see that Xn converges to a random variable Yp (say) almost surely as n → ∞. Then what is the distribution of Yp? It is shown that the Beta, (2, 2) distribution is the distribution of Y1; that is, the probability density function of Y1 is g(y) = 6y(1 ? y) I0,1(y). It is also shown that the distribution of Y0 is not a known distribution but has some interesting properties (convexity and differentiability).  相似文献   

12.
It is known that the classical orthogonal polynomials satisfy inequalities of the form Un2(x) ? Un + 1(x) Un ? 1(x) > 0 when x lies in the spectral interval. These are called Turan inequalities. In this paper we will prove a generalized Turan inequality for ultraspherical and Laguerre polynomials. Specifically if Pnλ(x) and Lnα(x) are the ultraspherical and Laguerre polynomials and Fnλ(x) = Pnλ(x)Pnλ(1), Gnα(x) = Lnα(x)Lnα(0), then Fnα(x) Fnβ(x) ? Fn + 1α(x) Fn ? 1β(x) > 0, ? 1 < x < 1, ?12 < α ? β ? α + 1 and Gnα(x) Gnβ(x) ? Gn + 1α(x) Gn ? 1β(x) > 0, x > 0, 0 < α ? β ? α + 1. We also prove the inequality (n + 1) Fnα(x) Fnβ(x) ? nFn + 1α(x) Fn ? 1β(x) > An[Fnα(x)]2, ?1 < x < 1, ?12 < α ? β < α + 1, where An is a positive constant depending on α and β.  相似文献   

13.
If k is a perfect field of characteristic p ≠ 0 and k(x) is the rational function field over k, it is possible to construct cyclic extensions Kn over k(x) such that [K : k(x)] = pn using the concept of Witt vectors. This is accomplished in the following way; if [β1, β2,…, βn] is a Witt vector over k(x) = K0, then the Witt equation yp ? y = β generates a tower of extensions through Ki = Ki?1(yi) where y = [y1, y2,…, yn]. In this paper, it is shown that there exists an alternate method of generating this tower which lends itself better for further constructions in Kn. This alternate generation has the form Ki = Ki?1(yi); yip ? yi = Bi, where, as a divisor in Ki?1, Bi has the form (Bi) = qΠpjλj. In this form q is prime to Πpjλj and each λj is positive and prime to p. As an application of this, the alternate generation is used to construct a lower-triangular form of the Hasse-Witt matrix of such a field Kn over an algebraically closed field of constants.  相似文献   

14.
The probability measure of X = (x0,…, xr), where x0,…, xr are independent isotropic random points in Rn (1 ≤ rn ? 1) with absolutely continuous distributions is, for a certain class of distributions of X, expressed as a product measure involving as factors the joint probability measure of (ω, ?), the probability measure of p, and the probability measure of Y1 = (y01,…, yr1). Here ω is the r-subspace parallel to the r-flat η determined by X, ? is a unit vector in ω with ‘initial’ point at the origin [ω is the (n ? r)-subspace orthocomplementary to ω], p is the norm of the vector z from the origin to the orthogonal projection of the origin on η, and yi1 = (xi ? z)α(p2), where α is a scale factor determined by p. The probability measure for ω is the unique probability measure on the Grassmann manifold of r-subspaces in Rn invariant under the group of rotations in Rn, while the conditional probability measure of ? given ω is uniform on the boundary of the unit (n ? r)-ball in ω with centre at the origin. The decomposition allows the evaluation of the moments, for a suitable class of distributions of X, of the r-volume of the simplicial convex hull of {x0,…, xr} for 1 ≤ rn.  相似文献   

15.
Orthogonal polynomials on the multivariate negative binomial distribution,
(1 + Θ)?α?x(πj=0pΘjxjxj!) Γ(α + x)Γ(α)
where α > 0, Θ1 > 0, x = ΣΘi, x0, x1, …, xp = 0,1, … are constructed and their properties studied.  相似文献   

16.
Let n denote the sample size, and let ri ∈ {1,…,n} fulfill the conditions ri ? ri?1 ≥ 5 for i = 1,…,k. It is proved that the joint normalized distribution of the order statistics Zri:n, i = 1,…,k, is independent of the underlying probability measure up to a remainder term of order O((kn)12). A counterexample shows that, as far as central order statistics are concerned, this remainder term is not of the order O((kn)12) if ri ? ri?1 = 1 for i = 2,…,k.  相似文献   

17.
For a(1) ? a(2) ? ··· ? a(n) ? 0, b(1) ? b(2) ? ··· ? b(n) ? 0, the ordered values of ai, bi, i = 1, 2,…, n, m fixed, m ? n, and p ? 1 it is shown that
1naibi ? 1map(i)1p1m?k?1 bq(i)+bq[m?k](k+1)qp1q
where 1p + 1q = 1, b[j] = b(j) + b(j + 1) + ··· + b(n), and k is the integer such that b(m ? k ? 1) ? b[m ? k](k + 1) and b(m ? k) < b[m ? k + 1]k. The inequality is shown to be sharp. When p < 1 and a(i)'s are in increasing order then the inequality is reversed.  相似文献   

18.
Let S be a Dirichlet form in L2(Ω; m), where Ω is an open subset of Rn, n ? 2, and m a Radon measure on Ω; for each integer k with 1 ? k < n, let Sk be a Dirichlet form on some k-dimensional submanifold Ωk of Ω. The paper is devoted to the study of the closability of the forms E with domain C0(Ω) and defined by: (?,g)=E(?, g)+ ip=1Eki(?ki, gki) where 1 ? kp < ? < n, and where ?ki, gki denote restrictions of ?, g in C0(Ω) to Ωki. Conditions are given for E to be closable if, for each i = 1,…, p, one has ki = n ? i. Other conditions are given for E to be nonclosable if, for some i, ki < n ? i.  相似文献   

19.
20.
Let {Xn}n≥1 be a sequence of independent and identically distributed random variables. For each integer n ≥ 1 and positive constants r, t, and ?, let Sn = Σj=1nXj and E{N(r, t, ?)} = Σn=1 nr?2P{|Sn| > ?nrt}. In this paper, we prove that (1) lim?→0+?α(r?1)E{N(r, t, ?)} = K(r, t) if E(X1) = 0, Var(X1) = 1, and E(| X1 |t) < ∞, where 2 ≤ t < 2r ≤ 2t, K(r, t) = {2α(r?1)2Γ((1 + α(r ? 1))2)}{(r ? 1) Γ(12)}, and α = 2t(2r ? t); (2) lim?→0+G(t, ?)H(t, ?) = 0 if 2 < t < 4, E(X1) = 0, Var(X1) > 0, and E(|X1|t) < ∞, where G(t, ?) = E{N(t, t, ?)} = Σn=1nt?2P{| Sn | > ?n} → ∞ as ? → 0+ and H(t, ?) = E{N(t, t, ?)} = Σn=1 nt?2P{| Sn | > ?n2t} → ∞ as ? → 0+, i.e., H(t, ?) goes to infinity much faster than G(t, ?) as ? → 0+ if 2 < t < 4, E(X1) = 0, Var(X1) > 0, and E(| X1 |t) < ∞. Our results provide us with a much better and deeper understanding of the tail probability of a distribution.  相似文献   

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