Differentiation and Integration in Sage Integrals Numerical Integration

Total Page:16

File Type:pdf, Size:1020Kb

Differentiation and Integration in Sage Integrals Numerical Integration MAT 305: Mathematical Computing John Perry Limits Differentiation Explicit differentiation MAT 305: Mathematical Computing Implicit differentiation Integration Differentiation and Integration in Sage Integrals Numerical integration Summary John Perry University of Southern Mississippi Fall 2011 MAT 305: Mathematical Computing Outline John Perry Limits Differentiation 1 Limits Explicit differentiation Implicit differentiation Integration Integrals 2 Differentiation Numerical integration Explicit differentiation Summary Implicit differentiation 3 Integration Integrals Numerical integration 4 Summary MAT 305: Mathematical Computing Maxima, Sympy, GSL John Perry Limits Maxima: symbolic Calculus Differentiation Explicit differentiation Storied history Implicit differentiation • Integration Written in LISP Integrals • Numerical integration Sometimes buggy in non-obvious ways • Summary Sympy: symbolic Calculus Far more recent • Written in Python • Often slower than Maxima • Eventual replacement for Maxima? • GSL: numerical Calculus GNU Scientific Library • MAT 305: Mathematical Computing Outline John Perry Limits Differentiation 1 Limits Explicit differentiation Implicit differentiation Integration Integrals 2 Differentiation Numerical integration Explicit differentiation Summary Implicit differentiation 3 Integration Integrals Numerical integration 4 Summary MAT 305: Mathematical Computing The limit() command John Perry Limits Differentiation Explicit differentiation Implicit differentiation Integration limit( f (x), x=a, direction) where Integrals Numerical integration f (x) is a function in x • Summary a • 2 R direction is optional, but if used has form • dir=’left’ or • dir=’right’ • MAT 305: Mathematical Computing Examples John Perry Limits sage: limit(x**2-1,x=4) Differentiation Explicit differentiation 15 Implicit differentiation Integration Integrals sage: limit(x/abs(x),x=0) Numerical integration und (Translation: “undefined”) Summary sage: limit(x/abs(x),x=0,dir=’right’) 1 sage: limit(x/abs(x),x=0,dir=’left’) -1 sage: limit(sin(1/x),x=0) ind (Translation: “indeterminate, but bounded”) MAT 305: Mathematical Computing Examples with infinite limits John Perry Limits sage: limit(e**(-x),x=infinity) Differentiation Explicit differentiation 0 Implicit differentiation Integration Integrals sage: limit((1+1/x)**x,x=infinity) Numerical integration e (An indeterminate form!) Summary sage: limit((3*x**2-1)/(2*x**2+cos(x)),x=infinity) 3/2 sage: limit(ln(x)/x,x=infinity) 0 (Another indeterminate form!) sage: limit(x/ln(x),x=infinity) +Infinity MAT 305: Mathematical Computing Outline John Perry Limits Differentiation 1 Limits Explicit differentiation Implicit differentiation Integration Integrals 2 Differentiation Numerical integration Explicit differentiation Summary Implicit differentiation 3 Integration Integrals Numerical integration 4 Summary MAT 305: Mathematical Computing The diff() command John Perry Limits Differentiation Explicit differentiation Implicit differentiation Integration diff( f (x), x, n) where Integrals Numerical integration f (x) is a function of x • Summary differentiate f with respect to x • “semi-optional”: mandatory if f has other variables • e.g., partial differentiation, or unknown constants • (optional) compute the nth derivative of f (x) • MAT 305: Mathematical Computing Examples John Perry Limits Differentiation sage: diff(e**x) Explicit differentiation e^x Implicit differentiation Integration sage: diff(x**10, 5) Integrals 30240*x^5 Numerical integration Summary sage: diff(sin(x), 99) -cos(x) sage: var(’y’) y sage: diff(x**2+y**2-1) . output cut. ValueError: No differentiation variable specified. sage: diff(x**2+y**2-1, x) 2*x MAT 305: Mathematical Computing 1 picture = 1000 words John Perry Limits sage: f(x) = sin(x) Differentiation Explicit differentiation Implicit differentiation sage: df(x) = diff(f) Integration Integrals Numerical integration sage: m0 = df(0) Summary sage: p0 = point((0,f(0)),pointsize=30) sage: fplot = plot(f,xmin=-pi/2,xmax=pi/2, thickness=2) sage: tan_line = plot(m0*(x-0)+f(0),xmin=-pi/2, xmax=pi/2,rgbcolor=(1,0,0)) sage: show(p0+fplot+tan_line,aspect_ratio=1) MAT 305: Mathematical Computing 1 picture = 1000 words John Perry Limits Differentiation 1.5 Explicit differentiation Implicit differentiation Integration 1 Integrals Numerical integration Summary 0.5 -1.5 -1 -0.5 0.5 1 1.5 -0.5 -1 -1.5 MAT 305: Mathematical Computing The implicit_diff() command John Perry Limits Differentiation Explicit differentiation Implicit differentiation There is no implicit_diff() command. To differentiate Integration implicitly, Integrals Numerical integration define yvar as a variable using the var() command; Summary • define yf as an implicit function of x using the function() • command; move everything to one side (as in implicit plots); • differentiate the non-zero side of the equation; and • solve() for diff(yf) • MAT 305: Mathematical Computing Example John Perry Limits sage: y = function(’y’,x) Differentiation sage:y Explicit differentiation Implicit differentiation y(x) (. so y is recognized as a function of x) Integration sage: diff(y) Integrals dy Numerical integration D[0](y)(x) (Sage’s dx ) Summary sage: expr = x**2 + y**2 - 1 sage: diff(expr) 2*y(x)*D[0](y)(x) + 2*x sage: deriv = diff(expr) sage: solve(deriv,diff(y)) [D[0](y)(x) == -x/y(x)] x . that is, y0 (x) = . − y MAT 305: Mathematical Computing Use computer memory, not yours John Perry Limits Differentiation Explicit differentiation Implicit differentiation Integration sage: y = function(’y’,x) Integrals Numerical integration Summary sage: yprime = diff(y) sage: deriv = diff(x**2+y**2-1) sage: solve(deriv,yprime) [D[0](y)(x) == -x/y(x)] MAT 305: Mathematical Computing 1 picture = 1000 words John Perry Limits sage: yvar = var(’y’) Differentiation Explicit differentiation sage: yf = function(’y’,x) Implicit differentiation sage: yprime = diff(yf) Integration Integrals sage: f = yf**2 - x**3 + x Numerical integration sage: df = diff(f) Summary sage: solve(df,yprime) [D[0](y)(x) == 1/2*(3*x^2 - 1)/y(x)] sage: mx(x,yvar) = 1/2*(3*x**2 - 1)/yvar sage: fplot = implicit_plot(yvar**2-x**3+x, (x,-2,2),(yvar,-2,2),color=’red’) sage: tan_line = plot(mx(-0.5,sqrt(3/8)) *(x+0.5)+sqrt(3/8), xmin=-2,xmax=2) sage: show(fplot+tan_line,aspect_ratio=1) MAT 305: Mathematical Computing 1 picture = 1000 words John Perry Limits 2 Differentiation Explicit differentiation 1.5 Implicit differentiation Integration Integrals 1 Numerical integration Summary 0.5 -0 -0.5 -1 -1.5 -2 -2 -1.5 -1 -0.5 -0 0.5 1 1.5 2 MAT 305: Mathematical Computing Outline John Perry Limits Differentiation 1 Limits Explicit differentiation Implicit differentiation Integration Integrals 2 Differentiation Numerical integration Explicit differentiation Summary Implicit differentiation 3 Integration Integrals Numerical integration 4 Summary MAT 305: Mathematical Computing The integral() command John Perry Limits Differentiation Explicit differentiation Implicit differentiation Integration Integrals Numerical integration integral( f (x), x, xmin, xmax) where Summary f (x) is a function of the (optional) variable x • (optional) xmin and xmax are limits of integration • MAT 305: Mathematical Computing Example John Perry Limits Differentiation Explicit differentiation sage: integral(x**2) Implicit differentiation 1/3*x^3 Integration Integrals Numerical integration sage: integral(x**2,x,0,1) Summary 1/3 sage: integral(1/x,x,1,infinity) . output cut. ValueError: Integral is divergent. sage: integral(1/x**2,x,1,infinity) 1 MAT 305: Mathematical Computing Beware the Jabberwock. John Perry Limits Differentiation Explicit differentiation Implicit differentiation Integration sage: integral(1/x**3,x,1,infinity) Integrals Numerical integration . output cut. Summary ValueError: Integral is divergent. (What the—? a Maxima bug!) (This error should not occur in Sage after version 4.1.1) MAT 305: Mathematical Computing His vorpal sword in hand. John Perry Limits Differentiation Explicit differentiation Implicit differentiation Integration Integrals Numerical integration Fortunately, Sympy works great for this integral: Summary sage: integral(1/x**3,1,infinity, algorithm=’sympy’) 1/2 (Correct answer!) MAT 305: Mathematical Computing Snicker snack! John Perry Limits Differentiation Explicit differentiation Maxima bug confirmed Implicit differentiation Integration Integrals http://trac.sagemath.org/sage_trac/ticket/6420 Numerical integration Summary Maxima 5.13.0 was correct • in older versions of Sage • Bug introduced in Maxima 5.16.3 • Sage 4.0.2–4.1.1 • Bug fixed in Maxima 5.18.1 • Sage 4.1.2 Maxima 5.19 • − MAT 305: Mathematical ? Computing 1 picture = 1000 words John Perry sage: fplot = plot(sin(x),xmin=0,xmax=pi, Limits fill=’axis’) Differentiation Explicit differentiation Implicit differentiation sage: farea = integral(sin(x),x,0,pi) Integration Integrals sage: areatext = text(farea,(pi/2,0.5),fontsize=40) Numerical integration Summary sage: fplot+areatext 1 0.75 0.5 2 0.25 1 2 3 MAT 305: Mathematical Computing Numerical integration: Review John Perry Limits Differentiation Explicit differentiation Not all integrals can be simplified into elementary functions Implicit differentiation Integration Integrals Numerical integration Summary MAT 305: Mathematical Computing Numerical integration: Review John Perry Limits Differentiation Explicit differentiation Not all integrals can be simplified into elementary functions Implicit differentiation Integration Integrals Numerical integration Example Summary 2
Recommended publications
  • Plotting Direction Fields in Matlab and Maxima – a Short Tutorial
    Plotting direction fields in Matlab and Maxima – a short tutorial Luis Carvalho Introduction A first order differential equation can be expressed as dx x0(t) = = f(t; x) (1) dt where t is the independent variable and x is the dependent variable (on t). Note that the equation above is in normal form. The difficulty in solving equation (1) depends clearly on f(t; x). One way to gain geometric insight on how the solution might look like is to observe that any solution to (1) should be such that the slope at any point P (t0; x0) is f(t0; x0), since this is the value of the derivative at P . With this motivation in mind, if you select enough points and plot the slopes in each of these points, you will obtain a direction field for ODE (1). However, it is not easy to plot such a direction field for any function f(t; x), and so we must resort to some computational help. There are many computational packages to help us in this task. This is a short tutorial on how to plot direction fields for first order ODE’s in Matlab and Maxima. Matlab Matlab is a powerful “computing environment that combines numeric computation, advanced graphics and visualization” 1. A nice package for plotting direction field in Matlab (although resourceful, Matlab does not provide such facility natively) can be found at http://math.rice.edu/»dfield, contributed by John C. Polking from the Dept. of Mathematics at Rice University. To install this add-on, simply browse to the files for your version of Matlab and download them.
    [Show full text]
  • CAS (Computer Algebra System) Mathematica
    CAS (Computer Algebra System) Mathematica- UML students can download a copy for free as part of the UML site license; see the course website for details From: Wikipedia 2/9/2014 A computer algebra system (CAS) is a software program that allows [one] to compute with mathematical expressions in a way which is similar to the traditional handwritten computations of the mathematicians and other scientists. The main ones are Axiom, Magma, Maple, Mathematica and Sage (the latter includes several computer algebras systems, such as Macsyma and SymPy). Computer algebra systems began to appear in the 1960s, and evolved out of two quite different sources—the requirements of theoretical physicists and research into artificial intelligence. A prime example for the first development was the pioneering work conducted by the later Nobel Prize laureate in physics Martin Veltman, who designed a program for symbolic mathematics, especially High Energy Physics, called Schoonschip (Dutch for "clean ship") in 1963. Using LISP as the programming basis, Carl Engelman created MATHLAB in 1964 at MITRE within an artificial intelligence research environment. Later MATHLAB was made available to users on PDP-6 and PDP-10 Systems running TOPS-10 or TENEX in universities. Today it can still be used on SIMH-Emulations of the PDP-10. MATHLAB ("mathematical laboratory") should not be confused with MATLAB ("matrix laboratory") which is a system for numerical computation built 15 years later at the University of New Mexico, accidentally named rather similarly. The first popular computer algebra systems were muMATH, Reduce, Derive (based on muMATH), and Macsyma; a popular copyleft version of Macsyma called Maxima is actively being maintained.
    [Show full text]
  • Sage Tutorial (Pdf)
    Sage Tutorial Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Introduction 3 1.1 Installation................................................4 1.2 Ways to Use Sage.............................................4 1.3 Longterm Goals for Sage.........................................5 2 A Guided Tour 7 2.1 Assignment, Equality, and Arithmetic..................................7 2.2 Getting Help...............................................9 2.3 Functions, Indentation, and Counting.................................. 10 2.4 Basic Algebra and Calculus....................................... 14 2.5 Plotting.................................................. 20 2.6 Some Common Issues with Functions.................................. 23 2.7 Basic Rings................................................ 26 2.8 Linear Algebra.............................................. 28 2.9 Polynomials............................................... 32 2.10 Parents, Conversion and Coercion.................................... 36 2.11 Finite Groups, Abelian Groups...................................... 42 2.12 Number Theory............................................. 43 2.13 Some More Advanced Mathematics................................... 46 3 The Interactive Shell 55 3.1 Your Sage Session............................................ 55 3.2 Logging Input and Output........................................ 57 3.3 Paste Ignores Prompts.......................................... 58 3.4 Timing Commands............................................ 58 3.5 Other IPython
    [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]
  • Programming for Computations – Python
    15 Svein Linge · Hans Petter Langtangen Programming for Computations – Python Editorial Board T. J.Barth M.Griebel D.E.Keyes R.M.Nieminen D.Roose T.Schlick Texts in Computational 15 Science and Engineering Editors Timothy J. Barth Michael Griebel David E. Keyes Risto M. Nieminen Dirk Roose Tamar Schlick More information about this series at http://www.springer.com/series/5151 Svein Linge Hans Petter Langtangen Programming for Computations – Python A Gentle Introduction to Numerical Simulations with Python Svein Linge Hans Petter Langtangen Department of Process, Energy and Simula Research Laboratory Environmental Technology Lysaker, Norway University College of Southeast Norway Porsgrunn, Norway On leave from: Department of Informatics University of Oslo Oslo, Norway ISSN 1611-0994 Texts in Computational Science and Engineering ISBN 978-3-319-32427-2 ISBN 978-3-319-32428-9 (eBook) DOI 10.1007/978-3-319-32428-9 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2016945368 Mathematic Subject Classification (2010): 26-01, 34A05, 34A30, 34A34, 39-01, 40-01, 65D15, 65D25, 65D30, 68-01, 68N01, 68N19, 68N30, 70-01, 92D25, 97-04, 97U50 © The Editor(s) (if applicable) and the Author(s) 2016 This book is published open access. Open Access This book is distributed under the terms of the Creative Commons Attribution-Non- Commercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.
    [Show full text]
  • They Have Very Good Docs At
    Intro to the Julia programming language Brendan O’Connor CMU, Dec 2013 They have very good docs at: http://julialang.org/ I’m borrowing some slides from: http://julialang.org/blog/2013/03/julia-tutorial-MIT/ 1 Tuesday, December 17, 13 Julia • A relatively new, open-source numeric programming language that’s both convenient and fast • Version 0.2. Still in flux, especially libraries. But the basics are very usable. • Lots of development momentum 2 Tuesday, December 17, 13 Why Julia? Dynamic languages are extremely popular for numerical work: ‣ Matlab, R, NumPy/SciPy, Mathematica, etc. ‣ very simple to learn and easy to do research in However, all have a “split language” approach: ‣ high-level dynamic language for scripting low-level operations ‣ C/C++/Fortran for implementing fast low-level operations Libraries in C — no productivity boost for library writers Forces vectorization — sometimes a scalar loop is just better slide from ?? 2012 3 Bezanson, Karpinski, Shah, Edelman Tuesday, December 17, 13 “Gang of Forty” Matlab Maple Mathematica SciPy SciLab IDL R Octave S-PLUS SAS J APL Maxima Mathcad Axiom Sage Lush Ch LabView O-Matrix PV-WAVE Igor Pro OriginLab FreeMat Yorick GAUSS MuPad Genius SciRuby Ox Stata JLab Magma Euler Rlab Speakeasy GDL Nickle gretl ana Torch7 slide from March 2013 4 Bezanson, Karpinski, Shah, Edelman Tuesday, December 17, 13 Numeric programming environments Core properties Dynamic Fast? and math-y? C/C++/ Fortran/Java − + Matlab + − Num/SciPy + − R + − Older table: http://brenocon.com/blog/2009/02/comparison-of-data-analysis-packages-r-matlab-scipy-excel-sas-spss-stata/ Tuesday, December 17, 13 - Dynamic vs Fast: the usual tradeof - PL quality: more subjective.
    [Show full text]
  • Teaching Dynamical Systems with Maxima 1. Mechanics For
    125 Teaching Dynamical Systems with Maxima Jaime E. Viliate University of Porto, Faculty of Engineering Rua Dr. Roberto Frias, Porto 42OO-~65, Portugal, villatec~fe.up.pt WWW home page: http://viliate.org/ We report on our experience in teaching a dynamical systems course to second year engineering students. The course has been taught with a hands-on ap proach, using a Computer Algebra System: Maxima. We have developed some additional programs to help students explore the dynamics of the systems, in a more intuitiva way and without losing too much time with computer program ming. The result has been very encouraging; students are better motivated using this approach, as compared to the more traditional methods that we used in the past. 1. Mechanics for Engineering students Our engineering students in the University of Porto have to take two courses in Physics during their first. or second year of studies. The first course is a course on Newtonian mechanics. When they attend that course, they have already taken one or two semesters of calculus, and during their high-school studies they must have already studied particle kinematics and dynamics. The main goal of the first course in physics is then to solve the equations of motion for some mechanical systems; for example, for a one-dimensional system the equations of motion are: (1) .F(x,v) (2) where x is the position, v the speed, m the mass and F is the total external force, which might depend on x and v. In general, equations 1 and 2 lead to a non-linear system.
    [Show full text]
  • Maxima by Example: Ch. 1, Getting Started ∗
    Maxima by Example: Ch. 1, Getting Started ¤ Edwin L. Woollett Contents 1 Getting Started 2 1.1 Introduction . 2 1.1.1 What is Maxima? . 2 1.2 Using the XMaxima Interface . 2 1.2.1 Using Console - Command Line Maxima . 3 1.2.2 Text Editors . 3 1.2.3 The Maxima Manual . 3 1.3 Expressions in Maxima . 4 1.3.1 Operators in Maxima . 4 1.3.2 Numbers in Maxima . 5 1.3.3 Variables in Maxima . 6 1.3.4 Constants in Maxima . 7 1.3.5 Rational Expressions in Maxima, rat(...) . 8 1.4 List Creation and Manipulation . 9 1.4.1 The Maxima List and the Lisp List . 10 1.4.2 cons, endcons, append . 10 1.4.3 makelist and map . 11 1.4.4 Anonymous Unnamed Function lamda . 14 1.4.5 Accessing List Elements Using part(...) or Bracket Pairs . 15 1.4.6 Creating a List of Fractional Values . 15 1.5 Data Files: Read, Write, Fit, and Plot . 15 1.5.1 le search . 15 1.5.2 le search maxima . 16 1.5.3 maxima-init.mac and maxima userdir . 17 1.5.4 Viewing a File with printle . 18 1.5.5 Creating a Data File using with stdout . 18 1.5.6 Creating a List from a Data File: read nested list . 18 1.5.7 Write List to Data File with write data . 19 1.5.8 Using Random Numbers to Create Noisy Data . 19 1.5.9 Using lsquares estimates for a Fit . 21 1.5.10 Nested List $ Matrix using apply and substpart .
    [Show full text]
  • CAS Maxima Workbook
    CAS Maxima Workbook Roland Salz January 7, 2021 Vers. 0.3.6 This work is published under the terms of the Creative Commons (CC) BY-NC-ND 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. As an exception to the above stated terms, the Maxima team is free to incorporate any material from this work into the official Maxima manual, and to remix, transform, or build upon this material within the official Maxima manual, to be published under its own license terms. No attribution is required in this case. Copyright © Roland Salz 2018-2021 No warranty whatsoever is given for the correctness or completeness of the information provided. Maple, Mathematica, Windows, and YouTube are registered trademarks. This project is work in progress. Comments and suggestions for improvement are welcome. Roland Salz Braunsberger Str. 26 D-44809 Bochum [email protected] i In some cases the objective is clear, and the results are surprising. Richard J. Fateman ii Preface Maxima was developed from 1968-1982 at MIT (Massachusetts Institute of Tech- nology) as the first comprehensive computer algebra system (CAS).
    [Show full text]
  • 1 of 3 10/23/2010 02:45 PM
    http://mathomatic.org/quickref.txt ****************************************************************************** Mathomatic Command Summary -------------------------- approximate - Approximate all numerical values in equation spaces. Usage: approximate [equation-number-ranges] calculate - Temporarily plug in values for variables and approximate. Usage: calculate ["factor"] [variable number-of-iterations] This command may be preceded with "repeat". clear - Delete expressions stored in memory so equation spaces can be reused. Usage: clear [equation-number-ranges] Tip: Use "clear all" to quickly restart Mathomatic. code - Output C, Java, or Python code for the specified equations. Usage: code ["c" or "java" or "python" or "integer"] [equation-number-ranges] Related commands: simplify, optimize, and variables compare - Compare two equation spaces to see if mathematically the same. Usage: compare ["symbolic"] equation-number ["with" equation-number] copy - Duplicate the contents of the specified equation spaces. Usage: copy [equation-number-range] derivative - Symbolically differentiate and simplify, order times. Usage: derivative ["nosimplify"] [variable or "all"] [order] Alternate name for this command: differentiate display - Display equation spaces in pretty multi-line (2D) fraction format. Usage: display ["factor"] [equation-number-ranges] divide - Prompt for 2 numbers or polynomials and divide. Give result and GCD. Usage: divide [variable] This command may be preceded with "repeat". echo - Output a line of text, followed by a newline. Usage: echo [text] edit - Edit all equation spaces or an input file, then read them in. Usage: edit [file-name] eliminate - Substitute the specified variables with solved equations. Usage: eliminate variables or "all" ["using" equation-number] This command may be preceded with "repeat". extrema - Show where the slope of the current equation equals zero. Usage: extrema [variable] [order] factor - Factor variables in equation spaces or factor given integers.
    [Show full text]
  • A Short Introduction to Sagemath
    A short introduction to SageMath Éric Gourgoulhon Laboratoire Univers et Théories (LUTH) CNRS / Observatoire de Paris / Université Paris Diderot Université Paris Sciences et Lettres 92190 Meudon, France http://luth.obspm.fr/~luthier/gourgoulhon/leshouches18/ École de Physique des Houches 11 July 2018 Éric Gourgoulhon SageMath Les Houches, 11 July 2018 1 / 13 Pynac, Maxima, SymPy: symbolic calculations GAP: group theory PARI/GP: number theory Singular: polynomial computations matplotlib: high quality figures Jupyter: graphical interface (notebook) and provides a uniform interface to them William Stein (Univ. of Washington) created SageMath in 2005; since then, ∼100 developers (mostly mathematicians) have joined the SageMath team SageMath is now supported by European Union via the open-math project OpenDreamKit (2015-2019, within the Horizon 2020 program) it is based on the Python programming language it makes use of many pre-existing open-sources packages, among which The mission Create a viable free open source alternative to Magma, Maple, Mathematica and Matlab. SageMath in a few words SageMath( nickname: Sage) is a free open-source mathematics software system Éric Gourgoulhon SageMath Les Houches, 11 July 2018 2 / 13 Pynac, Maxima, SymPy: symbolic calculations GAP: group theory PARI/GP: number theory Singular: polynomial computations matplotlib: high quality figures Jupyter: graphical interface (notebook) and provides a uniform interface to them William Stein (Univ. of Washington) created SageMath in 2005; since then, ∼100 developers (mostly mathematicians) have joined the SageMath team SageMath is now supported by European Union via the open-math project OpenDreamKit (2015-2019, within the Horizon 2020 program) it makes use of many pre-existing open-sources packages, among which The mission Create a viable free open source alternative to Magma, Maple, Mathematica and Matlab.
    [Show full text]
  • Solving Differential Equations Using Simulink by Russell Herman Is Licensed Under a Creative Com- Mons Attribution-Noncommercial-Share Alike 3.0 United States License
    R.HERMAN SOLVINGDIFFERENTIALEQUATIONS USINGSIMULINK R. L. HERMAN - VERSION DATE: JULY 1, 2019 Copyright © 2019 by R. Herman published by r. l. herman This text has been reformatted from the original using a modification of the Tufte-book documentclass in LATEX. See tufte-latex.googlecode.com. solving differential equations using simulink by Russell Herman is licensed under a Creative Com- mons Attribution-Noncommercial-Share Alike 3.0 United States License. These notes have resided at http://people.uncw.edu/hermanr/mat361/simulink since Summer 2015. First printing, 2015 Contents 1 Introduction to Simulink 1 1 Solving an ODE . 1 2 Handling Time in First Order Differential Equations . 8 3 Working with Simulink Output . 13 4 Printing Simulink Scope Images . 14 5 Scilab and Xcos . 19 6 First Order ODEs in MATLAB . 21 Symbolic Solutions . 22 ODE45 and Other Solvers. 23 Direction Fields . 23 7 Exercises . 26 2 First Order Differential Equations 27 1 Exponential Growth and Decay . 27 2 Newton’s Law of Cooling . 29 3 Free Fall with Drag . 33 4 Pursuit Curves . 35 5 The Logistic Equation . 38 6 The Logistic Equation with Delay . 39 7 Exercises . 41 3 Second Order Differential Equations 43 1 Constant Coefficient Equations . 44 Harmonic Oscillation . 45 2 Projectile Motion . 54 3 The Bouncing Ball . 56 4 Nonlinear Pendulum Animation . 59 5 Second Order ODEs in MATLAB . 62 6 Exercises . 66 4 Transfer Functions and State Space Blocks 69 1 State Space Formulation . 69 2 Transfer Functions . 70 4 5 Systems of Differential Equations 73 1 Linear Systems . 73 2 Nonlinear Models . 75 6 Index 83 1 Introduction to Simulink There are several computer packages for finding solutions of dif- Most of these models were created ferential equations, such as Maple, Mathematica, Maxima, MATLAB, etc.
    [Show full text]