Chebyshev approximations in subspaces of spline functions

The main purpose of this paper is to consider strict approximations from subspaces of spline functions of degree m-1 with k fixed knots. Rice defines the strict approximation which is a particular unique best Chebyshev approximation for problems defined on a finite set. In order to determine best approximations on an interval I we define a sequence of strict approximations on finite subsets of I where the subsets fill up the interval. It is shown that the sequences always converge if k≤m. In the case k>m the sequences are convergent if we restrict ourselves to problems defined on certain subsets of I. It seems to be natural to denote these limits as strict approximations. To be able to compute these functions we also develop a Remez type algorithm.     

An iterative computation of approximations on Korobov-like spaces

This paper treats the multidimensional application of a previous iterative Monte Carlo algorithm that enables the computation of approximations in L². The case of regular functions is studied using a Fourier basis on periodised functions, Legendre and Tchebychef polynomial bases. The dimensional effect is reduced by computing these approximations on Korobov-like spaces. Numerical results show the efficiency of the algorithm for both approximation and numerical integration.     

Zur Theorie der nichtlinearen Tschebyscheff-Approximation mit Nebenbedingungen

This paper deals with questions of nonlinear Tschebyscheff-approximation theory, the approximations being constrained by nonlinear relations. We assume the approximating functions depending Fréchet-differentiable on a parameter and the constraints satisfying certain regularity and differentiability properties. Under these hypotheses in the main theorem we give necessary conditions to characterisize best approximations. Using these results, some problems in approximating functions, the best approximations being regarded to satisfy interpolatory conditions, are discussed. We deduce, that in this case best approximations admit a characterisation by generalized alternants.

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Erster Teil einer gekürzten Fassung der Dissertation des Verfassers [1968].     

A tighter variant of Jensen’s lower bound for stochastic programs and separable approximations to recourse functions

In this paper, we propose a new method to compute lower bounds on the optimal objective value of a stochastic program and show how this method can be used to construct separable approximations to the recourse functions. We show that our method yields tighter lower bounds than Jensen’s lower bound and it requires a reasonable amount of computational effort even for large problems. The fundamental idea behind our method is to relax certain constraints by associating dual multipliers with them. This yields a smaller stochastic program that is easier to solve. We particularly focus on the special case where we relax all but one of the constraints. In this case, the recourse functions of the smaller stochastic program are one dimensional functions. We use these one dimensional recourse functions to construct separable approximations to the original recourse functions. Computational experiments indicate that our lower bounds can significantly improve Jensen’s lower bound and our recourse function approximations can provide good solutions.     

Error estimates for approximate approximations with Gaussian kernels on compact intervals

The aim of this paper is the investigation of the error which results from the method of approximate approximations applied to functions defined on compact intervals, only. This method, which is based on an approximate partition of unity, was introduced by Maz’ya in 1991 and has mainly been used for functions defined on the whole space up to now. For the treatment of differential equations and boundary integral equations, however, an efficient approximation procedure on compact intervals is needed.In the present paper we apply the method of approximate approximations to functions which are defined on compact intervals. In contrast to the whole space case here a truncation error has to be controlled in addition. For the resulting total error pointwise estimates and L₁-estimates are given, where all the constants are determined explicitly.     

符号函数的Legendre非线性逼近

1引言许多数学和物理工作者研究了逼近形式正交多项式级数的具有较好收敛性的非线性方法,如文献[2-5,9]．这些非线性逼近方法的一个共同点是使用了线性级数中正交多项式的母函数．众所周知,的符号函数具有很多的应用,如文献[7]利用符号函数的积分表示来分析相联存储器的回想过程．文献[1]及其中所引用的一些文献为了获得交迭格Dirac算子,讨论了符号函数的有理逼近和连分式展开．在本文中,我们研究符号函数的Lengendre     

An improved estimate of PSWF approximation and approximation by Mathieu functions

In this paper, an error estimate of spectral approximations by prolate spheroidal wave functions (PSWFs) with explicit dependence on the bandwidth parameter and optimal order of convergence is derived, which improves the existing result in [Chen et al., Spectral methods based on prolate spheroidal wave functions for hyperbolic PDEs, SIAM J. Numer. Anal. 43 (5) (2005) 1912-1933]. The underlying argument is applied to analyze spectral approximations of periodic functions by Mathieu functions, which leads to new estimates featured with explicit dependence on the intrinsic parameter.     

Double precision approximations to the elementary functions using Jacobi-fractions

Summary In this paper Chebyshev best fit approximations by rational functions are given to the functionsln, exp, sin, cos andarctan. The approximations are presented in two ways: by a Thiele-type fraction and by a Jacobi-fraction. While the Thielefractions are time consuming, but well behaved with respect to error propagation, the Jacobi-fractions are extremely fast in evaluation, but need provision for some guard digits to preserve the full precision of approximation. Error propagation and stability have been tested by several methods, e.g. using the TRIPLEX MAINZ S 2002Algol Compiler version implemented by N. Krier. The approximations presented here are a part of a CONTROL DATA 3300 FORTRAN Double REAL subsystem, where they run successfully. Higher precision approximations up to 40 decimal places using the same method are available from the author.In the model procedures given in the following it is understood that real arithmetic as well as integer arithmetic and the standard functions used work on operands of one same (extended) length.     

A comparative study of Gaussian geostatistical models and Gaussian Markov random field models

Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two distinct approaches commonly used in spatial models for modeling point-referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed : (i) the Kullback-Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM_2.5 collected in California is presented to illustrate the proposed method.     

Simple Approximations for the GI/G/c Queue-II: The Moments,the Inverse Distribution Function and the Loss Function of the Number in the System and of the Queue Delay

Based on results obtained in part I of this paper, approximations for the first four moments of the number in the system are developed and thence used to approximate the inverse distribution function (IDF) and the loss functions (LF), employing Shore's general approximations. Existing approximations for the first two moments of queueing time in a GI/G/l queue serve to approximate the IDF and the LF of queueing time in the corresponding GI/G/c queue. The accuracy attained is generally satisfactory, while a remarkable algebraic simplicity is preserved. A numerical example demonstrates the applicability of some of the new approximations to solve optimization problems.     

New Sobolev spaces via generalized Poincaré inequalities on metric measure spaces

In this paper, we introduce some new function spaces of Sobolev type on metric measure spaces. These new function spaces are defined by variants of Poincaré inequalities associated with generalized approximations of the identity, and they generalize the classical Sobolev spaces on Euclidean spaces. We then obtain two characterizations of these new Sobolev spaces including the characterization in terms of a variant of local sharp maximal functions associated with generalized approximations of the identity. For the well-known Hajłasz–Sobolev spaces on metric measure spaces, we also establish some new characterizations related to generalized approximations of the identity. Finally, we clarify the relations between the Sobolev-type spaces introduced in this paper and the Hajłasz–Sobolev spaces on metric measure spaces.     

Inference in hybrid Bayesian networks using mixtures of polynomials

The main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using mixture of polynomials (MOP) approximations of probability density functions (PDFs). Hybrid BNs contain a mix of discrete, continuous, and conditionally deterministic random variables. The conditionals for continuous variables are typically described by conditional PDFs. A major hurdle in making inference in hybrid BNs is marginalization of continuous variables, which involves integrating combinations of conditional PDFs. In this paper, we suggest the use of MOP approximations of PDFs, which are similar in spirit to using mixtures of truncated exponentials (MTEs) approximations. MOP functions can be easily integrated, and are closed under combination and marginalization. This enables us to propagate MOP potentials in the extended Shenoy-Shafer architecture for inference in hybrid BNs that can include deterministic variables. MOP approximations have several advantages over MTE approximations of PDFs. They are easier to find, even for multi-dimensional conditional PDFs, and are applicable for a larger class of deterministic functions in hybrid BNs.     

Approximation of functions by polynomials with various constraints

The pointwise approximations of functions with a given modulus of continuity of their r-th derivatives on a closed interval of the real axis are done with various additional constraints. The paper attempts to review such constraints.     

Error autocorrection in rational approximation and interval estimates. [A survey of results.]

The error autocorrection effect means that in a calculation all the intermediate errors compensate each other, so the final result is much more accurate than the intermediate results. In this case standard interval estimates (in the framework of interval analysis including the so-called a posteriori interval analysis of Yu. Matijasevich) are too pessimistic. We shall discuss a very strong form of the effect which appears in rational approximations to functions. The error autocorrection effect occurs in all efficient methods of rational approximation (e.g., best approxmations, Padé approximations, multipoint Padé approximations, linear and nonlinear Padé-Chebyshev approximations, etc.), where very significant errors in the approximant coefficients do not affect the accuracy of this approximant. The reason is that the errors in the coefficients of the rational approximant are not distributed in an arbitrary way, but form a collection of coefficients for a new rational approximant to the same approximated function. The understanding of this mechanism allows to decrease the approximation error by varying the approximation procedure depending on the form of the approximant. Results of computer experiments are presented. The effect of error autocorrection indicates that variations of an approximated function under some deformations of rather a general type may have little effect on the corresponding rational approximant viewed as a function (whereas the coefficients of the approximant can have very significant changes). Accordingly, while deforming a function for which good rational approximation is possible, the corresponding approximant’s error can rapidly increase, so the property of having good rational approximation is not stable under small deformations of the approximated functions. This property is “individual”, in the sense that it holds for specific functions.     

(ϕ, α)-Strong Summability of Fourier–Laplace Series for Functions Continuous on a Sphere

We establish upper bounds for approximations by generalized Totik strong means applied to deviations of Cezàro means of critical order for Fourier–Laplace series of continuous functions. The estimates obtained are represented in terms of uniform best approximations of continuous functions on a unit sphere.     

Approximations of pseudo-Boolean functions; applications to game theory

This paper studies the approximation of pseudo-Boolean functions by linear functions and more generally by functions of (at most) a specified degree. Here a pseudo-Boolean function means a real valued function defined on {0,1} ⁿ , and its degree is that of the unique multilinear polynomial that expresses it; linear functions are those of degree at most one. The approximation consists in choosing among all linear functions the one which is closest to a given function, where distance is measured by the Euclidean metric onR ²ⁿ . A characterization of the best linear approximation is obtained in terms of the average value of the function and its first derivatives. This leads to an explicit formula for computing the approximation from the polynomial expression of the given function. These results are later generalized to handle approximations of higher degrees, and further results are obtained regarding the interaction of approximations of different degrees. For the linear case, a certain constrained version of the approximation problem is also studied. Special attention is given to some important properties of pseudo-Boolean functions and the extent to which they are preserved in the approximation. A separate section points out the relevance of linear approximations to game theory and shows that the well known Banzhaf power index and Shapley value are obtained as best linear approximations of the game (each in a suitably defined sense).Supported by the Air Force Office of Scientific Research (under grant number AFOSR 89-0512 and AFOSR 90-0008 to Rutgers University), as well as the National Science Foundation (under grant number DMS 89-06870).     

Global and mid-range function approximation for engineering optimization

Global and mid-range approximation concepts are used in engineering optimisation in those cases were the commonly used local approximations are not available or applicable. In this paper the response surface method is discussed as a method to build both global and mid-range approximations of the objective and constraint functions. In this method analysis results in multiple design points are fitted on a chosen approximation model function by means of regression techniques. Especially global approximations rely heavily on appropriate choices of the model functions. This builds a serious bottleneck in applying the method. In mid-range approximations the model selection is much less critical. The response surface method is illustrated at two relatively simple design problems. For building global approximations a new method was developed by Sacks and co-workers, especially regarding the nature of computer experiments. Here, the analysis results in the design sites are exactly predicted, and model selection is more flexible compared to the response surface method. The method will be applied to an analytical test function and a simple design problem. Finally the methods are discussed and compared.     

Constrained best approximation in Hilbert space III. Applications ton-convex functions

This paper continues the study of best approximation in a Hilbert spaceX from a subsetK which is the intersection of a closed convex coneC and a closed linear variety, with special emphasis on application to then-convex functions. A subtle separation theorem is utilized to significantly extend the results in [4] and to obtain new results even for the “classical” cone of nonnegative functions. It was shown in [4] that finding best approximations inK to anyf inX can be reduced to the (generally much simpler) problem of finding best approximations to a certain perturbation off from either the coneC or a certain subconeC _F. We will show how to determine this subconeC _F, give the precise condition characterizing whenC _F=C, and apply and strengthen these general results in the practically important case whenC is the cone ofn-convex functions inL ₂ (a,b),     

Zur Stetigkeit rationaler T-Approximationen über unbeschränkten Intervallen

Summary In this paper, we study the continuity of the dependence of best rational. approximations to functions real-valued and continuous on unbounded intervals. We give sufficient conditions for continuity which seem to be also necessary.     

Polynomial approximations in the complex plane

Good polynomial approximations for analytic functions are potentially useful but are in short supply. A new approach introduced here involves the Lanczos τ-method, with perturbations proportional to Faber or Chebyshev polynomials for specific regions of the complex plane. The results show that suitable forms of the τ-method, which are easy to use, can produce near-minimax polynomial approximations for functions which satisfy linear differential equations with polynomial coefficients. In particular, some accurate approximations of low degree for Bessel functions are presented. An appendix describes a simple algorithm which generates polynomial approximations for the Bessel function J_ν(z) of any given order ν.