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A Short Course on Approximation Theory
A Short Course on Approximation Theory N. L. Carothers Department of Mathematics and Statistics Bowling Green State University ii Preface These are notes for a topics course offered at Bowling Green State University on a variety of occasions. The course is typically offered during a somewhat abbreviated six week summer session and, consequently, there is a bit less material here than might be associated with a full semester course offered during the academic year. On the other hand, I have tried to make the notes self-contained by adding a number of short appendices and these might well be used to augment the course. The course title, approximation theory, covers a great deal of mathematical territory. In the present context, the focus is primarily on the approximation of real-valued continuous functions by some simpler class of functions, such as algebraic or trigonometric polynomials. Such issues have attracted the attention of thousands of mathematicians for at least two centuries now. We will have occasion to discuss both venerable and contemporary results, whose origins range anywhere from the dawn of time to the day before yesterday. This easily explains my interest in the subject. For me, reading these notes is like leafing through the family photo album: There are old friends, fondly remembered, fresh new faces, not yet familiar, and enough easily recognizable faces to make me feel right at home. The problems we will encounter are easy to state and easy to understand, and yet their solutions should prove intriguing to virtually anyone interested in mathematics. The techniques involved in these solutions entail nearly every topic covered in the standard undergraduate curriculum. -
Year XIX, Supplement Ethnographic Study And/Or a Theoretical Survey of a Their Position in the Article Should Be Clearly Indicated
III. TITLES OF ARTICLES DRU[TVO ANTROPOLOGOV SLOVENIJE The journal of the Slovene Anthropological Society Titles (in English and Slovene) must be short, informa- SLOVENE ANTHROPOLOGICAL SOCIETY Anthropological Notebooks welcomes the submis- tive, and understandable. The title should be followed sion of papers from the field of anthropology and by the name of the author(s), their position, institutional related disciplines. Submissions are considered for affiliation, and if possible, by e-mail address. publication on the understanding that the paper is not currently under consideration for publication IV. ABSTRACT AND KEYWORDS elsewhere. It is the responsibility of the author to The abstract must give concise information about the obtain permission for using any previously published objective, the method used, the results obtained, and material. Please submit your manuscript as an e-mail the conclusions. Authors are asked to enclose in English attachment on [email protected] and enclose your contact information: name, position, and Slovene an abstract of 100 – 200 words followed institutional affiliation, address, phone number, and by three to five keywords. They must reflect the field of e-mail address. research covered in the article. English abstract should be placed at the beginning of an article and the Slovene one after the references at the end. V. NOTES A N T H R O P O L O G I C A L INSTRUCTIONS Notes should also be double-spaced and used sparingly. They must be numbered consecutively throughout the text and assembled at the end of the article just before references. VI. QUOTATIONS Short quotations (less than 30 words) should be placed in single quotation marks with double marks for quotations within quotations. -
Approximation Atkinson Chapter 4, Dahlquist & Bjork Section 4.5
Approximation Atkinson Chapter 4, Dahlquist & Bjork Section 4.5, Trefethen's book Topics marked with ∗ are not on the exam 1 In approximation theory we want to find a function p(x) that is `close' to another function f(x). We can define closeness using any metric or norm, e.g. Z 2 2 kf(x) − p(x)k2 = (f(x) − p(x)) dx or kf(x) − p(x)k1 = sup jf(x) − p(x)j or Z kf(x) − p(x)k1 = jf(x) − p(x)jdx: In order for these norms to make sense we need to restrict the functions f and p to suitable function spaces. The polynomial approximation problem takes the form: Find a polynomial of degree at most n that minimizes the norm of the error. Naturally we will consider (i) whether a solution exists and is unique, (ii) whether the approximation converges as n ! 1. In our section on approximation (loosely following Atkinson, Chapter 4), we will first focus on approximation in the infinity norm, then in the 2 norm and related norms. 2 Existence for optimal polynomial approximation. Theorem (no reference): For every n ≥ 0 and f 2 C([a; b]) there is a polynomial of degree ≤ n that minimizes kf(x) − p(x)k where k · k is some norm on C([a; b]). Proof: To show that a minimum/minimizer exists, we want to find some compact subset of the set of polynomials of degree ≤ n (which is a finite-dimensional space) and show that the inf over this subset is less than the inf over everything else. -
Fundamental Theorems in Mathematics
SOME FUNDAMENTAL THEOREMS IN MATHEMATICS OLIVER KNILL Abstract. An expository hitchhikers guide to some theorems in mathematics. Criteria for the current list of 243 theorems are whether the result can be formulated elegantly, whether it is beautiful or useful and whether it could serve as a guide [6] without leading to panic. The order is not a ranking but ordered along a time-line when things were writ- ten down. Since [556] stated “a mathematical theorem only becomes beautiful if presented as a crown jewel within a context" we try sometimes to give some context. Of course, any such list of theorems is a matter of personal preferences, taste and limitations. The num- ber of theorems is arbitrary, the initial obvious goal was 42 but that number got eventually surpassed as it is hard to stop, once started. As a compensation, there are 42 “tweetable" theorems with included proofs. More comments on the choice of the theorems is included in an epilogue. For literature on general mathematics, see [193, 189, 29, 235, 254, 619, 412, 138], for history [217, 625, 376, 73, 46, 208, 379, 365, 690, 113, 618, 79, 259, 341], for popular, beautiful or elegant things [12, 529, 201, 182, 17, 672, 673, 44, 204, 190, 245, 446, 616, 303, 201, 2, 127, 146, 128, 502, 261, 172]. For comprehensive overviews in large parts of math- ematics, [74, 165, 166, 51, 593] or predictions on developments [47]. For reflections about mathematics in general [145, 455, 45, 306, 439, 99, 561]. Encyclopedic source examples are [188, 705, 670, 102, 192, 152, 221, 191, 111, 635]. -
Function Approximation Through an Efficient Neural Networks Method
Paper ID #25637 Function Approximation through an Efficient Neural Networks Method Dr. Chaomin Luo, Mississippi State University Dr. Chaomin Luo received his Ph.D. in Department of Electrical and Computer Engineering at Univer- sity of Waterloo, in 2008, his M.Sc. in Engineering Systems and Computing at University of Guelph, Canada, and his B.Eng. in Electrical Engineering from Southeast University, Nanjing, China. He is cur- rently Associate Professor in the Department of Electrical and Computer Engineering, at the Mississippi State University (MSU). He was panelist in the Department of Defense, USA, 2015-2016, 2016-2017 NDSEG Fellowship program and panelist in 2017 NSF GRFP Panelist program. He was the General Co-Chair of 2015 IEEE International Workshop on Computational Intelligence in Smart Technologies, and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies, Cleveland, OH. Currently, he is Associate Editor of International Journal of Robotics and Automation, and Interna- tional Journal of Swarm Intelligence Research. He was the Publicity Chair in 2011 IEEE International Conference on Automation and Logistics. He was on the Conference Committee in 2012 International Conference on Information and Automation and International Symposium on Biomedical Engineering and Publicity Chair in 2012 IEEE International Conference on Automation and Logistics. He was a Chair of IEEE SEM - Computational Intelligence Chapter; a Vice Chair of IEEE SEM- Robotics and Automa- tion and Chair of Education Committee of IEEE SEM. He has extensively published in reputed journal and conference proceedings, such as IEEE Transactions on Neural Networks, IEEE Transactions on SMC, IEEE-ICRA, and IEEE-IROS, etc. His research interests include engineering education, computational intelligence, intelligent systems and control, robotics and autonomous systems, and applied artificial in- telligence and machine learning for autonomous systems. -
A Rational Approximation to the Logarithm
A Rational Approximation to the Logarithm By R. P. Kelisky and T. J. Rivlin We shall use the r-method of Lanczos to obtain rational approximations to the logarithm which converge in the entire complex plane slit along the nonpositive real axis, uniformly in certain closed regions in the slit plane. We also provide error esti- mates valid in these regions. 1. Suppose z is a complex number, and s(l, z) denotes the closed line segment joining 1 and z. Let y be a positive real number, y ¿¿ 1. As a polynomial approxima- tion, of degree n, to log x on s(l, y) we propose p* G Pn, Pn being the set of poly- nomials with real coefficients of degree at most n, which minimizes ||xp'(x) — X|J===== max \xp'(x) — 1\ *e«(l.v) among all p G Pn which satisfy p(l) = 0. (This procedure is suggested by the observation that w = log x satisfies xto'(x) — 1=0, to(l) = 0.) We determine p* as follows. Suppose p(x) = a0 + aix + ■• • + a„xn, p(l) = 0 and (1) xp'(x) - 1 = x(x) = b0 + bix +-h bnxn . Then (2) 6o = -1 , (3) a,- = bj/j, j = 1, ■•-,«, and n (4) a0= - E Oj • .-i Thus, minimizing \\xp' — 1|| subject to p(l) = 0 is equivalent to minimizing ||«-|| among all i£P„ which satisfy —ir(0) = 1. This, in turn, is equivalent to maximiz- ing |7r(0) I among all v G Pn which satisfy ||ir|| = 1, in the sense that if vo maximizes |7r(0)[ subject to ||x|| = 1, then** = —iro/-7ro(0) minimizes ||x|| subject to —ir(0) = 1. -
Study Kit Framework Template.Docx
STUDY KIT FRAMEWORK Title: Hudournik Topic: field work with students Key words: observing, orientation, tectonic fault, cell phone applications Subject: geography, biology Cross-curricular Topic: climate and biodiversity on the Vojsko plateau Level: Medium Age: 15-18 Number of students: 5-15 Duration in minutes: 70-90 Place (classroom, outdoor etc.): Outdoor Author: Ester Mrak School: Jurij Vega Grammar School Idrija Language: English, Slovenian Overview: Practical work in the field of geography and biology where participants learn about how various natural elements are interconnected. Objectives: Participants will learn by doing, explore an important geological site, use simple cell phone applications, observe the relief above the Idrijca, the Kanomljica and the Hotenja rivers, understand the connection between endogenic and exogenous forces and relief shapes in the region, explain the connection between relief shapes and… - population density in the region, - vegetation, - river’s network name several plants, growing on the Vojsko plateau, analyze the connection between altitude and vegetation. Learning material and tools: Working sheet, cell phone, maps, text, vegetation book Preparation: Activity participants should download the required applications on their cell phones, read the geological text about the Idrija fault, understand the basic geological & geographical terms such as geological time scale, fault, tectonic plates, relief, limestone. This publication was supported by the Erasmus+ Programme of the European Commission. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained herein. Evaluation: Participants make a terminological dictionary, containing new terms from the fields of geography and biology. -
Topics in High-Dimensional Approximation Theory
TOPICS IN HIGH-DIMENSIONAL APPROXIMATION THEORY DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University By Yeonjong Shin Graduate Program in Mathematics The Ohio State University 2018 Dissertation Committee: Dongbin Xiu, Advisor Ching-Shan Chou Chuan Xue c Copyright by Yeonjong Shin 2018 ABSTRACT Several topics in high-dimensional approximation theory are discussed. The fundamental problem in approximation theory is to approximate an unknown function, called the target function by using its data/samples/observations. Depending on different scenarios on the data collection, different approximation techniques need to be sought in order to utilize the data properly. This dissertation is concerned with four different approximation methods in four different scenarios in data. First, suppose the data collecting procedure is resource intensive, which may require expensive numerical simulations or experiments. As the performance of the approximation highly depends on the data set, one needs to carefully decide where to collect data. We thus developed a method of designing a quasi-optimal point set which guides us where to collect the data, before the actual data collection procedure. We showed that the resulting quasi-optimal set notably outperforms than other standard choices. Second, it is not always the case that we obtain the exact data. Suppose the data is corrupted by unexpected external errors. These unexpected corruption errors can have big magnitude and in most cases are non-random. We proved that a well-known classical method, the least absolute deviation (LAD), could effectively eliminate the corruption er- rors. -
Approximation of Π
Lund University/LTH Bachelor thesis FMAL01 Approximation of π Abstract The inverse tangent function can be used to approximate π. When approximating π the convergence of infinite series or products are of great importance for the accuracy of the digits. This thesis will present historical formulas used to approximate π, especially Leibniz–Gregory and Machin’s. Also, the unique Machin two-term formulas will be presented and discussed to show that Machin’s way of calculate π have had a great impact on calculating this number. Madeleine Jansson [email protected] Mentor: Frank Wikstr¨om Examinator: Tomas Persson June 13, 2019 Contents 1 Introduction 2 2 History 2 2.1 – 500 Anno Domini . .2 2.2 1500 – 1800 Anno Domini . .5 2.3 1800 – Anno Domini . .6 2.4 Proofs . .7 2.4.1 Vi`ete’sformula . .7 2.4.2 Wallis’s formula . .9 2.4.3 Irrationality of π ............................. 11 3 Mathematical background 13 3.1 Precision and accuracy . 13 3.2 Convergence of series . 14 3.3 Rounding . 15 4 Gregory–Leibniz formula 17 4.1 History . 18 4.2 Proof . 18 4.3 Convergence . 20 4.4 x = √1 ....................................... 21 3 5 Machin’s formula 22 5.1 History . 22 5.2 Proof . 23 5.3 Convergence . 24 6 Machin-like formulas 26 6.1 Machin-like formula . 27 6.2 Two-term formula . 27 6.3 Complex factors . 28 6.3.1 Euler’s formula . 30 6.3.2 Machin’s formula . 30 6.3.3 Hermann’s formula . 30 6.3.4 Hutton’s formula . -
On Complex Approximation
Pacific Journal of Mathematics ON COMPLEX APPROXIMATION LAWRENCE CARL EGGAN AND EUGENE A. MAIER Vol. 13, No. 2 April 1963 ON COMPLEX APPROXIMATION L. C. EGGAN and E. A. MAIBR 1. Let C denote the set of complex numbers and G the set of Gaussian integers. In this note we prove the following theorem which is a two-dimensional analogue of Theorem 2 in [3]. THEOREM 1. If β,yeC, then there exists ueG such that I β — u 1 < 2 and (27/82 As an illustration of the application of Theorem 1 to complex approximation, we use it to prove the following result. THEOREM 2. If θeC is irrational and aeC, a Φ mθ + n where m,neG, then there exist infinitely many pairs of relatively prime integers x,yeG such that I x(xθ -y - a)\< 1/2 . The method of proof of Theorem 2 is due to Niven [6]. Also in [7], Niven uses Theorem 1 to obtain a more general result concerning complex approximation by nonhomogeneous linear forms. Alternatively, Theorem 2 may be obtained as a consequence of a theorem of Hlawka [5]. This was done by Eggan [2] using Chalk's statement [1] of Hlawka's Theorem. 2 Theorem 1 may be restated in an equivalent form. For u,b,ce C, define g(u, b, c) = \u — (b + c)\\ u — (b — c)\ . Then Theorem 1 may be stated as follows. THEOREM 1'. If b, ce C, then there exist ulf u2eG such that (i) I uλ - (6 + c) \< 2, I u2 - (6 - c) |< 2 and for i = 1,2, 27/32 ίf (nr )\ g(Ui,( b,c)K ^ < \ί M<l/Π/32 It is clear that Theorem 1' implies Theorem 1 by taking Received February 14, 1962. -
Chapter 1. Finite Difference Approximations
“rjlfdm” 2007/4/10 i i page 3 i i Copyright ©2007 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. Chapter 1 Finite Difference Approximations Our goal is to approximate solutions to differential equations, i.e., to find a function (or some discrete approximation to this function) that satisfies a given relationship between various of its derivatives on some given region of space and/or time, along with some boundary conditions along the edges of this domain. In general this is a difficult problem, Q2 and only rarely can an analytic formula be found for the solution. A finite difference method proceeds by replacing the derivatives in the differential equations with finite difference approximations. This gives a large but finite algebraic system of equations to be solved in place of the differential equation, something that can be done on a computer. Before tackling this problem, we first consider the more basic question of how we can approximate the derivatives of a known function by finite difference formulas based only on values of the function itself at discrete points. Besides providing a basis for the later development of finite difference methods for solving differential equations, this allows us to investigate several key concepts such as the order of accuracy of an approximation in the simplest possible setting. Let u.x/ represent a function of one variable that, unless otherwise stated, will always be assumed to be smooth, meaning that we can differentiate the function several times and each derivative is a well-defined bounded function over an interval containing a particular point of interest xN. -
Chebyshev Approximation by Exponential-Polynomial Sums (*)
Chebyshev approximation by exponential-polynomial sums (*) Charles B. Dunham (**) ABSTRACT It is shown that best Chebyshev approximations by exponential-polynomial sums are character- ized by (a variable number of) alternations of their error curve and are unique. Computation of best approximations via the Remez algorithm and Barrodale approach is considered. Let [a, 3] be a closed finite interval and C[a, 3] be are distinct and nonzero (in the case m = 0 we will the space of continuous functions on [a, 3]. For permit one of a n + 1 ..... a2n to be zero). It is easily g ~ C [a, 3] define seen that any parameter has an equivalent standard parameter. The degeneracy d (A) of a standard parameter Itgll=max (Ig(x)l:a<x<3). A is the number of zero coefficients in a I ..... a n. The Let w be a positive element of C[a, 3], an ordinary degree of F at standard parameter A is defined to be multiplicative weight function. For given fLxed n > 0, 2n + m - d (A). m >/ 0 define n m k-1 Haar subspaces F (A, x) = k~=l a k exp (an + k x) + k Z=I a2n + kX Definition The Chebyshev problem is given f~ C[a, 3] to find a A linear subspace of C [a, 3] of dimension ~ is a Haar parameter A* to minimize IIw(f-F(A, .))II. Such a subspace on [a, 3] if only the zero element has ~ zeros parameter A* is called best and F(A*, .) is called a on [a, 31.