DOCSLIB.ORG

Generalization of Risch's Algorithm to Special Functions

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Generalization of Risch's Algorithm to Special Functions Generalization of Risch's Algorithm to Special Functions Clemens G. Raab (RISC) LHCPhenonet School Hagenberg, July 9, 2012 Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Overview 1 Introduction to symbolic integration 2 Relevant classes of functions and Risch's algorithm 3 Basics of differential fields 4 A generalization of Risch's algorithm 1 Introduction 2 Inside the algorithm 5 Application to definite integrals depending on parameters Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Introduction to symbolic integration Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Different approaches and structures Differential algebra: differential fields Holonomic systems: Ore algebras Rule-based: expressions, tables of transformation rules ... Symbolic integration Computer algebra 1 Model the functions by algebraic structures 2 Computations in the algebraic framework 3 Interpret result in terms of functions Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Holonomic systems: Ore algebras Rule-based: expressions, tables of transformation rules ... Symbolic integration Computer algebra 1 Model the functions by algebraic structures 2 Computations in the algebraic framework 3 Interpret result in terms of functions Different approaches and structures Differential algebra: differential fields Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Rule-based: expressions, tables of transformation rules ... Symbolic integration Computer algebra 1 Model the functions by algebraic structures 2 Computations in the algebraic framework 3 Interpret result in terms of functions Different approaches and structures Differential algebra: differential fields Holonomic systems: Ore algebras Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Symbolic integration Computer algebra 1 Model the functions by algebraic structures 2 Computations in the algebraic framework 3 Interpret result in terms of functions Different approaches and structures Differential algebra: differential fields Holonomic systems: Ore algebras Rule-based: expressions, tables of transformation rules ... Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Examples Z Li (x) − xLi (x) x ln(1 − x)2 3 2 dx = (Li (x) − Li (x)) + (1 − x)2 1 − x 3 2 2 Z 0 2 1 0 2 0 2 2 Ai (x) dx = 3 xAi (x) + 2Ai(x)Ai (x) − x Ai(x) Z 1 π Y (x) dx = ln n xJn(x)Yn(x) 2 Jn(x) Indefinite integration Antiderivatives Z f (x) dx = g(x) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Indefinite integration Antiderivatives Z f (x) dx = g(x) Examples Z Li (x) − xLi (x) x ln(1 − x)2 3 2 dx = (Li (x) − Li (x)) + (1 − x)2 1 − x 3 2 2 Z 0 2 1 0 2 0 2 2 Ai (x) dx = 3 xAi (x) + 2Ai(x)Ai (x) − x Ai(x) Z 1 π Y (x) dx = ln n xJn(x)Yn(x) 2 Jn(x) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Examples Z 1 zx x dx = Li2(z) 0 e − z Z 1 −sx Γ(a) e γ(a; x) dx = a 0 s(s + 1) n Z 1 1 i X 1 e−2nπix ln(sin( π x)) dx = − + 2 4n nπ 2k − 1 0 k=1 Definite integration Integrals depending on parameters Z b f (x; y) dx = g(y) a Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Definite integration Integrals depending on parameters Z b f (x; y) dx = g(y) a Examples Z 1 zx x dx = Li2(z) 0 e − z Z 1 −sx Γ(a) e γ(a; x) dx = a 0 s(s + 1) n Z 1 1 i X 1 e−2nπix ln(sin( π x)) dx = − + 2 4n nπ 2k − 1 0 k=1 Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions After integrating from 0 to 1 we obtain Z 1 Z 1 z −x 1 z f (z; x)dx − f (z + 1; x)dx = x e x=0 0 0 In other words, we proved zΓ(z) − Γ(z + 1) = 0 Example: Gamma function Z 1 Γ(z) := xz−1e−x dx for z > 0 0 | {z } =:f (z;x) We compute d zf (z; x) − f (z + 1; x) = xz e−x dx Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions In other words, we proved zΓ(z) − Γ(z + 1) = 0 Example: Gamma function Z 1 Γ(z) := xz−1e−x dx for z > 0 0 | {z } =:f (z;x) We compute d zf (z; x) − f (z + 1; x) = xz e−x dx After integrating from 0 to 1 we obtain Z 1 Z 1 z −x 1 z f (z; x)dx − f (z + 1; x)dx = x e x=0 0 0 Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Example: Gamma function Z 1 Γ(z) := xz−1e−x dx for z > 0 0 | {z } =:f (z;x) We compute d zf (z; x) − f (z + 1; x) = xz e−x dx After integrating from 0 to 1 we obtain Z 1 Z 1 z −x 1 z f (z; x)dx − f (z + 1; x)dx = x e x=0 0 0 In other words, we proved zΓ(z) − Γ(z + 1) = 0 Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions yields an ODE for Z b I (y) := f (x; y)dx a d c0(n)f (x; n) + ··· + cm(n)f (x; n + m) = dx g(x; n) yields a recurrence for Z b I (n) := f (x; n)dx a Linear Relations Integrals depending on one parameter @mf d c0(y)f (x; y) + ··· + cm(y) @y m (x; y) = dx g(x; y) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions d c0(n)f (x; n) + ··· + cm(n)f (x; n + m) = dx g(x; n) yields a recurrence for Z b I (n) := f (x; n)dx a Linear Relations Integrals depending on one parameter @mf d c0(y)f (x; y) + ··· + cm(y) @y m (x; y) = dx g(x; y) yields an ODE for Z b I (y) := f (x; y)dx a Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions yields a recurrence for Z b I (n) := f (x; n)dx a Linear Relations Integrals depending on one parameter @mf d c0(y)f (x; y) + ··· + cm(y) @y m (x; y) = dx g(x; y) yields an ODE for Z b I (y) := f (x; y)dx a d c0(n)f (x; n) + ··· + cm(n)f (x; n + m) = dx g(x; n) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions Linear Relations Integrals depending on one parameter @mf d c0(y)f (x; y) + ··· + cm(y) @y m (x; y) = dx g(x; y) yields an ODE for Z b I (y) := f (x; y)dx a d c0(n)f (x; n) + ··· + cm(n)f (x; n + m) = dx g(x; n) yields a recurrence for Z b I (n) := f (x; n)dx a Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions g(x) is a certificate for the relation It is easy to verify 0 c0f0(x) + ··· + cmfm(x) = g (x) and r = g(b) − g(a) 0 ;:::; fm(x) and c0;:::; cm const. w.r.t. x c0 0 + ··· + cmfm(x) Transfer this to a relation of corresponding integrals b b R R c0 f0(x)dx + ··· + cm fm(x)dx = g(b) − g(a) a a Certificate b b R R c0 f0(x)dx + ··· + cm fm(x)dx = r a a Parametric integration Compute linear relation of integrals Given f (x) , find g(x) s.t. f (x) = g 0(x) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions g(x) is a certificate for the relation It is easy to verify 0 c0f0(x) + ··· + cmfm(x) = g (x) and r = g(b) − g(a) and c0;:::; cm const. w.r.t. x c0 0 + ··· + cmfm(x) Transfer this to a relation of corresponding integrals b b R R c0 f0(x)dx + ··· + cm fm(x)dx = g(b) − g(a) a a Certificate b b R R c0 f0(x)dx + ··· + cm fm(x)dx = r a a Parametric integration Compute linear relation of integrals Given f 0(x);:::; fm(x), find g(x) s.t. f (x) = g 0(x) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions g(x) is a certificate for the relation It is easy to verify 0 c0f0(x) + ··· + cmfm(x) = g (x) and r = g(b) − g(a) c0 0 + ··· + cmfm(x) Transfer this to a relation of corresponding integrals b b R R c0 f0(x)dx + ··· + cm fm(x)dx = g(b) − g(a) a a Certificate b b R R c0 f0(x)dx + ··· + cm fm(x)dx = r a a Parametric integration Compute linear relation of integrals Given f 0(x);:::; fm(x), find g(x) and c0;:::; cm const. w.r.t. x s.t. f (x) = g 0(x) Clemens G. Raab (RISC) Generalization of Risch's Algorithm to Special Functions g(x) is a certificate for the relation It is easy to verify 0 c0f0(x) + ··· + cmfm(x) = g (x) and r = g(b) − g(a) Transfer this to a relation of corresponding integrals b b R R c0 f0(x)dx + ··· + cm fm(x)dx = g(b) − g(a) a a Certificate b b R R c0 f0(x)dx + ··· + cm fm(x)dx = r a a Parametric integration Compute linear relation of integrals Given f 0(x);:::; fm(x), find g(x) and c0;:::; cm const.
Load more

Recommended publications
  • ECE3040: Table of Contents
    ECE3040: Table of Contents Lecture 1: Introduction and Overview Instructor contact information Navigating the course web page What is meant by numerical methods? Example problems requiring numerical methods What is Matlab and why do we need it? Lecture 2: Matlab Basics I The Matlab environment Basic arithmetic calculations Command Window control & formatting Built-in constants & elementary functions Lecture 3: Matlab Basics II The assignment operator “=” for defining variables Creating and manipulating arrays Element-by-element array operations: The “.” Operator Vector generation with linspace function and “:” (colon) operator Graphing data and functions Lecture 4: Matlab Programming I Matlab scripts (programs) Input-output: The input and disp commands The fprintf command User-defined functions Passing functions to M-files: Anonymous functions Global variables Lecture 5: Matlab Programming II Making decisions: The if-else structure The error, return, and nargin commands Loops: for and while structures Interrupting loops: The continue and break commands Lecture 6: Programming Examples Plotting piecewise functions Computing the factorial of a number Beeping Looping vs vectorization speed: tic and toc commands Passing an “anonymous function” to Matlab function Approximation of definite integrals: Riemann sums Computing cos(푥) from its power series Stopping criteria for iterative numerical methods Computing the square root Evaluating polynomials Errors and Significant Digits Lecture 7: Polynomials Polynomials
    [Show full text]
  • Package 'Mosaiccalc'
    Package ‘mosaicCalc’ May 7, 2020 Type Package Title Function-Based Numerical and Symbolic Differentiation and Antidifferentiation Description Part of the Project MOSAIC (<http://mosaic-web.org/>) suite that provides utility functions for doing calculus (differentiation and integration) in R. The main differentiation and antidifferentiation operators are described using formulas and return functions rather than numerical values. Numerical values can be obtained by evaluating these functions. Version 0.5.1 Depends R (>= 3.0.0), mosaicCore Imports methods, stats, MASS, mosaic, ggformula, magrittr, rlang Suggests testthat, knitr, rmarkdown, mosaicData Author Daniel T. Kaplan <[email protected]>, Ran- dall Pruim <[email protected]>, Nicholas J. Horton <[email protected]> Maintainer Daniel Kaplan <[email protected]> VignetteBuilder knitr License GPL (>= 2) LazyLoad yes LazyData yes URL https://github.com/ProjectMOSAIC/mosaicCalc BugReports https://github.com/ProjectMOSAIC/mosaicCalc/issues RoxygenNote 7.0.2 Encoding UTF-8 NeedsCompilation no Repository CRAN Date/Publication 2020-05-07 13:00:13 UTC 1 2 D R topics documented: connector . .2 D..............................................2 findZeros . .4 fitSpline . .5 integrateODE . .5 numD............................................6 plotFun . .7 rfun .............................................7 smoother . .7 spliner . .7 Index 8 connector Create an interpolating function going through a set of points Description This is defined in the mosaic package: See connector. D Derivative and Anti-derivative operators Description Operators for computing derivatives and anti-derivatives as functions. Usage D(formula, ..., .hstep = NULL, add.h.control = FALSE) antiD(formula, ..., lower.bound = 0, force.numeric = FALSE) makeAntiDfun(.function, .wrt, from, .tol = .Machine$double.eps^0.25) numerical_integration(f, wrt, av, args, vi.from, ciName = "C", .tol) D 3 Arguments formula A formula.
    [Show full text]
  • Calculus 141, Section 8.5 Symbolic Integration Notes by Tim Pilachowski
    Calculus 141, section 8.5 Symbolic Integration notes by Tim Pilachowski Back in my day (mid-to-late 1970s for high school and college) we had no handheld calculators, and the only electronic computers were mainframes at universities and government facilities. After learning the integration techniques that you are now learning, we were sent to Tables of Integrals, which listed results for (often) hundreds of simple to complicated integration results. Some could be evaluated using the methods of Calculus 1 and 2, others needed more esoteric methods. It was necessary to scan the Tables to find the form that was needed. If it was there, great! If not… The UMCP Physics Department posts one from the textbook they use (current as of 2017) at www.physics.umd.edu/hep/drew/IntegralTable.pdf . A similar Table of Integrals from http://www.had2know.com/academics/table-of-integrals-antiderivative-formulas.html is appended at the end of this Lecture outline. x 1 x Example F from 8.1: Evaluate e sindxx using a Table of Integrals. Answer : e ()sin x − cos x + C ∫ 2 If you remember, we had to define I, do a series of two integrations by parts, then solve for “ I =”. From the Had2Know Table of Integrals below: Identify a = and b = , then plug those values in, and voila! Now, with the development of handheld calculators and personal computers, much more is available to us, including software that will do all the work. The text mentions Derive, Maple, Mathematica, and MATLAB, and gives examples of Mathematica commands. You’ll be using MATLAB in Math 241 and several other courses.
    [Show full text]
  • Symbolic and Numerical Integration in MATLAB 1 Symbolic Integration in MATLAB
    Symbolic and Numerical Integration in MATLAB 1 Symbolic Integration in MATLAB Certain functions can be symbolically integrated in MATLAB with the int command. Example 1. Find an antiderivative for the function f(x)= x2. We can do this in (at least) three different ways. The shortest is: >>int(’xˆ2’) ans = 1/3*xˆ3 Alternatively, we can define x symbolically first, and then leave off the single quotes in the int statement. >>syms x >>int(xˆ2) ans = 1/3*xˆ3 Finally, we can first define f as an inline function, and then integrate the inline function. >>syms x >>f=inline(’xˆ2’) f = Inline function: >>f(x) = xˆ2 >>int(f(x)) ans = 1/3*xˆ3 In certain calculations, it is useful to define the antiderivative as an inline function. Given that the preceding lines of code have already been typed, we can accomplish this with the following commands: >>intoff=int(f(x)) intoff = 1/3*xˆ3 >>intoff=inline(char(intoff)) intoff = Inline function: intoff(x) = 1/3*xˆ3 1 The inline function intoff(x) has now been defined as the antiderivative of f(x)= x2. The int command can also be used with limits of integration. △ Example 2. Evaluate the integral 2 x cos xdx. Z1 In this case, we will only use the first method from Example 1, though the other two methods will work as well. We have >>int(’x*cos(x)’,1,2) ans = cos(2)+2*sin(2)-cos(1)-sin(1) >>eval(ans) ans = 0.0207 Notice that since MATLAB is working symbolically here the answer it gives is in terms of the sine and cosine of 1 and 2 radians.
    [Show full text]
  • Integration Benchmarks for Computer Algebra Systems
    The Electronic Journal of Mathematics and Technology, Volume 2, Number 3, ISSN 1933-2823 Integration on Computer Algebra Systems Kevin Charlwood e-mail: [email protected] Washburn University Topeka, KS 66621 Abstract In this article, we consider ten indefinite integrals and the ability of three computer algebra systems (CAS) to evaluate them in closed-form, appealing only to the class of real, elementary functions. Although these systems have been widely available for many years and have undergone major enhancements in new versions, it is interesting to note that there are still indefinite integrals that escape the capacity of these systems to provide antiderivatives. When this occurs, we consider what a user may do to find a solution with the aid of a CAS. 1. Introduction We will explore the use of three CAS’s in the evaluation of indefinite integrals: Maple 11, Mathematica 6.0.2 and the Texas Instruments (TI) 89 Titanium graphics calculator. We consider integrals of real elementary functions of a single real variable in the examples that follow. Students often believe that a good CAS will enable them to solve any problem when there is a known solution; these examples are useful in helping instructors show their students that this is not always the case, even in a calculus course. A CAS may provide a solution, but in a form containing special functions unfamiliar to calculus students, or too cumbersome for students to use directly, [1]. Students may ask, “Why do we need to learn integration methods when our CAS will do all the exercises in the homework?” As instructors, we want our students to come away from their mathematics experience with some capacity to make intelligent use of a CAS when needed.
    [Show full text]
  • Sequences, Series and Taylor Approximation (Ma2712b, MA2730)
    Sequences, Series and Taylor Approximation (MA2712b, MA2730) Level 2 Teaching Team Current curator: Simon Shaw November 20, 2015 Contents 0 Introduction, Overview 6 1 Taylor Polynomials 10 1.1 Lecture 1: Taylor Polynomials, Definition . .. 10 1.1.1 Reminder from Level 1 about Differentiable Functions . .. 11 1.1.2 Definition of Taylor Polynomials . 11 1.2 Lectures 2 and 3: Taylor Polynomials, Examples . ... 13 x 1.2.1 Example: Compute and plot Tnf for f(x) = e ............ 13 1.2.2 Example: Find the Maclaurin polynomials of f(x) = sin x ...... 14 2 1.2.3 Find the Maclaurin polynomial T11f for f(x) = sin(x ) ....... 15 1.2.4 QuestionsforChapter6: ErrorEstimates . 15 1.3 Lecture 4 and 5: Calculus of Taylor Polynomials . .. 17 1.3.1 GeneralResults............................... 17 1.4 Lecture 6: Various Applications of Taylor Polynomials . ... 22 1.4.1 RelativeExtrema .............................. 22 1.4.2 Limits .................................... 24 1.4.3 How to Calculate Complicated Taylor Polynomials? . 26 1.5 ExerciseSheet1................................... 29 1.5.1 ExerciseSheet1a .............................. 29 1.5.2 FeedbackforSheet1a ........................... 33 2 Real Sequences 40 2.1 Lecture 7: Definitions, Limit of a Sequence . ... 40 2.1.1 DefinitionofaSequence .......................... 40 2.1.2 LimitofaSequence............................. 41 2.1.3 Graphic Representations of Sequences . .. 43 2.2 Lecture 8: Algebra of Limits, Special Sequences . ..... 44 2.2.1 InfiniteLimits................................ 44 1 2.2.2 AlgebraofLimits.............................. 44 2.2.3 Some Standard Convergent Sequences . .. 46 2.3 Lecture 9: Bounded and Monotone Sequences . ..... 48 2.3.1 BoundedSequences............................. 48 2.3.2 Convergent Sequences and Closed Bounded Intervals . .... 48 2.4 Lecture10:MonotoneSequences .
    [Show full text]
  • Multivariable Calculus with Maxima
    Multivariable Calculus with Maxima G. Jay Kerns December 1, 2009 The following is a short guide to multivariable calculus with Maxima. It loosely follows the treatment of Stewart’s Calculus, Seventh Edition. Refer there for definitions, theorems, proofs, explanations, and exercises. The simple goal of this guide is to demonstrate how to use Maxima to solve problems in that vein. This was originally written for the students in my third semester Calculus class, but once it grew past twenty pages I thought it might be of interest to a wider audience. Here it is. I am releasing this as a FREE document, and other people are free to build on this to make it better. The source for this document is located at http://people.ysu.edu/~gkerns/maxima/ It was inspired by Maxima by Example by Edwin Woollett, A Maxima Guide for Calculus Students by Moses Glasner, and Tutorial on Maxima by unknown. I also received help from the Maxima mailing list archives and volunteer responses to my questions. Thanks to all of those individuals. Contents 1 Getting Maxima 4 1.1 How to Install imaxima for Microsoft Windowsr ................ 4 1.1.1 Install the software ............................ 5 1.1.2 Configure your system .......................... 5 2 Three Dimensional Geometry 6 2.1 Vectors and Linear Algebra ........................... 6 2.2 Lines, Planes, and Quadric Surfaces ....................... 8 2.3 Vector Valued Functions ............................. 14 2.4 Arc Length and Curvature ............................ 19 3 Functions of Several Variables 20 3.1 Partial Derivatives ................................ 23 3.2 Linear Approximation and Differentials ..................... 24 3.3 Chain Rule and Implicit Differentiation ....................
    [Show full text]
  • Highlighting Wxmaxima in Calculus
    mathematics Article Not Another Computer Algebra System: Highlighting wxMaxima in Calculus Natanael Karjanto 1,* and Husty Serviana Husain 2 1 Department of Mathematics, University College, Natural Science Campus, Sungkyunkwan University Suwon 16419, Korea 2 Department of Mathematics Education, Faculty of Mathematics and Natural Science Education, Indonesia University of Education, Bandung 40154, Indonesia; [email protected] * Correspondence: [email protected] Abstract: This article introduces and explains a computer algebra system (CAS) wxMaxima for Calculus teaching and learning at the tertiary level. The didactic reasoning behind this approach is the need to implement an element of technology into classrooms to enhance students’ understanding of Calculus concepts. For many mathematics educators who have been using CAS, this material is of great interest, particularly for secondary teachers and university instructors who plan to introduce an alternative CAS into their classrooms. By highlighting both the strengths and limitations of the software, we hope that it will stimulate further debate not only among mathematics educators and software users but also also among symbolic computation and software developers. Keywords: computer algebra system; wxMaxima; Calculus; symbolic computation Citation: Karjanto, N.; Husain, H.S. 1. Introduction Not Another Computer Algebra A computer algebra system (CAS) is a program that can solve mathematical problems System: Highlighting wxMaxima in by rearranging formulas and finding a formula that solves the problem, as opposed to Calculus. Mathematics 2021, 9, 1317. just outputting the numerical value of the result. Maxima is a full-featured open-source https://doi.org/10.3390/ CAS: the software can serve as a calculator, provide analytical expressions, and perform math9121317 symbolic manipulations.
    [Show full text]
  • VSDITLU: a Verifiable Symbolic Definite Integral Table Look-Up
    VSDITLU: a verifiable symbolic definite integral table look-up A. A. Adams, H. Gottliebsen, S. A. Linton, and U. Martin Department of Computer Science, University of St Andrews, St Andrews KY16 9ES, Scotland {aaa,hago,sal,um}@cs.st-and.ac.uk Abstract. We present a verifiable symbolic definite integral table look-up: a sys- tem which matches a query, comprising a definite integral with parameters and side conditions, against an entry in a verifiable table and uses a call to a library of facts about the reals in the theorem prover PVS to aid in the transformation of the table entry into an answer. Our system is able to obtain correct answers in cases where standard techniques implemented in computer algebra systems fail. We present the full model of such a system as well as a description of our prototype implementation showing the efficacy of such a system: for example, the prototype is able to obtain correct answers in cases where computer algebra systems [CAS] do not. We extend upon Fateman’s web-based table by including parametric limits of integration and queries with side conditions. 1 Introduction In this paper we present a verifiable symbolic definite integral table look-up: a system which matches a query, comprising a definite integral with parameters and side condi- tions, against an entry in a verifiable table, and uses a call to a library of facts about the reals in the theorem prover PVS to aid in the transformation of the table entry into an answer. Our system is able to obtain correct answers in cases where standard tech- niques, such as those implemented in the computer algebra systems [CAS] Maple and Mathematica, do not.
    [Show full text]
  • The 30 Year Horizon
    The 30 Year Horizon Manuel Bronstein W illiam Burge T imothy Daly James Davenport Michael Dewar Martin Dunstan Albrecht F ortenbacher P atrizia Gianni Johannes Grabmeier Jocelyn Guidry Richard Jenks Larry Lambe Michael Monagan Scott Morrison W illiam Sit Jonathan Steinbach Robert Sutor Barry T rager Stephen W att Jim W en Clifton W illiamson Volume 10: Axiom Algebra: Theory i Portions Copyright (c) 2005 Timothy Daly The Blue Bayou image Copyright (c) 2004 Jocelyn Guidry Portions Copyright (c) 2004 Martin Dunstan Portions Copyright (c) 2007 Alfredo Portes Portions Copyright (c) 2007 Arthur Ralfs Portions Copyright (c) 2005 Timothy Daly Portions Copyright (c) 1991-2002, The Numerical ALgorithms Group Ltd. All rights reserved. This book and the Axiom software is licensed as follows: Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. - Neither the name of The Numerical ALgorithms Group Ltd. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS
    [Show full text]
  • Modeling and Simulation of Engineering Systems Laboratory #4
    ENGM 541 Laboratory #4 University of Alberta ENGM 541: Modeling and Simulation of Engineering Systems Laboratory #4 © M.G. Lipsett 2010 MATLAB is capable of solving sets of equations in a number of ways. This lab will explore a few of them for a small linear system. Picard’s Iteration Method and Taylor Series for Short Duration Simulation We’ve looked at two numerical methods for short duration integrations with respect to time. Each has a truncation error because of the simplification of the integration of the governing equations with respect to time by truncating the Taylor series. For a symbolic expression, M ATLAB can evaluate the first few terms of the Taylor Series using the command taylor in the Symbolic Math Toolbox (which is part of the network licence). Typing the following commands, >> syms x >> s = taylor(sin(x)) returns s = x-1/6*x^3+1/20*x^5 which we recognize to be the first three terms of the Taylor Series expansion of sin( x). Actually, the call to the function taylor with no additional arguments (optional parameters) returns the first five terms by default. In the case of sin( x), only three of the first five terms are non-zero. Picard’s Method is not a vendor-supplied routine; but there are third-parties who have developed m-file functions to calculate the integral numerically using Picard’s method. Exercise 1 Use ezplot to plot the symbolic expression s. How accurate does it appear to be near x=0? How accurate does it look farther away from x=0? Find the expression for the first twenty terms of the Taylor Series of sin( x), by typing >> s = taylor(sin(x),20) Use ezplot to plot the symbolic expression s again.
    [Show full text]
  • A Versatile Approach to Calculus and Numerical Methods
    A Versatile Approach to Calculus and Numerical Methods David Tall Mathematics Education Research Centre University of Warwick COVENTRY CV4 7AL U K Traditionally the calculus is the study of the symbolic algorithms for differentiation and integration, the relationship between them, and their use in solving problems. Only at the end of the course, when all else fails, are numerical methods introduced, such as the Newton-Raphson method of solving equations, or Simpson’s rule for calculating areas. The problem with such an approach is that it often produces students who are very well versed in the algorithms and can solve the most fiendish of symbolic problems, yet have no understanding of the meaning of what they are doing. Given the arrival of computer software which can carry out these algorithms mechanically, the question arises as to what parts of calculus need to be studied in the curriculum of the future. It is my contention that such a study can use the computer technology to produce a far more versatile approach to the subject, in which the numerical and graphical representations may be used from the outset to produce insights into the fundamental meanings, in which a wider understanding of the processes of change and growth will be possible than the narrow band of problems that can be solved by traditional symbolic methods of the calculus. The fundamental ideas are those of change, the rate of change, and the accumulation due to change. Symbolically these are represented by the function concept, the derivative and the integral. In the calculus curriculum of the future, we need to broaden our ideas of these three things to give a theory that applies not just to the beautiful abstraction that is traditional calculus, but also a theory that applies to the real world outside.
    [Show full text]