Quarkonial frames with compression properties

This chapter is about metric spaces, an abstract generalization of the real line that allows discussion of open and closed sets, limits, convergence, continuity, and similar properties. The usual distance function for the real line becomes an example of a metric. The other notions are defined in terms of the metric. The advantage of the generalization is that proofs of certain properties of the real line immediately go over to all other examples. Section 1 gives the definition of metric space and open set, and it lists a number of important examples, including Euclidean spaces and certain spaces of functions. Sections 2 through 4 develop properties of open and closed sets, continuity, and convergence of sequences that are simple generalizations of known facts about $\mathbb{R}$. Section 5 shows how a subset of a metric space can be made into a metric space so that the restriction of a continuous function from the whole space to the subset remains continuous. It also shows that three natural metrics for the product of two metric spaces lead to the same open sets, continuous functions, and convergent sequences. Section 6 shows that any metric space is “Hausdorff,” “regular,” and “normal,” and it goes on to exhibit three different countability hypotheses about a metric space as equivalent. A metric space with these properties is called “separable.” Section 7 concerns compactness and completeness. A metric space is defined to be “compact” if every open cover has a finite subcover. This property is equivalent to the condition that every sequence has a convergent subsequence. The Heine–Borel Theorem says that the compact sets of $\mathbb{R}^n$ are exactly the closed bounded sets. A number of the results early in Chapter I that were proved by the Bolzano–Weierstrass Theorem in the context of the real line are seen to extend to any compact metric space. A metric space is “complete” if every Cauchy sequence is convergent. A metric space is compact if and only if it is complete and “totally bounded.” Section 8 concerns connectedness, which is an abstraction of the property of an interval of the line that accounts for the Intermediate Value Theorem. Section 9 proves a fundamental result known as the Baire Category Theorem. A sample consequence of the theorem is that the pointwise limit of a sequence of continuous complex-valued functions on a complete metric space must have points where it is continuous. Section 10 studies the spaces of real-valued and complex-valued continuous functions on a compact metric space. A generalization of Ascoli's Theorem from the setting of Chapter I provides a characterization of compact sets in either of these spaces of continuous functions. A generalization of the Weierstrass Approximation Theorem, known as the Stone–Weierstrass Theorem, gives sufficient conditions for a subalgebra of either of these spaces of continuous functions to be dense. One consequence is that these spaces of continuous functions are separable. Section 11 constructs the “completion” of a metric space out of Cauchy sequences in the given space. The result is a complete metric space and a distance-preserving map of the given metric space into the completion such that the image is dense.

Object and image spaces are widely used in geometrical optics to describe the functions of optical systems. However, the mapping between these two spaces is non-linear, meaning that a function expressed in coordinate frames in the image space cannot be directly applied in the object space, and vice versa. For example, the relationship between the wavefront and ray aberrations given by [1] is valid for coordinate frames in the image space but fails for coordinate frames in the object space. To overcome this limitation, this study converts the wavefront-ray aberration relationship using the chain rule so that it can also be applied to coordinate frames in the object space. The numerical results obtained for the primary ray aberrations using the proposed converted relationship are shown to be in close agreement with the Zemax simulation results.

In the present paper, we study frames for finite-dimensional vector spaces over an arbitrary field. We develop a theory of dual frames in order to obtain and study the different representations of the elements of the vector space provided by a frame. We relate the introduced theory with the classical one of dual frames for Hilbert spaces and apply it to study dual frames for three types of vector spaces: for vector spaces over conjugate closed subfields of the complex numbers (in particular, for cyclotomic fields), for metric vector spaces, and for ultrametric normed vector spaces over complete non-archimedean valued fields. Finally, we consider the matrix representation of operators using dual frames and its application to the solution of operators equations in a Petrov-Galerkin scheme.

Integral-type operators on continuous function spaces on the real line

We give a generalization of a theorem concerning the change of basis for the matrix representation of unbounded operators defined in a scalar product space. We introduce for the proof a suitable structure which can be useful when one has to make operations with operators defined between different scalar product spaces.

In this paper we deal with special classes of second order elliptic differential operators on the real line. We show that these operators are generators of positive $C_{0}$-semigroups on weighted spaces of continuous functions and we represent them as limits of iterates of integral-type operators. By means of such representation, some qualitative properties of the semigroups are stated.

The paper is concerned with some characteristics of approximation by piecewise polynomial functions with nonfixed knots in various integral metrics Lp(a,b), 0 <p ≤∞ .

A multi-level discontinuous Galerkin method for solving the stationary Navier–Stokes equations

We design a new, fast algorithm for agnostically learning univariate probability distributions whose densities are well-approximated by piecewise polynomial functions. Let ƒ be the density function of an arbitrary univariate distribution, and suppose that ƒ is OPT-close in Li- distance to an unknown piecewise polynomial function with t interval pieces and degree d. For any γ > 0, our algorithm draws n = Õγ(t(d + 1)/ ∊2) samples from ƒ, runs in time Õ(n), and with probability at least 9/10 outputs an Ογ (t)-piecewise degree-d hypothesis H that is (3 + γ) · OPT + ∊ close to f. Our approximation factor almost matches the best known information-theoretic (but computationally inefficient) upper bound of 3.Our general algorithm yields (nearly) sample- optimal and nearly-linear time estimators for a wide range of structured distribution families over both continuous and discrete domains in a unified way. For most of our applications, these are the first sample-optimal and nearly-linear time estimators in the literature. As a consequence, our work resolves the sample and computational complexities of a broad class of inference tasks via a single “meta-algorithm”. Moreover, we demonstrate that our algorithm performs very well in experiments.Our algorithm consists of three levels: (i) At the top level, we employ an iterative greedy algorithm for finding a good partition of the real line into the pieces of a piecewise polynomial. (ii) For each piece, we show that the sub-problem of finding a good polynomial fit on the current interval can be solved efficiently with a separation oracle method. (iii) We reduce the task of finding a separating hyperplane to a combinatorial problem and design a nearly-linear algorithm for this problem. Combining these three procedures gives a density estimation algorithm with the claimed guarantees.

Korovkin type approximation theorems are useful tools to check whether a given sequence ( L n ) n ≥ 1 of positive linear operators on C[0,1] of all continuous functions on the real interval [0,1] is an approximation process. That is, these theorems exhibit a variety of test functions, which assure that the approximation property holds on the whole space if it holds for them. Such a property was discovered by Korovkin in 1953 for the functions 1, x and x 2 in the space C[0,1] as well as for the functions 1, cos and sin in the space of all continuous 2π-periodic functions on the real line. In this paper, we use the notion of statistical summability (C,1) to prove the Korovkin approximation theorem for the functions 1, cos and sin in the space of all continuous 2π-periodic functions on the real line and show that our result is stronger. We also study the rate of weighted statistical convergence.MSC:41A10, 41A25, 41A36, 40A30, 40G15.

The independent increments process, also known as (positive) Levy process, with its associated Levy measure has emerged as an important vehicle in the development of a group of processes such as neutral to the right, gamma, extended gamma, beta, and beta-Stacy processes that are discussed cohesively in this chapter. After a brief review of the Levy process and its generalization the completely random measure and of the Poisson Process which is used in its construction, we present a detail treatment of the neutral to the right process defined on the real line since all other processes discussed here can be classified as of the neutral to the right type. It is shown to have the desirable conjugacy property which facilitates the Bayesian analysis of statistical data even in the case of right censored data which the Dirichlet process fails to do. We devote a sizable effort to the derivation of the posterior distribution under this prior and simulation procedures necessary thereof, since it carries over to other neutral to the right type processes as well. Thereafter we introduce a simple case of Levy process, namely the gamma process, in which the independent increments are assumed to be distributed as gamma distribution, followed by its convolution with a known function, the extended gamma process, to serve as a prior on the space of hazard functions.We discuss next the beta process, which was developed to place a prior on the space of cumulative hazard functions defined on the real line, and also indirectly on the space of distribution functions, along with its important properties. The Levy measure of the beta process when viewed on an arbitrary space is shown to be useful in generating a prior process known as the Indian buffet process, which serves as a prior on the space of sparse binary matrices encountered in featural modeling. Further generalizations of the beta process, such as the stable and kernel beta processes, are also considered in the same context. A special case of the Levy process—the log-beta process, in which the increments are assumed to have a beta-Stacy distribution, is used in developing the beta-Stacy process presented next, and noted that the latter not only generalizes the Dirichlet process but is also conjugate with respect to data which may include right censored observations as well. Finally, we present the derivation of three special processes, namely the Chinese restaurant, the Indian buffet, and infinite gamma-Poisson processes, which have emerged to be very useful in areas outside the statistical field, such as machine learning, information retrieval, and featural modeling. We also discuss in the chapter various properties, special characteristics, essential features and limitations, as well as challenges in applications of the above mentioned processes.

Optimal Approximation of Periodic Analytic Functions with Integrable Boundary Values

We prove a theorem about elliptic operators with symmetric potential functions, defined on a function space over a closed loop. The result is similar to a known result for a function space on an interval with Dirichlet boundary conditions. These theorems provide accurate numerical methods for finding the spectra of those operators over either type of function space. As an application, we numerically compute the Morse index of constant mean curvature tori of revolution in the unit $3$-sphere $\mathbb{S}^3$, confirming that every such torus has Morse index at least five, and showing that other known lower bounds for this Morse index are close to optimal.

The bases used in quantum mechanics, in general, are orthonormal. The relations in orthonormal basis vectors are simpler than those in nonorthogonal basis vectors. Also, the operators that represent different physical quantities are Hermitian and the eigenvectors of these operators belonging to different eigenvalues are orthonormal to each other. But, in many cases, such as sets of screened hydrogenic wavefunctions, nonorthogonal sets of basis vectors occur. It is, therefore, necessary to consider the representation of vectors and operators in nonorthogonal bases. Making use of the set of vectors reciprocal to the given set of nonorthogonal base vectors, the expression for the norms of vectors and for the matrix elements has been found. Also, adjoint and Hermitian matrices in nonorthogonal bases have been worked out using a set of reciprocal basis vectors.

Low-distortion embeddings of infinite metric spaces into the real line