A Graphic Approach to Euler's Method
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Handout of Differential Equations
HANDOUT DIFFERENTIAL EQUATIONS For International Class Nikenasih Binatari NIP. 19841019 200812 2 005 Mathematics Educational Department Faculty of Mathematics and Natural Sciences State University of Yogyakarta 2010 CONTENTS Cover Contents I. Introduction to Differential Equations - Some basic mathematical models………………………………… 1 - Definitions and terminology……………………………………… 3 - Classification of Differential Equation……………………………. 7 - Initial Value Problems…………………………………………….. 9 - Boundary Value Problems………………………………………... 10 - Autonomous Equation……………………………………………. 11 - Definition of Differential Equation Solutions……………………... 16 II. First Order Equations for which exact solutions are obtainable - Standart forms of first order Differential Equation………………. 19 - Exact Equation…………………………………………………….. 20 - Solution of exact differential equations…………………………... 22 - Method of grouping………………………………………………. 25 - Integrating factor………………………………………………….. 26 - Separable differential equations…………………………………... 28 - Homogeneous differential equations……………………………... 31 - Linear differential Equations……………………………………… 34 - Bernoulli differential equations…………………………………… 36 - Special integrating factor………………………………………….. 39 - Special transformations…………………………………………… 40 III. Application of first order equations - Orthogonal trajectories…………………………………………... 43 - Oblique trajectories………………………………………………. 46 - Application in mechanics problems and rate problems………….. 48 IV. Explicit methods of solving higher order linear differential equations - Basic theory of linear differential -
Chapter 7. the Principle of Least Action
Chapter 7. The Principle of Least Action 7.1 Force Methods vs. Energy Methods We have so far studied two distinct ways of analyzing physics problems: force methods, basically consisting of the application of Newton’s Laws, and energy methods, consisting of the application of the principle of conservation of energy (the conservations of linear and angular momenta can also be considered as part of this). Both have their advantages and disadvantages. More precisely, energy methods often involve scalar quantities (e.g., work, kinetic energy, and potential energy) and are thus easier to handle than forces, which are vectorial in nature. However, forces tell us more. The simple example of a particle subjected to the earth’s gravitation field will clearly illustrate this. We know that when a particle moves from position y0 to y in the earth’s gravitational field, the conservation of energy tells us that ΔK + ΔUgrav = 0 (7.1) 1 2 2 m v − v0 + mg(y − y0 ) = 0, 2 ( ) which implies that the final speed of the particle, of mass m , is 2 v = v0 + 2g(y0 − y). (7.2) Although we know the final speed v of the particle, given its initial speed v0 and the initial and final positions y0 and y , we do not know how its position and velocity (and its speed) evolve with time. On the other hand, if we apply Newton’s Second Law, then we write −mgey = maey dv (7.3) = m e . dt y This equation is easily manipulated to yield t v = dv ∫t0 t = − gdτ + v0 (7.4) ∫t0 = −g(t − t0 ) + v0 , - 138 - where v0 is the initial velocity (it appears in equation (7.4) as a constant of integration). -
Infinitesimal and Tangent to Polylogarithmic Complexes For
AIMS Mathematics, 4(4): 1248–1257. DOI:10.3934/math.2019.4.1248 Received: 11 June 2019 Accepted: 11 August 2019 http://www.aimspress.com/journal/Math Published: 02 September 2019 Research article Infinitesimal and tangent to polylogarithmic complexes for higher weight Raziuddin Siddiqui* Department of Mathematical Sciences, Institute of Business Administration, Karachi, Pakistan * Correspondence: Email: [email protected]; Tel: +92-213-810-4700 Abstract: Motivic and polylogarithmic complexes have deep connections with K-theory. This article gives morphisms (different from Goncharov’s generalized maps) between |-vector spaces of Cathelineau’s infinitesimal complex for weight n. Our morphisms guarantee that the sequence of infinitesimal polylogs is a complex. We are also introducing a variant of Cathelineau’s complex with the derivation map for higher weight n and suggesting the definition of tangent group TBn(|). These tangent groups develop the tangent to Goncharov’s complex for weight n. Keywords: polylogarithm; infinitesimal complex; five term relation; tangent complex Mathematics Subject Classification: 11G55, 19D, 18G 1. Introduction The classical polylogarithms represented by Lin are one valued functions on a complex plane (see [11]). They are called generalization of natural logarithms, which can be represented by an infinite series (power series): X1 zk Li (z) = = − ln(1 − z) 1 k k=1 X1 zk Li (z) = 2 k2 k=1 : : X1 zk Li (z) = for z 2 ¼; jzj < 1 n kn k=1 The other versions of polylogarithms are Infinitesimal (see [8]) and Tangential (see [9]). We will discuss group theoretic form of infinitesimal and tangential polylogarithms in x 2.3, 2.4 and 2.5 below. -
Introduction to Differential Equations
Introduction to Differential Equations Lecture notes for MATH 2351/2352 Jeffrey R. Chasnov k kK m m x1 x2 The Hong Kong University of Science and Technology The Hong Kong University of Science and Technology Department of Mathematics Clear Water Bay, Kowloon Hong Kong Copyright ○c 2009–2016 by Jeffrey Robert Chasnov This work is licensed under the Creative Commons Attribution 3.0 Hong Kong License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/hk/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Preface What follows are my lecture notes for a first course in differential equations, taught at the Hong Kong University of Science and Technology. Included in these notes are links to short tutorial videos posted on YouTube. Much of the material of Chapters 2-6 and 8 has been adapted from the widely used textbook “Elementary differential equations and boundary value problems” by Boyce & DiPrima (John Wiley & Sons, Inc., Seventh Edition, ○c 2001). Many of the examples presented in these notes may be found in this book. The material of Chapter 7 is adapted from the textbook “Nonlinear dynamics and chaos” by Steven H. Strogatz (Perseus Publishing, ○c 1994). All web surfers are welcome to download these notes, watch the YouTube videos, and to use the notes and videos freely for teaching and learning. An associated free review book with links to YouTube videos is also available from the ebook publisher bookboon.com. I welcome any comments, suggestions or corrections sent by email to [email protected]. -
An Appreciation of Euler's Formula
Rose-Hulman Undergraduate Mathematics Journal Volume 18 Issue 1 Article 17 An Appreciation of Euler's Formula Caleb Larson North Dakota State University Follow this and additional works at: https://scholar.rose-hulman.edu/rhumj Recommended Citation Larson, Caleb (2017) "An Appreciation of Euler's Formula," Rose-Hulman Undergraduate Mathematics Journal: Vol. 18 : Iss. 1 , Article 17. Available at: https://scholar.rose-hulman.edu/rhumj/vol18/iss1/17 Rose- Hulman Undergraduate Mathematics Journal an appreciation of euler's formula Caleb Larson a Volume 18, No. 1, Spring 2017 Sponsored by Rose-Hulman Institute of Technology Department of Mathematics Terre Haute, IN 47803 [email protected] a scholar.rose-hulman.edu/rhumj North Dakota State University Rose-Hulman Undergraduate Mathematics Journal Volume 18, No. 1, Spring 2017 an appreciation of euler's formula Caleb Larson Abstract. For many mathematicians, a certain characteristic about an area of mathematics will lure him/her to study that area further. That characteristic might be an interesting conclusion, an intricate implication, or an appreciation of the impact that the area has upon mathematics. The particular area that we will be exploring is Euler's Formula, eix = cos x + i sin x, and as a result, Euler's Identity, eiπ + 1 = 0. Throughout this paper, we will develop an appreciation for Euler's Formula as it combines the seemingly unrelated exponential functions, imaginary numbers, and trigonometric functions into a single formula. To appreciate and further understand Euler's Formula, we will give attention to the individual aspects of the formula, and develop the necessary tools to prove it. -
Dynamics of the Elastic Pendulum Qisong Xiao; Shenghao Xia ; Corey Zammit; Nirantha Balagopal; Zijun Li Agenda
Dynamics of the Elastic Pendulum Qisong Xiao; Shenghao Xia ; Corey Zammit; Nirantha Balagopal; Zijun Li Agenda • Introduction to the elastic pendulum problem • Derivations of the equations of motion • Real-life examples of an elastic pendulum • Trivial cases & equilibrium states • MATLAB models The Elastic Problem (Simple Harmonic Motion) 푑2푥 푑2푥 푘 • 퐹 = 푚 = −푘푥 = − 푥 푛푒푡 푑푡2 푑푡2 푚 • Solve this differential equation to find 푥 푡 = 푐1 cos 휔푡 + 푐2 sin 휔푡 = 퐴푐표푠(휔푡 − 휑) • With velocity and acceleration 푣 푡 = −퐴휔 sin 휔푡 + 휑 푎 푡 = −퐴휔2cos(휔푡 + 휑) • Total energy of the system 퐸 = 퐾 푡 + 푈 푡 1 1 1 = 푚푣푡2 + 푘푥2 = 푘퐴2 2 2 2 The Pendulum Problem (with some assumptions) • With position vector of point mass 푥 = 푙 푠푖푛휃푖 − 푐표푠휃푗 , define 푟 such that 푥 = 푙푟 and 휃 = 푐표푠휃푖 + 푠푖푛휃푗 • Find the first and second derivatives of the position vector: 푑푥 푑휃 = 푙 휃 푑푡 푑푡 2 푑2푥 푑2휃 푑휃 = 푙 휃 − 푙 푟 푑푡2 푑푡2 푑푡 • From Newton’s Law, (neglecting frictional force) 푑2푥 푚 = 퐹 + 퐹 푑푡2 푔 푡 The Pendulum Problem (with some assumptions) Defining force of gravity as 퐹푔 = −푚푔푗 = 푚푔푐표푠휃푟 − 푚푔푠푖푛휃휃 and tension of the string as 퐹푡 = −푇푟 : 2 푑휃 −푚푙 = 푚푔푐표푠휃 − 푇 푑푡 푑2휃 푚푙 = −푚푔푠푖푛휃 푑푡2 Define 휔0 = 푔/푙 to find the solution: 푑2휃 푔 = − 푠푖푛휃 = −휔2푠푖푛휃 푑푡2 푙 0 Derivation of Equations of Motion • m = pendulum mass • mspring = spring mass • l = unstreatched spring length • k = spring constant • g = acceleration due to gravity • Ft = pre-tension of spring 푚푔−퐹 • r = static spring stretch, 푟 = 푡 s 푠 푘 • rd = dynamic spring stretch • r = total spring stretch 푟푠 + 푟푑 Derivation of Equations of Motion -
Numerical Solution of Ordinary Differential Equations
NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS Kendall Atkinson, Weimin Han, David Stewart University of Iowa Iowa City, Iowa A JOHN WILEY & SONS, INC., PUBLICATION Copyright c 2009 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created ore extended by sales representatives or written sales materials. The advice and strategies contained herin may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. -
Measuring Earth's Gravitational Constant with a Pendulum
Measuring Earth’s Gravitational Constant with a Pendulum Philippe Lewalle, Tony Dimino PHY 141 Lab TA, Fall 2014, Prof. Frank Wolfs University of Rochester November 30, 2014 Abstract In this lab we aim to calculate Earth’s gravitational constant by measuring the period of a pendulum. We obtain a value of 9.79 ± 0.02m/s2. This measurement agrees with the accepted value of g = 9.81m/s2 to within the precision limits of our procedure. Limitations of the techniques and assumptions used to calculate these values are discussed. The pedagogical context of this example report for PHY 141 is also discussed in the final remarks. 1 Theory The gravitational acceleration g near the surface of the Earth is known to be approximately constant, disregarding small effects due to geological variations and altitude shifts. We aim to measure the value of that acceleration in our lab, by observing the motion of a pendulum, whose motion depends both on g and the length L of the pendulum. It is a well known result that a pendulum consisting of a point mass and attached to a massless rod of length L obeys the relationship shown in eq. (1), d2θ g = − sin θ, (1) dt2 L where θ is the angle from the vertical, as showng in Fig. 1. For small displacements (ie small θ), we make a small angle approximation such that sin θ ≈ θ, which yields eq. (2). d2θ g = − θ (2) dt2 L Eq. (2) admits sinusoidal solutions with an angular frequency ω. We relate this to the period T of oscillation, to obtain an expression for g in terms of the period and length of the pendulum, shown in eq. -
2. the Tangent Line
2. The Tangent Line The tangent line to a circle at a point P on its circumference is the line perpendicular to the radius of the circle at P. In Figure 1, The line T is the tangent line which is Tangent Line perpendicular to the radius of the circle at the point P. T Figure 1: A Tangent Line to a Circle While the tangent line to a circle has the property that it is perpendicular to the radius at the point of tangency, it is not this property which generalizes to other curves. We shall make an observation about the tangent line to the circle which is carried over to other curves, and may be used as its defining property. Let us look at a specific example. Let the equation of the circle in Figure 1 be x22 + y = 25. We may easily determine the equation of the tangent line to this circle at the point P(3,4). First, we observe that the radius is a segment of the line passing through the origin (0, 0) and P(3,4), and its equation is (why?). Since the tangent line is perpendicular to this line, its slope is -3/4 and passes through P(3, 4), using the point-slope formula, its equation is found to be Let us compute y-values on both the tangent line and the circle for x-values near the point P(3, 4). Note that near P, we can solve for the y-value on the upper half of the circle which is found to be When x = 3.01, we find the y-value on the tangent line is y = -3/4(3.01) + 25/4 = 3.9925, while the corresponding value on the circle is (Note that the tangent line lie above the circle, so its y-value was expected to be a larger.) In Table 1, we indicate other corresponding values as we vary x near P. -
Numerical Integration
Chapter 12 Numerical Integration Numerical differentiation methods compute approximations to the derivative of a function from known values of the function. Numerical integration uses the same information to compute numerical approximations to the integral of the function. An important use of both types of methods is estimation of derivatives and integrals for functions that are only known at isolated points, as is the case with for example measurement data. An important difference between differen- tiation and integration is that for most functions it is not possible to determine the integral via symbolic methods, but we can still compute numerical approx- imations to virtually any definite integral. Numerical integration methods are therefore more useful than numerical differentiation methods, and are essential in many practical situations. We use the same general strategy for deriving numerical integration meth- ods as we did for numerical differentiation methods: We find the polynomial that interpolates the function at some suitable points, and use the integral of the polynomial as an approximation to the function. This means that the truncation error can be analysed in basically the same way as for numerical differentiation. However, when it comes to round-off error, integration behaves differently from differentiation: Numerical integration is very insensitive to round-off errors, so we will ignore round-off in our analysis. The mathematical definition of the integral is basically via a numerical in- tegration method, and we therefore start by reviewing this definition. We then derive the simplest numerical integration method, and see how its error can be analysed. We then derive two other methods that are more accurate, but for these we just indicate how the error analysis can be done. -
1101 Calculus I Lecture 2.1: the Tangent and Velocity Problems
Calculus Lecture 2.1: The Tangent and Velocity Problems Page 1 1101 Calculus I Lecture 2.1: The Tangent and Velocity Problems The Tangent Problem A good way to think of what the tangent line to a curve is that it is a straight line which approximates the curve well in the region where it touches the curve. A more precise definition will be developed later. Recall, straight lines have equations y = mx + b (slope m, y-intercept b), or, more useful in this case, y − y0 = m(x − x0) (slope m, and passes through the point (x0, y0)). Your text has a fairly simple example. I will do something more complex instead. Example Find the tangent line to the parabola y = −3x2 + 12x − 8 at the point P (3, 1). Our solution involves finding the equation of a straight line, which is y − y0 = m(x − x0). We already know the tangent line should touch the curve, so it will pass through the point P (3, 1). This means x0 = 3 and y0 = 1. We now need to determine the slope of the tangent line, m. But we need two points to determine the slope of a line, and we only know one. We only know that the tangent line passes through the point P (3, 1). We proceed by approximations. We choose a point on the parabola that is nearby (3,1) and use it to approximate the slope of the tangent line. Let’s draw a sketch. Choose a point close to P (3, 1), say Q(4, −8). -
3 Runge-Kutta Methods
3 Runge-Kutta Methods In contrast to the multistep methods of the previous section, Runge-Kutta methods are single-step methods — however, with multiple stages per step. They are motivated by the dependence of the Taylor methods on the specific IVP. These new methods do not require derivatives of the right-hand side function f in the code, and are therefore general-purpose initial value problem solvers. Runge-Kutta methods are among the most popular ODE solvers. They were first studied by Carle Runge and Martin Kutta around 1900. Modern developments are mostly due to John Butcher in the 1960s. 3.1 Second-Order Runge-Kutta Methods As always we consider the general first-order ODE system y0(t) = f(t, y(t)). (42) Since we want to construct a second-order method, we start with the Taylor expansion h2 y(t + h) = y(t) + hy0(t) + y00(t) + O(h3). 2 The first derivative can be replaced by the right-hand side of the differential equation (42), and the second derivative is obtained by differentiating (42), i.e., 00 0 y (t) = f t(t, y) + f y(t, y)y (t) = f t(t, y) + f y(t, y)f(t, y), with Jacobian f y. We will from now on neglect the dependence of y on t when it appears as an argument to f. Therefore, the Taylor expansion becomes h2 y(t + h) = y(t) + hf(t, y) + [f (t, y) + f (t, y)f(t, y)] + O(h3) 2 t y h h = y(t) + f(t, y) + [f(t, y) + hf (t, y) + hf (t, y)f(t, y)] + O(h3(43)).