Introduction to Maxima
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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. -
Ordinary Differential Equations (ODE) Using Euler's Technique And
Mathematical Models and Methods in Modern Science Ordinary Differential Equations (ODE) using Euler’s Technique and SCILAB Programming ZULZAMRI SALLEH Applied Sciences and Advance Technology Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Bandar Teknologi Maritim Jalan Pantai Remis 32200 Lumut, Perak MALAYSIA [email protected] http://www.mimet.edu.my Abstract: - Mathematics is very important for the engineering and scientist but to make understand the mathematics is very difficult if without proper tools and suitable measurement. A numerical method is one of the algorithms which involved with computer programming. In this paper, Scilab is used to carter the problems related the mathematical models such as Matrices, operation with ODE’s and solving the Integration. It remains true that solutions of the vast majority of first order initial value problems cannot be found by analytical means. Therefore, it is important to be able to approach the problem in other ways. Today there are numerous methods that produce numerical approximations to solutions of differential equations. Here, we introduce the oldest and simplest such method, originated by Euler about 1768. It is called the tangent line method or the Euler method. It uses a fixed step size h and generates the approximate solution. The purpose of this paper is to show the details of implementing of Euler’s method and made comparison between modify Euler’s and exact value by integration solution, as well as solve the ODE’s use built-in functions available in Scilab programming. Key-Words: - Numerical Methods, Scilab programming, Euler methods, Ordinary Differential Equation. 1 Introduction availability of digital computers has led to a Numerical methods are techniques by which veritable explosion in the use and development of mathematical problems are formulated so that they numerical methods [1]. -
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. -
Flow Routing Techniques for Environmental Modeling
EPA/600/B-18/256 | August 2018 | www.epa.gov/research Flow Routing Techniques for Environmental Modeling photo 0 EPA/600/B-18/256 August 2018 Flow Routing Techniques for Environmental Modeling by Jan Sitterson1 , Chris Knightes2 , Brian Avant3 1 Oak Ridge Associated Universities 2 U.S. Environmental Protection Agency, Office of Research and Development 3 Oak Ridge Institute for Science and Education Chris Knightes - Project Officer Office of Research and Development National Exposure Research Laboratory Athens, GA, 30605 1 Notice The U.S. Environmental Protection Agency (EPA) through its Office of Research and Development funded and managed the research described here. The research described herein was conducted at the Computational Exposure Division of the U.S. Environmental Protection Agency National Exposure Research Laboratory in Athens, GA. Any mention of trade names, products, or services does not imply an endorsement by the U.S. Government or the U.S. Environmental Protection Agency. The EPA does not endorse any commercial products, services, or enterprises. This document has been reviewed by the U.S. Environmental Protection Agency, Office of Research and Development, and approved for publication. 2 Abstract This report describes a few ways to simulate the movement of water through a network of streams. Flow routing connects excess water from precipitation and runoff to the stream to other surface water as part of the hydrological cycle. Simulating flow helps elucidate the transportation of nutrients through a stream system, predict flood events, inform decision makers, and regulate water quality and quantity issues. Three flow routing techniques are presented and discussed in this paper. -
Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder
Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder September 11, 2018 Prof. Michael Paul Prediction Functions Remember: a prediction function is the function that predicts what the output should be, given the input. Prediction Functions Linear regression: f(x) = wTx + b Linear classification (perceptron): f(x) = 1, wTx + b ≥ 0 -1, wTx + b < 0 Need to learn what w should be! Learning Parameters Goal is to learn to minimize error • Ideally: true error • Instead: training error The loss function gives the training error when using parameters w, denoted L(w). • Also called cost function • More general: objective function (in general objective could be to minimize or maximize; with loss/cost functions, we want to minimize) Learning Parameters Goal is to minimize loss function. How do we minimize a function? Let’s review some math. Rate of Change The slope of a line is also called the rate of change of the line. y = ½x + 1 “rise” “run” Rate of Change For nonlinear functions, the “rise over run” formula gives you the average rate of change between two points Average slope from x=-1 to x=0 is: 2 f(x) = x -1 Rate of Change There is also a concept of rate of change at individual points (rather than two points) Slope at x=-1 is: f(x) = x2 -2 Rate of Change The slope at a point is called the derivative at that point Intuition: f(x) = x2 Measure the slope between two points that are really close together Rate of Change The slope at a point is called the derivative at that point Intuition: Measure the -
Numerical Methods for Ordinary Differential Equations
CHAPTER 1 Numerical Methods for Ordinary Differential Equations In this chapter we discuss numerical method for ODE . We will discuss the two basic methods, Euler’s Method and Runge-Kutta Method. 1. Numerical Algorithm and Programming in Mathcad 1.1. Numerical Algorithm. If you look at dictionary, you will the following definition for algorithm, 1. a set of rules for solving a problem in a finite number of steps; 2. a sequence of steps designed for programming a computer to solve a specific problem. A numerical algorithm is a set of rules for solving a problem in finite number of steps that can be easily implemented in computer using any programming language. The following is an algorithm for compute the root of f(x) = 0; Input f, a, N and tol . Output: the approximate solution to f(x) = 0 with initial guess a or failure message. ² Step One: Set x = a ² Step Two: For i=0 to N do Step Three - Four f(x) Step Three: Compute x = x ¡ f 0(x) Step Four: If f(x) · tol return x ² Step Five return ”failure”. In analogy, a numerical algorithm is like a cook recipe that specify the input — cooking material, the output—the cooking product, and steps of carrying computation — cooking steps. In an algorithm, you will see loops (for, while), decision making statements(if, then, else(otherwise)) and return statements. ² for loop: Typically used when specific number of steps need to be carried out. You can break a for loop with return or break statement. 1 2 1. NUMERICAL METHODS FOR ORDINARY DIFFERENTIAL EQUATIONS ² while loop: Typically used when unknown number of steps need to be carried out. -
Getting Started with Euler
Getting started with Euler Samuel Fux High Performance Computing Group, Scientific IT Services, ETH Zurich ETH Zürich | Scientific IT Services | HPC Group Samuel Fux | 19.02.2020 | 1 Outlook . Introduction . Accessing the cluster . Data management . Environment/LMOD modules . Using the batch system . Applications . Getting help ETH Zürich | Scientific IT Services | HPC Group Samuel Fux | 19.02.2020 | 2 Outlook . Introduction . Accessing the cluster . Data management . Environment/LMOD modules . Using the batch system . Applications . Getting help ETH Zürich | Scientific IT Services | HPC Group Samuel Fux | 19.02.2020 | 3 Intro > What is EULER? . EULER stands for . Erweiterbarer, Umweltfreundlicher, Leistungsfähiger ETH Rechner . It is the 5th central (shared) cluster of ETH . 1999–2007 Asgard ➔ decommissioned . 2004–2008 Hreidar ➔ integrated into Brutus . 2005–2008 Gonzales ➔ integrated into Brutus . 2007–2016 Brutus . 2014–2020+ Euler . It benefits from the 15 years of experience gained with those previous large clusters ETH Zürich | Scientific IT Services | HPC Group Samuel Fux | 19.02.2020 | 4 Intro > Shareholder model . Like its predecessors, Euler has been financed (for the most part) by its users . So far, over 100 (!) research groups from almost all departments of ETH have invested in Euler . These so-called “shareholders” receive a share of the cluster’s resources (processors, memory, storage) proportional to their investment . The small share of Euler financed by IT Services is open to all members of ETH . The only requirement -
Freemat V3.6 Documentation
FreeMat v3.6 Documentation Samit Basu November 16, 2008 2 Contents 1 Introduction and Getting Started 5 1.1 INSTALL Installing FreeMat . 5 1.1.1 General Instructions . 5 1.1.2 Linux . 5 1.1.3 Windows . 6 1.1.4 Mac OS X . 6 1.1.5 Source Code . 6 2 Variables and Arrays 7 2.1 CELL Cell Array Definitions . 7 2.1.1 Usage . 7 2.1.2 Examples . 7 2.2 Function Handles . 8 2.2.1 Usage . 8 2.3 GLOBAL Global Variables . 8 2.3.1 Usage . 8 2.3.2 Example . 9 2.4 INDEXING Indexing Expressions . 9 2.4.1 Usage . 9 2.4.2 Array Indexing . 9 2.4.3 Cell Indexing . 13 2.4.4 Structure Indexing . 14 2.4.5 Complex Indexing . 16 2.5 MATRIX Matrix Definitions . 17 2.5.1 Usage . 17 2.5.2 Examples . 17 2.6 PERSISTENT Persistent Variables . 19 2.6.1 Usage . 19 2.6.2 Example . 19 2.7 STRING String Arrays . 20 2.7.1 Usage . 20 2.8 STRUCT Structure Array Constructor . 22 2.8.1 Usage . 22 2.8.2 Example . 22 3 4 CONTENTS 3 Functions and Scripts 25 3.1 ANONYMOUS Anonymous Functions . 25 3.1.1 Usage . 25 3.1.2 Examples . 25 3.2 FUNCTION Function Declarations . 26 3.2.1 Usage . 26 3.2.2 Examples . 28 3.3 KEYWORDS Function Keywords . 30 3.3.1 Usage . 30 3.3.2 Example . 31 3.4 NARGIN Number of Input Arguments . 32 3.4.1 Usage . -
Integration and Differential Equations
R.S. Johnson Integration and differential equations Download free eBooks at bookboon.com 2 Integration and differential equations © 2012 R.S. Johnson & Ventus Publishing ApS ISBN 978-87-7681-970-5 Download free eBooks at bookboon.com 3 Integration and differential equations Contents Contents Preface to these two texts 8 Part I An introduction to the standard methods of elementary integration 9 List of Integrals 10 Preface 11 1 Introduction and Background 12 1.1 The Riemann integral 12 1.2 The fundamental theorem of calculus 17 1.4 Theorems on integration 19 1.5 Standard integrals 23 1.6 Integration by parts 26 1.7 Improper integrals 32 1.8 Non-uniqueness of representation 35 Exercises 1 36 2 The integration of rational functions 38 2.1 Improper fractions 38 2.2 Linear denominator, Q(x) 39 The next step for top-performing graduates Masters in Management Designed for high-achieving graduates across all disciplines, London Business School’s Masters in Management provides specific and tangible foundations for a successful career in business. This 12-month, full-time programme is a business qualification with impact. In 2010, our MiM employment rate was 95% within 3 months of graduation*; the majority of graduates choosing to work in consulting or financial services. As well as a renowned qualification from a world-class business school, you also gain access to the School’s network of more than 34,000 global alumni – a community that offers support and opportunities throughout your career. For more information visit www.london.edu/mm, email [email protected] or give us a call on +44 (0)20 7000 7573. -
Maxima and Minima
Basic Mathematics Maxima and Minima R Horan & M Lavelle The aim of this document is to provide a short, self assessment programme for students who wish to be able to use differentiation to find maxima and mininima of functions. Copyright c 2004 [email protected] , [email protected] Last Revision Date: May 5, 2005 Version 1.0 Table of Contents 1. Rules of Differentiation 2. Derivatives of order 2 (and higher) 3. Maxima and Minima 4. Quiz on Max and Min Solutions to Exercises Solutions to Quizzes Section 1: Rules of Differentiation 3 1. Rules of Differentiation Throughout this package the following rules of differentiation will be assumed. (In the table of derivatives below, a is an arbitrary, non-zero constant.) y axn sin(ax) cos(ax) eax ln(ax) dy 1 naxn−1 a cos(ax) −a sin(ax) aeax dx x If a is any constant and u, v are two functions of x, then d du dv (u + v) = + dx dx dx d du (au) = a dx dx Section 1: Rules of Differentiation 4 The stationary points of a function are those points where the gradient of the tangent (the derivative of the function) is zero. Example 1 Find the stationary points of the functions (a) f(x) = 3x2 + 2x − 9 , (b) f(x) = x3 − 6x2 + 9x − 2 . Solution dy (a) If y = 3x2 + 2x − 9 then = 3 × 2x2−1 + 2 = 6x + 2. dx The stationary points are found by solving the equation dy = 6x + 2 = 0 . dx In this case there is only one solution, x = −1/3. -
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 -
Calculus 120, Section 7.3 Maxima & Minima of Multivariable Functions
Calculus 120, section 7.3 Maxima & Minima of Multivariable Functions notes by Tim Pilachowski A quick note to start: If you’re at all unsure about the material from 7.1 and 7.2, now is the time to go back to review it, and get some help if necessary. You’ll need all of it for the next three sections. Just like we had a first-derivative test and a second-derivative test to maxima and minima of functions of one variable, we’ll use versions of first partial and second partial derivatives to determine maxima and minima of functions of more than one variable. The first derivative test for functions of more than one variable looks very much like the first derivative test we have already used: If f(x, y, z) has a relative maximum or minimum at a point ( a, b, c) then all partial derivatives will equal 0 at that point. That is ∂f ∂f ∂f ()a b,, c = 0 ()a b,, c = 0 ()a b,, c = 0 ∂x ∂y ∂z Example A: Find the possible values of x, y, and z at which (xf y,, z) = x2 + 2y3 + 3z 2 + 4x − 6y + 9 has a relative maximum or minimum. Answers : (–2, –1, 0); (–2, 1, 0) Example B: Find the possible values of x and y at which fxy(),= x2 + 4 xyy + 2 − 12 y has a relative maximum or minimum. Answer : (4, –2); z = 12 Notice that the example above asked for possible values. The first derivative test by itself is inconclusive. The second derivative test for functions of more than one variable is a good bit more complicated than the one used for functions of one variable.