Algorithms for Asymptotic Extrapolation∗
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Mathematical Construction of Interpolation and Extrapolation Function by Taylor Polynomials Arxiv:2002.11438V1 [Math.NA] 26 Fe
Mathematical Construction of Interpolation and Extrapolation Function by Taylor Polynomials Nijat Shukurov Department of Engineering Physics, Ankara University, Ankara, Turkey E-mail: [email protected] , [email protected] Abstract: In this present paper, I propose a derivation of unified interpolation and extrapolation function that predicts new values inside and outside the given range by expanding direct Taylor series on the middle point of given data set. Mathemati- cal construction of experimental model derived in general form. Trigonometric and Power functions adopted as test functions in the development of the vital aspects in numerical experiments. Experimental model was interpolated and extrapolated on data set that generated by test functions. The results of the numerical experiments which predicted by derived model compared with analytical values. KEYWORDS: Polynomial Interpolation, Extrapolation, Taylor Series, Expansion arXiv:2002.11438v1 [math.NA] 26 Feb 2020 1 1 Introduction In scientific experiments or engineering applications, collected data are usually discrete in most cases and physical meaning is likely unpredictable. To estimate the outcomes and to understand the phenomena analytically controllable functions are desirable. In the mathematical field of nu- merical analysis those type of functions are called as interpolation and extrapolation functions. Interpolation serves as the prediction tool within range of given discrete set, unlike interpola- tion, extrapolation functions designed to predict values out of the range of given data set. In this scientific paper, direct Taylor expansion is suggested as a instrument which estimates or approximates a new points inside and outside the range by known individual values. Taylor se- ries is one of most beautiful analogies in mathematics, which make it possible to rewrite every smooth function as a infinite series of Taylor polynomials. -
Stable Extrapolation of Analytic Functions
STABLE EXTRAPOLATION OF ANALYTIC FUNCTIONS LAURENT DEMANET AND ALEX TOWNSEND∗ Abstract. This paper examines the problem of extrapolation of an analytic function for x > 1 given perturbed samples from an equally spaced grid on [−1; 1]. Mathematical folklore states that extrapolation is in general hopelessly ill-conditioned, but we show that a more precise statement carries an interesting nuance. For a function f on [−1; 1] that is analytic in a Bernstein ellipse with parameter ρ > 1, and for a uniform perturbation level " on the function samples, we construct an asymptotically best extrapolant e(x) as a least squares polynomial approximant of degree M ∗ given explicitly. We show that the extrapolant e(x) converges to f(x) pointwise in the interval −1 Iρ 2 [1; (ρ+ρ )=2) as " ! 0, at a rate given by a x-dependent fractional power of ". More precisely, for each x 2 Iρ we have p x + x2 − 1 jf(x) − e(x)j = O "− log r(x)= log ρ ; r(x) = ; ρ up to log factors, provided that the oversampling conditioning is satisfied. That is, 1 p M ∗ ≤ N; 2 which is known to be needed from approximation theory. In short, extrapolation enjoys a weak form of stability, up to a fraction of the characteristic smoothness length. The number of function samples, N + 1, does not bear on the size of the extrapolation error provided that it obeys the oversampling condition. We also show that one cannot construct an asymptotically more accurate extrapolant from N + 1 equally spaced samples than e(x), using any other linear or nonlinear procedure. -
On Sharp Extrapolation Theorems
ON SHARP EXTRAPOLATION THEOREMS by Dariusz Panek M.Sc., Mathematics, Jagiellonian University in Krak¶ow,Poland, 1995 M.Sc., Applied Mathematics, University of New Mexico, USA, 2004 DISSERTATION Submitted in Partial Ful¯llment of the Requirements for the Degree of Doctor of Philosophy Mathematics The University of New Mexico Albuquerque, New Mexico December, 2008 °c 2008, Dariusz Panek iii Acknowledgments I would like ¯rst to express my gratitude for M. Cristina Pereyra, my advisor, for her unconditional and compact support; her unbounded patience and constant inspi- ration. Also, I would like to thank my committee members Dr. Pedro Embid, Dr Dimiter Vassilev, Dr Jens Lorens, and Dr Wilfredo Urbina for their time and positive feedback. iv ON SHARP EXTRAPOLATION THEOREMS by Dariusz Panek ABSTRACT OF DISSERTATION Submitted in Partial Ful¯llment of the Requirements for the Degree of Doctor of Philosophy Mathematics The University of New Mexico Albuquerque, New Mexico December, 2008 ON SHARP EXTRAPOLATION THEOREMS by Dariusz Panek M.Sc., Mathematics, Jagiellonian University in Krak¶ow,Poland, 1995 M.Sc., Applied Mathematics, University of New Mexico, USA, 2004 Ph.D., Mathematics, University of New Mexico, 2008 Abstract Extrapolation is one of the most signi¯cant and powerful properties of the weighted theory. It basically states that an estimate on a weighted Lpo space for a single expo- p nent po ¸ 1 and all weights in the Muckenhoupt class Apo implies a corresponding L estimate for all p; 1 < p < 1; and all weights in Ap. Sharp Extrapolation Theorems track down the dependence on the Ap characteristic of the weight. -
Convergence Acceleration Methods: the Past Decade *
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Computational and Applied Mathematics 12&13 (1985) 19-36 19 North-Holland Convergence acceleration methods: The past decade * Claude BREZINSKI Laboratoire d’AnaIyse Numkrique et d’optimisation, UER IEEA, Unicersitk de Lille I, 59655- Villeneuce diiscq Cedex, France Abstract: The aim of this paper is to present a review of the most significant results obtained the past ten years on convergence acceleration methods. It is divided into four sections dealing respectively with the theory of sequence transformations, the algorithms for such transformations, the practical problems related to their implementation and their applications to various subjects of numerical analysis. Keywords: Convergence acceleration, extrapolation. Convergence acceleration has changed much these last ten years not only because important new results, algorithms and applications have been added to those yet existing but mainly because our point of view on the subject completely changed thanks to the deep understanding that arose from the illuminating theoretical studies which were carried out. It is the purpose of this paper to present a ‘state of the art’ on convergence acceleration methods. It has been divided into four main sections. The first one deals with the theory of sequence transformations, the second section is devoted to the algorithms for sequence transfor- mations. The third section treats the practical problems involved in the use of convergence acceleration methods and the fourth section presents some applications to numerical analysis. In such a review it is, of course, impossible to analyze all the results which have been obtained and thus I had to make a choice among the contributions I know. -
3.2 Rational Function Interpolation and Extrapolation
104 Chapter 3. Interpolation and Extrapolation do 11 i=1,n Here we find the index ns of the closest table entry, dift=abs(x-xa(i)) if (dift.lt.dif) then ns=i dif=dift endif c(i)=ya(i) and initialize the tableau of c’s and d’s. d(i)=ya(i) http://www.nr.com or call 1-800-872-7423 (North America only), or send email to [email protected] (outside North Amer readable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, v Permission is granted for internet users to make one paper copy their own personal use. Further reproduction, or any copyin Copyright (C) 1986-1992 by Cambridge University Press. Programs Copyright (C) 1986-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN FORTRAN 77: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43064-X) enddo 11 y=ya(ns) This is the initial approximation to y. ns=ns-1 do 13 m=1,n-1 For each column of the tableau, do 12 i=1,n-m we loop over the current c’s and d’s and update them. ho=xa(i)-x hp=xa(i+m)-x w=c(i+1)-d(i) den=ho-hp if(den.eq.0.)pause ’failure in polint’ This error can occur only if two input xa’s are (to within roundoff)identical. den=w/den d(i)=hp*den Here the c’s and d’s are updated. c(i)=ho*den enddo 12 if (2*ns.lt.n-m)then After each column in the tableau is completed, we decide dy=c(ns+1) which correction, c or d, we want to add to our accu- else mulating value of y, i.e., which path to take through dy=d(ns) the tableau—forking up or down. -
Resummation of QED Perturbation Series by Sequence Transformations and the Prediction of Perturbative Coefficients
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CERN Document Server Resummation of QED Perturbation Series by Sequence Transformations and the Prediction of Perturbative Coefficients 1, 1 2 1 U. D. Jentschura ∗,J.Becher,E.J.Weniger, and G. Soff 1Institut f¨ur Theoretische Physik, TU Dresden, D-01062 Dresden, Germany 2Institut f¨ur Physikalische und Theoretische Chemie, Universit¨at Regensburg, D-93040 Regensburg, Germany (November 9, 1999) have also been used for the prediction of unknown per- We propose a method for the resummation of divergent turbative coefficients in quantum field theory [6–8]. The perturbative expansions in quantum electrodynamics and re- [l/m]Pad´e approximant to the quantity (g) represented lated field theories. The method is based on a nonlinear se- by the power series (1) is the ratio ofP two polynomials quence transformation and uses as input data only the numer- P (g)andQ (g) of degree l and m, respectively, ical values of a finite number of perturbative coefficients. The l m results obtained in this way are for alternating series superior P(g) p+pg+...+p gl to those obtained using Pad´e approximants. The nonlinear [l/m] (g)= l = 0 1 l . m sequence transformation fulfills an accuracy-through-order re- P Qm(g) 1+q1g+...+qm g lation and can be used to predict perturbative coefficients. In many cases, these predictions are closer to available analytic The polynomials Pl(g)andQm(g) are constructed so that results than predictions obtained using the Pad´e method. -
Practical Exrapolation Methods: Theory and Applications
This page intentionally left blank Practical Extrapolation Methods An important problem that arises in many scientific and engineering applications is that of approximating limits of infinite sequences {Am }. In most cases, these sequences converge very slowly. Thus, to approximate their limits with reasonable accuracy, one must compute a large number of the terms of {Am }, and this is generally costly. These limits can be approximated economically and with high accuracy by applying suitable extrapolation (or convergence acceleration) methods to a small number of terms of {Am }. This bookis concerned with the coherent treatment, including derivation, analysis, and applications, of the most useful scalar extrapolation methods. The methods it discusses are geared toward problems that commonly arise in scientific and engineering disci- plines. It differs from existing books on the subject in that it concentrates on the most powerful nonlinear methods, presents in-depth treatments of them, and shows which methods are most effective for different classes of practical nontrivial problems; it also shows how to fine-tune these methods to obtain best numerical results. This bookis intended to serve as a state-of-the-art reference on the theory and practice of extrapolation methods. It should be of interest to mathematicians interested in the theory of the relevant methods and serve applied scientists and engineers as a practical guide for applying speed-up methods in the solution of difficult computational problems. Avram Sidi is Professor of Numerical Analysis in the Computer Science Department at the Technion–Israel Institute of Technology and holds the Technion Administration Chair in Computer Science. He has published extensively in various areas of numerical analysis and approximation theory and in journals such as Mathematics of Computation, SIAM Review, SIAM Journal on Numerical Analysis, Journal of Approximation The- ory, Journal of Computational and Applied Mathematics, Numerische Mathematik, and Journal of Scientific Computing. -
3.1 Polynomial Interpolation and Extrapolation
108 Chapter 3. Interpolation and Extrapolation f(x, y, z). Multidimensional interpolation is often accomplished by a sequence of one-dimensional interpolations. We discuss this in 3.6. § CITED REFERENCES AND FURTHER READING: Abramowitz, M., and Stegun, I.A. 1964, Handbook of Mathematical Functions, Applied Mathe- visit website http://www.nr.com or call 1-800-872-7423 (North America only),or send email to [email protected] (outside North America). readable files (including this one) to any servercomputer, is strictly prohibited. To order Numerical Recipes books,diskettes, or CDROMs Permission is granted for internet users to make one paper copy their own personal use. Further reproduction, or any copying of machine- Copyright (C) 1988-1992 by Cambridge University Press.Programs Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) matics Series, Volume 55 (Washington: National Bureau of Standards; reprinted 1968 by Dover Publications, New York), 25.2. § Stoer, J., and Bulirsch, R. 1980, Introduction to Numerical Analysis (New York: Springer-Verlag), Chapter 2. Acton, F.S. 1970, Numerical Methods That Work; 1990, corrected edition (Washington: Mathe- matical Association of America), Chapter 3. Kahaner, D., Moler, C., and Nash, S. 1989, Numerical Methods and Software (Englewood Cliffs, NJ: Prentice Hall), Chapter 4. Johnson, L.W., and Riess, R.D. 1982, Numerical Analysis, 2nd ed. (Reading, MA: Addison- Wesley), Chapter 5. Ralston, A., and Rabinowitz, P. 1978, A First Course in Numerical Analysis, 2nd ed. (New York: McGraw-Hill), Chapter 3. Isaacson, E., and Keller, H.B. 1966, Analysis of Numerical Methods (New York: Wiley), Chapter 6. -
Conservative Sequence-To-Series Transformation Matrices
MATHEMATICS CONSERVATIVE SEQUENCE-TO-SERIES TRANSFORMATION MATRICES BY F. GERRISH (Communicated by Prof. J. F. KoKS:\'IA at the meeting of September 29, 1956) The three methods (sequence-to-sequence, series-to-sequence, series to-series) of defining generalized limits or generalized sums by infinite matrices are here complemented by a fourth (sequence-to-series); and a new class of matrices (</>-matrices) giving conservative transformations is introduced (§ 2), together with its regular sub-class (17-matrices). Products of these with each other and with the known types K, T, {3, y, <5, £X (§ I) of summability matrix are investigated and, in all cases when the product always exists, its type is ascertained. The results are tabulated at the end of § 4. Associativity of triple products of summability matrices is discussed (§ 5) in the cases when the product always exists for both bracketings. The work reveals useful new subclasses of the {3- and <5-matrices (here called {30, bll respectively) which are generalizations of the {30- and <50-matrices (§I) defined in a recent paper [4]. Some simple inclusion relations between the various classes of matrices are obtained in § 3, and used in subsequent sections. Notation. Throughout this paper all summations are from I to oo, 00 unless otherwise stated; thus L means L. Likewise limn will mean k k~l lim. In both cases the variable will be omitted when it is unambiguous. Given an infinite matrix X, M(X) will denote some fixed positive number associated with X. 1. Introduction Two well-known methods of defining the generalized limit of a sequence and the generalized sum of a series respectively by an infinite matrix ([I], 59, 65) give rise to the following matrix classes: 1.1. -
Resummation of QED Perturbation Series by Sequence Transformations and the Prediction of Perturbative Coefficients
Missouri University of Science and Technology Scholars' Mine Physics Faculty Research & Creative Works Physics 01 Sep 2000 Resummation of QED Perturbation Series by Sequence Transformations and the Prediction of Perturbative Coefficients Ulrich D. Jentschura Missouri University of Science and Technology, [email protected] Jens Becher Ernst Joachim Weniger Gerhard Soff Follow this and additional works at: https://scholarsmine.mst.edu/phys_facwork Part of the Physics Commons Recommended Citation U. D. Jentschura et al., "Resummation of QED Perturbation Series by Sequence Transformations and the Prediction of Perturbative Coefficients," Physical Review Letters, vol. 85, no. 12, pp. 2446-2449, American Physical Society (APS), Sep 2000. The definitive version is available at https://doi.org/10.1103/PhysRevLett.85.2446 This Article - Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Physics Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. VOLUME 85, NUMBER 12 PHYSICAL REVIEW LETTERS 18SEPTEMBER 2000 Resummation of QED Perturbation Series by Sequence Transformations and the Prediction of Perturbative Coefficients U. D. Jentschura,1,* J. Becher,1 E. J. Weniger,2,3 and G. Soff 1 1Institut für Theoretische Physik, TU Dresden, D-01062 Dresden, Germany 2Max-Planck-Institut für Physik Komplexer Systeme, Nöthnitzer Straße 38-40, 01187 Dresden, Germany 3Institut für Physikalische und Theoretische Chemie, Universität Regensburg, D-93040 Regensburg, Germany (Received 10 November 1999) We propose a method for the resummation of divergent perturbative expansions in quantum electro- dynamics and related field theories. -
Interpolation and Extrapolation in Statistics
Interpolation is a useful mathematical and statistical tool used to estimate values between two points. In this lesson, you will learn about this tool, its formula and how to use it. What Is Interpolation? Interpolation is the process of finding a value between two points on a line or curve. To help us remember what it means, we should think of the first part of the word, 'inter,' as meaning 'enter,' which reminds us to look 'inside' the data we originally had. This tool, interpolation, is not only useful in statistics, but is also useful in science, business or any time there is a need to predict values that fall within two existing data points. Interpolation Example Here's an example that will illustrate the concept of interpolation. A gardener planted a tomato plant and she measured and kept track of its growth every other day. This gardener is a curious person, and she would like to estimate how tall her plant was on the fourth day. Her table of observations looked like this: Based on the chart, it's not too difficult to figure out that the plant was probably 6 mm tall on the fourth day. This is because this disciplined tomato plant grew in a linear pattern; there was a linear relationship between the number of days measured and the plant's height growth. Linear pattern means the points created a straight line. We could even estimate by plotting the data on a graph. PDF Created with deskPDF PDF Writer - Trial :: http://www.docudesk.com But what if the plant was not growing with a convenient linear pattern? What if its growth looked more like this? What would the gardener do in order to make an estimation based on the above curve? Well, that is where the interpolation formula would come in handy. -
Interpolation and Extrapolation: Introduction
Interpolation and Extrapolation: Introduction [Nematrian website page: InterpolationAndExtrapolationIntro, © Nematrian 2015] Suppose we know the value that a function 푓(푥) takes at a set of points {푥0, 푥1, … , 푥푁−1} say, where we have ordered the 푥푖 so that 푥0 < 푥1 < ⋯ < 푥푁−1. However we do not have an analytic expression for 푓(푥) that allows us to calculate it at an arbitrary point. Often the 푥푖’s are equally spaced, but not necessarily. How might we best estimate 푓(푥) for arbitrary 푥 by in some sense drawing a smooth curve through and potentially beyond the 푥푖? If 푥0 < 푥 < 푥푁−1, i.e. within the range spanned by the 푥푖 then this problem is called interpolation, otherwise it is called extrapolation. To do this we need to model 푓(푥), between or beyond the known points, by some plausible functional form. It is relatively easy to find pathological functions that invalidate any given interpolation scheme, so there is no single ‘right’ answer to this problem. Approaches that are often used in practice involve modelling 푓(푥) using polynomials or rational functions (i.e. quotients of polynomials). Trigonometric functions, i.e. sines and cosines, can also be used, giving rise to so- called Fourier methods. The approach used can be ‘global’, in the sense that we fit to all points simultaneously (giving each in some sense ‘equal’ weight in the computation). More commonly, the approach adopted is ‘local’, in the sense that we give greater weight in the curve fitting to points ‘close’ to the value of 푥 in which we are interested.