Approximation of Π
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Ch. 15 Power Series, Taylor Series
Ch. 15 Power Series, Taylor Series 서울대학교 조선해양공학과 서유택 2017.12 ※ 본 강의 자료는 이규열, 장범선, 노명일 교수님께서 만드신 자료를 바탕으로 일부 편집한 것입니다. Seoul National 1 Univ. 15.1 Sequences (수열), Series (급수), Convergence Tests (수렴판정) Sequences: Obtained by assigning to each positive integer n a number zn z . Term: zn z1, z 2, or z 1, z 2 , or briefly zn N . Real sequence (실수열): Sequence whose terms are real Convergence . Convergent sequence (수렴수열): Sequence that has a limit c limznn c or simply z c n . For every ε > 0, we can find N such that Convergent complex sequence |zn c | for all n N → all terms zn with n > N lie in the open disk of radius ε and center c. Divergent sequence (발산수열): Sequence that does not converge. Seoul National 2 Univ. 15.1 Sequences, Series, Convergence Tests Convergence . Convergent sequence: Sequence that has a limit c Ex. 1 Convergent and Divergent Sequences iin 11 Sequence i , , , , is convergent with limit 0. n 2 3 4 limznn c or simply z c n Sequence i n i , 1, i, 1, is divergent. n Sequence {zn} with zn = (1 + i ) is divergent. Seoul National 3 Univ. 15.1 Sequences, Series, Convergence Tests Theorem 1 Sequences of the Real and the Imaginary Parts . A sequence z1, z2, z3, … of complex numbers zn = xn + iyn converges to c = a + ib . if and only if the sequence of the real parts x1, x2, … converges to a . and the sequence of the imaginary parts y1, y2, … converges to b. Ex. -
Arxiv:1207.1472V2 [Math.CV]
SOME SIMPLIFICATIONS IN THE PRESENTATIONS OF COMPLEX POWER SERIES AND UNORDERED SUMS OSWALDO RIO BRANCO DE OLIVEIRA Abstract. This text provides very easy and short proofs of some basic prop- erties of complex power series (addition, subtraction, multiplication, division, rearrangement, composition, differentiation, uniqueness, Taylor’s series, Prin- ciple of Identity, Principle of Isolated Zeros, and Binomial Series). This is done by simplifying the usual presentation of unordered sums of a (countable) family of complex numbers. All the proofs avoid formal power series, double series, iterated series, partial series, asymptotic arguments, complex integra- tion theory, and uniform continuity. The use of function continuity as well as epsilons and deltas is kept to a mininum. Mathematics Subject Classification: 30B10, 40B05, 40C15, 40-01, 97I30, 97I80 Key words and phrases: Power Series, Multiple Sequences, Series, Summability, Complex Analysis, Functions of a Complex Variable. Contents 1. Introduction 1 2. Preliminaries 2 3. Absolutely Convergent Series and Commutativity 3 4. Unordered Countable Sums and Commutativity 5 5. Unordered Countable Sums and Associativity. 9 6. Sum of a Double Sequence and The Cauchy Product 10 7. Power Series - Algebraic Properties 11 8. Power Series - Analytic Properties 14 References 17 arXiv:1207.1472v2 [math.CV] 27 Jul 2012 1. Introduction The objective of this work is to provide a simplification of the theory of un- ordered sums of a family of complex numbers (in particular, for a countable family of complex numbers) as well as very easy proofs of basic operations and properties concerning complex power series, such as addition, scalar multiplication, multipli- cation, division, rearrangement, composition, differentiation (see Apostol [2] and Vyborny [21]), Taylor’s formula, principle of isolated zeros, uniqueness, principle of identity, and binomial series. -
INFINITE SERIES in P-ADIC FIELDS
INFINITE SERIES IN p-ADIC FIELDS KEITH CONRAD 1. Introduction One of the major topics in a course on real analysis is the representation of functions as power series X n anx ; n≥0 where the coefficients an are real numbers and the variable x belongs to R. In these notes we will develop the theory of power series over complete nonarchimedean fields. Let K be a field that is complete with respect to a nontrivial nonarchimedean absolute value j · j, such as Qp with its absolute value j · jp. We will look at power series over K (that is, with coefficients in K) and a variable x coming from K. Such series share some properties in common with real power series: (1) There is a radius of convergence R (a nonnegative real number, or 1), for which there is convergence at x 2 K when jxj < R and not when jxj > R. (2) A power series is uniformly continuous on each closed disc (of positive radius) where it converges. (3) A power series can be differentiated termwise in its disc of convergence. There are also some contrasts: (1) The convergence of a power series at its radius of convergence R does not exhibit mixed behavior: it is either convergent at all x with jxj = R or not convergent at P n all x with jxj = R, unlike n≥1 x =n on R, where R = 1 and there is convergence at x = −1 but not at x = 1. (2) A power series can be expanded around a new point in its disc of convergence and the new series has exactly the same disc of convergence as the original series. -
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. -
Series: Convergence and Divergence Comparison Tests
Series: Convergence and Divergence Here is a compilation of what we have done so far (up to the end of October) in terms of convergence and divergence. • Series that we know about: P∞ n Geometric Series: A geometric series is a series of the form n=0 ar . The series converges if |r| < 1 and 1 a1 diverges otherwise . If |r| < 1, the sum of the entire series is 1−r where a is the first term of the series and r is the common ratio. P∞ 1 2 p-Series Test: The series n=1 np converges if p1 and diverges otherwise . P∞ • Nth Term Test for Divergence: If limn→∞ an 6= 0, then the series n=1 an diverges. Note: If limn→∞ an = 0 we know nothing. It is possible that the series converges but it is possible that the series diverges. Comparison Tests: P∞ • Direct Comparison Test: If a series n=1 an has all positive terms, and all of its terms are eventually bigger than those in a series that is known to be divergent, then it is also divergent. The reverse is also true–if all the terms are eventually smaller than those of some convergent series, then the series is convergent. P P P That is, if an, bn and cn are all series with positive terms and an ≤ bn ≤ cn for all n sufficiently large, then P P if cn converges, then bn does as well P P if an diverges, then bn does as well. (This is a good test to use with rational functions. -
Divergent and Conditionally Convergent Series Whose Product Is Absolutely Convergent
DIVERGENTAND CONDITIONALLY CONVERGENT SERIES WHOSEPRODUCT IS ABSOLUTELYCONVERGENT* BY FLORIAN CAJOKI § 1. Introduction. It has been shown by Abel that, if the product : 71—« I(¥» + V«-i + ■•• + M„«o)> 71=0 of two conditionally convergent series : 71—0 71— 0 is convergent, it converges to the product of their sums. Tests of the conver- gence of the product of conditionally convergent series have been worked out by A. Pringsheim,| A. Voss,J and myself.§ There exist certain conditionally- convergent series which yield a convergent result when they are raised to a cer- tain positive integral power, but which yield a divergent result when they are raised to a higher power. Thus, 71 = «3 Z(-l)"+13, 7>=i n where r = 7/9, is a conditionally convergent series whose fourth power is con- vergent, but whose fifth power is divergent. || These instances of conditionally * Presented to the Society April 28, 1900. Received for publication April 28, 1900. fMathematische Annalen, vol. 21 (1883), p. 327 ; vol. 2(5 (1886), p. 157. %Mathematische Annalen, vol. 24 (1884), p. 42. § American Journal of Mathematics, vol. 15 (1893), p. 339 ; vol. 18 (1896), p. 195 ; Bulletin of the American Mathematical Society, (2) vol. 1 (1895), p. 180. || This may be a convenient place to point out a slight and obvious extension of the results which I have published in the American Journal of Mathematics, vol. 18, p. 201. It was proved there that the conditionally convergent series : V(_l)M-lI (0<r5|>). Sí n when raised by Cauchy's multiplication rule to a positive integral power g , is convergent whenever (î — l)/ï <C r ! out the power of the series is divergent, if (q— 1 )¡q > r. -
Dirichlet’S Test
4. CONDITIONALLY CONVERGENT SERIES 23 4. Conditionally convergent series Here basic tests capable of detecting conditional convergence are studied. 4.1. Dirichlet’s test. Theorem 4.1. (Dirichlet’s test) n Let a complex sequence {An}, An = k=1 ak, be bounded, and the real sequence {bn} is decreasing monotonically, b1 ≥ b2 ≥ b3 ≥··· , so that P limn→∞ bn = 0. Then the series anbn converges. A proof is based on the identity:P m m m m−1 anbn = (An − An−1)bn = Anbn − Anbn+1 n=k n=k n=k n=k−1 X X X X m−1 = An(bn − bn+1)+ Akbk − Am−1bm n=k X Since {An} is bounded, there is a number M such that |An|≤ M for all n. Therefore it follows from the above identity and non-negativity and monotonicity of bn that m m−1 anbn ≤ An(bn − bn+1) + |Akbk| + |Am−1bm| n=k n=k X X m−1 ≤ M (bn − bn+1)+ bk + bm n=k ! X ≤ 2Mbk By the hypothesis, bk → 0 as k → ∞. Therefore the right side of the above inequality can be made arbitrary small for all sufficiently large k and m ≥ k. By the Cauchy criterion the series in question converges. This completes the proof. Example. By the root test, the series ∞ zn n n=1 X converges absolutely in the disk |z| < 1 in the complex plane. Let us investigate the convergence on the boundary of the disk. Put z = eiθ 24 1. THE THEORY OF CONVERGENCE ikθ so that ak = e and bn = 1/n → 0 monotonically as n →∞. -
Chapter 3: Infinite Series
Chapter 3: Infinite series Introduction Definition: Let (an : n ∈ N) be a sequence. For each n ∈ N, we define the nth partial sum of (an : n ∈ N) to be: sn := a1 + a2 + . an. By the series generated by (an : n ∈ N) we mean the sequence (sn : n ∈ N) of partial sums of (an : n ∈ N) (ie: a series is a sequence). We say that the series (sn : n ∈ N) generated by a sequence (an : n ∈ N) is convergent if the sequence (sn : n ∈ N) of partial sums converges. If the series (sn : n ∈ N) is convergent then we call its limit the sum of the series and we write: P∞ lim sn = an. In detail: n→∞ n=1 s1 = a1 s2 = a1 + a + 2 = s1 + a2 s3 = a + 1 + a + 2 + a3 = s2 + a3 ... = ... sn = a + 1 + ... + an = sn−1 + an. Definition: A series that is not convergent is called divergent, ie: each series is ei- P∞ ther convergent or divergent. It has become traditional to use the notation n=1 an to represent both the series (sn : n ∈ N) generated by the sequence (an : n ∈ N) and the sum limn→∞ sn. However, this ambiguity in the notation should not lead to any confu- sion, provided that it is always made clear that the convergence of the series must be established. Important: Although traditionally series have been (and still are) represented by expres- P∞ sions such as n=1 an they are, technically, sequences of partial sums. So if somebody comes up to you in a supermarket and asks you what a series is - you answer “it is a P∞ sequence of partial sums” not “it is an expression of the form: n=1 an.” n−1 Example: (1) For 0 < r < ∞, we may define the sequence (an : n ∈ N) by, an := r for each n ∈ N. -
February 2009
How Euler Did It by Ed Sandifer Estimating π February 2009 On Friday, June 7, 1779, Leonhard Euler sent a paper [E705] to the regular twice-weekly meeting of the St. Petersburg Academy. Euler, blind and disillusioned with the corruption of Domaschneff, the President of the Academy, seldom attended the meetings himself, so he sent one of his assistants, Nicolas Fuss, to read the paper to the ten members of the Academy who attended the meeting. The paper bore the cumbersome title "Investigatio quarundam serierum quae ad rationem peripheriae circuli ad diametrum vero proxime definiendam maxime sunt accommodatae" (Investigation of certain series which are designed to approximate the true ratio of the circumference of a circle to its diameter very closely." Up to this point, Euler had shown relatively little interest in the value of π, though he had standardized its notation, using the symbol π to denote the ratio of a circumference to a diameter consistently since 1736, and he found π in a great many places outside circles. In a paper he wrote in 1737, [E74] Euler surveyed the history of calculating the value of π. He mentioned Archimedes, Machin, de Lagny, Leibniz and Sharp. The main result in E74 was to discover a number of arctangent identities along the lines of ! 1 1 1 = 4 arctan " arctan + arctan 4 5 70 99 and to propose using the Taylor series expansion for the arctangent function, which converges fairly rapidly for small values, to approximate π. Euler also spent some time in that paper finding ways to approximate the logarithms of trigonometric functions, important at the time in navigation tables. -
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. -
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.