Cálculo de Variaciones I Tarea # 3
|
|
- Erik Oliver
- 5 years ago
- Views:
Transcription
1 1 Universidad Autónoma Metropolitana - Iztapalapa Cálculo de Variaciones I Tarea # 3 Resuelva los siguientes problemas: De la sección 3.1: resolver los ejercicios 1, 2, 3 y 5. Nota: La ecuación (3.4) a la que hace referencia el ejercicio 2 es: H(y, y,y f d f f )=y y f =const. (3.4) y dx y y De la sección 3.2: resolver los ejercicios 1, 2 y 3. Nota: La ecuación (3.17) a la que hacen referencia los ejercicios 1 y 2 es: H = n k=1 q k L q k L =const. (3.17) De la sección 4.2: resolver los ejercicios 1 y 2. Prof. Antonio Hernández Garduño
2 3.1 Functionals Containing Higher-Order Derivatives 59 is x f(x, y, y,...,y (n) ) dx ( ) ( ) ( 1) n dn f n 1 dn 1 f dx n y n +( 1) dx n 1 y n f y =. (3.9) Exercises 3.1: 1. Find the general solution for the extremals to the functional J defined by x ( (y ) 2 y 2 +2yx 3) dx. 2. Conservation Law: Suppose the integrand f defining the functional J does not depend on x explicitly. Prove that equation (3.4) is satisfied along any extremal. 3. For the functional J defined by y 1+(y ) 2 dx, find an extremal satisfying the conditions y() =, y () =, y(1) = 1, and y (1) = Degenerate Case: Let J be a functional of the form x (A(x, y, y )y + B(x, y, y )) dx, where A and B are smooth functions of x, y, andy. Prove that the Euler- Lagrange equation for this functional is a differential equation of at most second order and that consequently any solutions can satisfy at most two arbitrary boundary conditions. 5. Let J and K be functionals defined by K(y) = x x1 x where, for some smooth function G, f(x, y, y,y ) dx F (x, y, y,y ) dx, F (x, y, y,y )=f(x, y, y,y )+ d dx G(x, y, y ). Prove that any extremals for J must also be extremals for K.
3 64 3 Some Generalizations which, in turn, lead to the relations d L = L, dt q k q k m q k = V q k, for k =1, 2, 3. Recall from Section 1.3 that the kth component of force, f k on the particle is given by f k = V q k. Hence, the Euler-Lagrange equations imply Newton s equation f = ma. where a = q is the acceleration and f =(f 1,f 2,f 3 )istheforceontheparticle. For this example note that if the potential energy V does not depend on time explicitly then neither does L. In this case, we have the conservation law (3.17), which gives H = 1 2 m( q2 1 + q q 2 3)+V (q) =const.; i.e., the total energy of the particle is conserved along an extremal. Exercises 3.2: 1. Let L(t, q, q) = 1 2 ( q q 2 2 ) gq2, where g is a constant. (a) Find the extremals for the functional J defined by J(q) = t1 t L(t, q, q) dt. (b) Verify that equation (3.17) is satisfied. 2. Prove equation (3.17). 3. Let L(t, q, q) = q q2 2 q2 2 kq 2, where k is a constant. Find the extremals for the functional J defined by J(q) = t1 t L(t, q, q) dt.
4 4.2 The Isoperimetric Problem 93 The Lagrange multiplier therefore corresponds to the rate of change of the extremum J(y) with respect to the isoperimetric parameter L. We note a certain duality that exists for the isoperimetric problem. Suppose that λ ; then any extremal y to the problem with F = f λg must also be an extremal to a problem with G = g ˆλf, whereˆλ =1/λ. More specifically, suppose that y minimizes J subject to the isoperimetric constraint I(y) = L. Let K denote the minimum value J(y). Then and thus K = J(y) λ(i(y) L), L = I(y) ˆλ(J(y) K). We have that J λi = λ(i ˆλJ), and this indicates that the minimum for the functional x 1 x Fdxcorresponds to the maximum for the functional x Gdx. A similar statement can be made if y produces a maximum for J subject to I(y) = L. Wethushavethefollowingresult. Theorem Suppose that y produces a minimum (maximum) value for J subject to the constraint I(y) =L and that λ.letk = J(y). Then y produces a maximum (minimum) for I subject to the constraint K, and I(y) =L. In view of the above result, suppose we revisit, for example, the catenary problem of Example We saw that the catenary is the curve along which the potential energy is an extremum subject to the condition that the cable is of length L. Infact,itcanbeshownthatthepotentialenergyisminimum along a catenary for the appropriate choice of ˆξ. Theorem4.2.3showsthat, for a fixed value of potential energy, the catenary is the curve along which the arclength is maximized. The duality relationship also helps to elucidate the condition that y not be an extremal for I in Theorem If y is an extremal for I, theninthe dual problem ˆλ =.ThismeansthatI(y) =L, independent of the constraint K, so that K can be prescribed without changing the extremum for I. Alternatively, if λ =, then K, independent of the constraint I(y) =L, so that the problem does not depend on the constraint. At any rate, if λ is not finite or if λ =theproblemisdegenerate. Exercises 4.2: 1. Let J and I be the functionals defined by y 2 dx, I(y) = ydx. Find the extremals for J subject to the conditions y() =, y(1) = 2, and I(y) =L.
5 94 4 Isoperimetric Problems 2. Dido s Problem in Polar Coördinates: LetJ and I be functionals defined by J(r) = 1 2 π r 2 dθ, I(r) = π r2 + r 2 dθ, where r = dr/dθ. FindanextremalforJ subject to the conditions r() =, r(π) =, and I(r) =L>. 3. Let J and I be the functionals defined by (yy ) 2 dx, I(y) =int 1 y 2 dx. Suppose that y is an extremal for J subject to the conditions y() = 1, y(1) = 2, and I(y) =L. (a) Find a first integral for the Euler-Lagrange equations for this problem and show that for L =3, y(x) = 4 3(x 1) 2. (b) For L =7/3 show that there exists a linear function that is an extremal for the problem. (c) For L =5/2 showthatthisproblemadmitsthesolutionλ =.Find the extremal corresponding to this value. 4. Let A(y) beasmoothfunctionandlet and I(y) = A(y)y dx 1+y 2 dx. Formulate the Euler-Lagrange equations for the isoperimetric problem with y() =, y(1) = 1, and I(y) =L> 2. Show that λ =,andthat there are an infinite number of solutions to the problem. Explain without using the Euler-Lagrange equations (or any conservation laws) why there must be an infinite number of solutions to this problem. 5. Let y be the extremal to the catenary problem of Example Show that for L sufficiently large there is an x (, 1) such that y(x) <. 4.3 Some Generalizations on the Isoperimetric Problem In this section we present some modest generalizations on the isoperimetric problem discussed in Section 4.2. Most of the details are left to the reader.
Calculus of Variations Summer Term 2014
Calculus of Variations Summer Term 2014 Lecture 5 7. Mai 2014 c Daria Apushkinskaya 2014 () Calculus of variations lecture 5 7. Mai 2014 1 / 25 Purpose of Lesson Purpose of Lesson: To discuss catenary
More informationIntroduction to the Calculus of Variations
236861 Numerical Geometry of Images Tutorial 1 Introduction to the Calculus of Variations Alex Bronstein c 2005 1 Calculus Calculus of variations 1. Function Functional f : R n R Example: f(x, y) =x 2
More informationMath 210, Final Exam, Fall 2010 Problem 1 Solution. v cosθ = u. v Since the magnitudes of the vectors are positive, the sign of the dot product will
Math, Final Exam, Fall Problem Solution. Let u,, and v,,3. (a) Is the angle between u and v acute, obtuse, or right? (b) Find an equation for the plane through (,,) containing u and v. Solution: (a) The
More informationReading group: Calculus of Variations and Optimal Control Theory by Daniel Liberzon
: Calculus of Variations and Optimal Control Theory by Daniel Liberzon 16th March 2017 1 / 30 Content 1 2 Recall on finite-dimensional of a global minimum 3 Infinite-dimensional 4 2 / 30 Content 1 2 Recall
More informationMATH 307: Problem Set #3 Solutions
: Problem Set #3 Solutions Due on: May 3, 2015 Problem 1 Autonomous Equations Recall that an equilibrium solution of an autonomous equation is called stable if solutions lying on both sides of it tend
More informationThe Corrected Trial Solution in the Method of Undetermined Coefficients
Definition of Related Atoms The Basic Trial Solution Method Symbols Superposition Annihilator Polynomial for f(x) Annihilator Equation for f(x) The Corrected Trial Solution in the Method of Undetermined
More informationCalculus of Variations Summer Term 2014
Calculus of Variations Summer Term 2014 Lecture 9 23. Mai 2014 c Daria Apushkinskaya 2014 () Calculus of variations lecture 9 23. Mai 2014 1 / 23 Purpose of Lesson Purpose of Lesson: To consider several
More informationOrdinary Differential Equations (ODEs)
c01.tex 8/10/2010 22: 55 Page 1 PART A Ordinary Differential Equations (ODEs) Chap. 1 First-Order ODEs Sec. 1.1 Basic Concepts. Modeling To get a good start into this chapter and this section, quickly
More informationCalculus of Variations Summer Term 2014
Calculus of Variations Summer Term 2014 Lecture 2 25. April 2014 c Daria Apushkinskaya 2014 () Calculus of variations lecture 2 25. April 2014 1 / 20 Purpose of Lesson Purpose of Lesson: To discuss the
More information2.12: Derivatives of Exp/Log (cont d) and 2.15: Antiderivatives and Initial Value Problems
2.12: Derivatives of Exp/Log (cont d) and 2.15: Antiderivatives and Initial Value Problems Mathematics 3 Lecture 14 Dartmouth College February 03, 2010 Derivatives of the Exponential and Logarithmic Functions
More information(Most of the material presented in this chapter is taken from Thornton and Marion, Chap. 6)
Chapter 3. Calculus of Variations Most of the material presented in this chapter is taken from Thornton and Marion, Chap. 6 In preparation for our study of Lagrangian and Hamiltonian dynamics in later
More informationHigher-order ordinary differential equations
Higher-order ordinary differential equations 1 A linear ODE of general order n has the form a n (x) dn y dx n +a n 1(x) dn 1 y dx n 1 + +a 1(x) dy dx +a 0(x)y = f(x). If f(x) = 0 then the equation is called
More informationJohann Bernoulli ( )he established the principle of the so-called virtual work for static systems.
HISTORICAL SURVEY Wilhelm von Leibniz (1646-1716): Leibniz s approach to mechanics was based on the use of mathematical operations with the scalar quantities of energy, as opposed to the vector quantities
More informationChapter 2: First Order DE 2.6 Exact DE and Integrating Fa
Chapter 2: First Order DE 2.6 Exact DE and Integrating Factor First Order DE Recall the general form of the First Order DEs (FODE): dy dx = f(x, y) (1) (In this section x is the independent variable; not
More informationminimize x subject to (x 2)(x 4) u,
Math 6366/6367: Optimization and Variational Methods Sample Preliminary Exam Questions 1. Suppose that f : [, L] R is a C 2 -function with f () on (, L) and that you have explicit formulae for
More informationUNIT 3 INTEGRATION 3.0 INTRODUCTION 3.1 OBJECTIVES. Structure
Calculus UNIT 3 INTEGRATION Structure 3.0 Introduction 3.1 Objectives 3.2 Basic Integration Rules 3.3 Integration by Substitution 3.4 Integration of Rational Functions 3.5 Integration by Parts 3.6 Answers
More informationCalculus of Variations Summer Term 2014
Calculus of Variations Summer Term 2014 Lecture 12 26. Juni 2014 c Daria Apushkinskaya 2014 () Calculus of variations lecture 12 26. Juni 2014 1 / 25 Purpose of Lesson Purpose of Lesson: To discuss numerical
More informationCalculus 2502A - Advanced Calculus I Fall : Local minima and maxima
Calculus 50A - Advanced Calculus I Fall 014 14.7: Local minima and maxima Martin Frankland November 17, 014 In these notes, we discuss the problem of finding the local minima and maxima of a function.
More informationOptimization using Calculus. Optimization of Functions of Multiple Variables subject to Equality Constraints
Optimization using Calculus Optimization of Functions of Multiple Variables subject to Equality Constraints 1 Objectives Optimization of functions of multiple variables subjected to equality constraints
More informationSolutions to old Exam 3 problems
Solutions to old Exam 3 problems Hi students! I am putting this version of my review for the Final exam review here on the web site, place and time to be announced. Enjoy!! Best, Bill Meeks PS. There are
More informationVariational Methods and Optimal Control
Variational Methods and Optimal Control A/Prof. Matthew Roughan July 26, 2010 lecture01 Introduction What is the point of this course? Revision Example 1: The money pit. Example 2: Catenary: shape of a
More informationRobotics. Islam S. M. Khalil. November 15, German University in Cairo
Robotics German University in Cairo November 15, 2016 Fundamental concepts In optimal control problems the objective is to determine a function that minimizes a specified functional, i.e., the performance
More informationMTH4101 CALCULUS II REVISION NOTES. 1. COMPLEX NUMBERS (Thomas Appendix 7 + lecture notes) ax 2 + bx + c = 0. x = b ± b 2 4ac 2a. i = 1.
MTH4101 CALCULUS II REVISION NOTES 1. COMPLEX NUMBERS (Thomas Appendix 7 + lecture notes) 1.1 Introduction Types of numbers (natural, integers, rationals, reals) The need to solve quadratic equations:
More information2.2 Separable Equations
2.2 Separable Equations Definition A first-order differential equation that can be written in the form Is said to be separable. Note: the variables of a separable equation can be written as Examples Solve
More informationCalculus of Variations Summer Term 2015
Calculus of Variations Summer Term 2015 Lecture 12 Universität des Saarlandes 17. Juni 2015 c Daria Apushkinskaya (UdS) Calculus of variations lecture 12 17. Juni 2015 1 / 31 Purpose of Lesson Purpose
More information6.1 Matrices. Definition: A Matrix A is a rectangular array of the form. A 11 A 12 A 1n A 21. A 2n. A m1 A m2 A mn A 22.
61 Matrices Definition: A Matrix A is a rectangular array of the form A 11 A 12 A 1n A 21 A 22 A 2n A m1 A m2 A mn The size of A is m n, where m is the number of rows and n is the number of columns The
More informationMath 212-Lecture Interior critical points of functions of two variables
Math 212-Lecture 24 13.10. Interior critical points of functions of two variables Previously, we have concluded that if f has derivatives, all interior local min or local max should be critical points.
More informationLagrange Relaxation and Duality
Lagrange Relaxation and Duality As we have already known, constrained optimization problems are harder to solve than unconstrained problems. By relaxation we can solve a more difficult problem by a simpler
More informationVariational Methods & Optimal Control
Variational Methods & Optimal Control lecture 12 Matthew Roughan Discipline of Applied Mathematics School of Mathematical Sciences University of Adelaide April 14, 216
More informationLecture 13 - Wednesday April 29th
Lecture 13 - Wednesday April 29th jacques@ucsdedu Key words: Systems of equations, Implicit differentiation Know how to do implicit differentiation, how to use implicit and inverse function theorems 131
More informationSecond Order ODEs. CSCC51H- Numerical Approx, Int and ODEs p.130/177
Second Order ODEs Often physical or biological systems are best described by second or higher-order ODEs. That is, second or higher order derivatives appear in the mathematical model of the system. For
More informationStudy Guide Block 2: Ordinary Differential Equations. Unit 9: The Laplace Transform, Part Overview
Unit 9: The Laplace Transform, Part 1 1. Overview ~ The Laplace transform has application far beyond its present role in this block of being a useful device for solving certain types of linear differential
More informationCalculus of Variation An Introduction To Isoperimetric Problems
Calculus of Variation An Introduction To Isoperimetric Problems Kevin Wang The University of Sydney SSP Working Seminars, MATH2916 May 4, 2013 Contents I Lagrange Multipliers 2 1 Single Constraint Lagrange
More informationIntegration, Separation of Variables
Week #1 : Integration, Separation of Variables Goals: Introduce differential equations. Review integration techniques. Solve first-order DEs using separation of variables. 1 Sources of Differential Equations
More informationLecture 9: Implicit function theorem, constrained extrema and Lagrange multipliers
Lecture 9: Implicit function theorem, constrained extrema and Lagrange multipliers Rafikul Alam Department of Mathematics IIT Guwahati What does the Implicit function theorem say? Let F : R 2 R be C 1.
More informationMath 308, Sections 301, 302, Summer 2008 Review before Test I 06/09/2008
Math 308, Sections 301, 302, Summer 2008 Review before Test I 06/09/2008 Chapter 1. Introduction Section 1.1 Background Definition Equation that contains some derivatives of an unknown function is called
More information01 Harmonic Oscillations
Utah State University DigitalCommons@USU Foundations of Wave Phenomena Library Digital Monographs 8-2014 01 Harmonic Oscillations Charles G. Torre Department of Physics, Utah State University, Charles.Torre@usu.edu
More informationMATH Midterm 1 Sample 4
1. (15 marks) (a) (4 marks) Given the function: f(x, y) = arcsin x 2 + y, find its first order partial derivatives at the point (0, 3). Simplify your answers. Solution: Compute the first order partial
More informationMath221: HW# 7 solutions
Math22: HW# 7 solutions Andy Royston November 7, 25.3.3 let x = e u. Then ln x = u, x2 = e 2u, and dx = e 2u du. Furthermore, when x =, u, and when x =, u =. Hence x 2 ln x) 3 dx = e 2u u 3 e u du) = e
More informationNumerical Analysis: Interpolation Part 1
Numerical Analysis: Interpolation Part 1 Computer Science, Ben-Gurion University (slides based mostly on Prof. Ben-Shahar s notes) 2018/2019, Fall Semester BGU CS Interpolation (ver. 1.00) AY 2018/2019,
More informationLECTURE 16 GAUSS QUADRATURE In general for Newton-Cotes (equispaced interpolation points/ data points/ integration points/ nodes).
CE 025 - Lecture 6 LECTURE 6 GAUSS QUADRATURE In general for ewton-cotes (equispaced interpolation points/ data points/ integration points/ nodes). x E x S fx dx hw' o f o + w' f + + w' f + E 84 f 0 f
More informationPhysics 129a Calculus of Variations Frank Porter Revision
Physics 129a Calculus of Variations 71113 Frank Porter Revision 171116 1 Introduction Many problems in physics have to do with extrema. When the problem involves finding a function that satisfies some
More informationFinal Exam 2011 Winter Term 2 Solutions
. (a Find the radius of convergence of the series: ( k k+ x k. Solution: Using the Ratio Test, we get: L = lim a k+ a k = lim ( k+ k+ x k+ ( k k+ x k = lim x = x. Note that the series converges for L
More informationDEPARTMENT OF MATHEMATICS AND STATISTICS UNIVERSITY OF MASSACHUSETTS. MATH 233 SOME SOLUTIONS TO EXAM 2 Fall 2018
DEPARTMENT OF MATHEMATICS AND STATISTICS UNIVERSITY OF MASSACHUSETTS MATH 233 SOME SOLUTIONS TO EXAM 2 Fall 208 Version A refers to the regular exam and Version B to the make-up. Version A. A particle
More informationM343 Homework 3 Enrique Areyan May 17, 2013
M343 Homework 3 Enrique Areyan May 17, 013 Section.6 3. Consider the equation: (3x xy + )dx + (6y x + 3)dy = 0. Let M(x, y) = 3x xy + and N(x, y) = 6y x + 3. Since: y = x = N We can conclude that this
More informationCalculus of Variations
ECE 68 Midterm Exam Solution April 1, 8 1 Calculus of Variations This exam is open book and open notes You may consult additional references You may even discuss the problems (with anyone), but you must
More informationFirst Order Differential Equations
Chapter 2 First Order Differential Equations 2.1 9 10 CHAPTER 2. FIRST ORDER DIFFERENTIAL EQUATIONS 2.2 Separable Equations A first order differential equation = f(x, y) is called separable if f(x, y)
More informationLagrange multipliers. Portfolio optimization. The Lagrange multipliers method for finding constrained extrema of multivariable functions.
Chapter 9 Lagrange multipliers Portfolio optimization The Lagrange multipliers method for finding constrained extrema of multivariable functions 91 Lagrange multipliers Optimization problems often require
More informationA Short Essay on Variational Calculus
A Short Essay on Variational Calculus Keonwook Kang, Chris Weinberger and Wei Cai Department of Mechanical Engineering, Stanford University Stanford, CA 94305-4040 May 3, 2006 Contents 1 Definition of
More informationFirst Order ODEs, Part I
Craig J. Sutton craig.j.sutton@dartmouth.edu Department of Mathematics Dartmouth College Math 23 Differential Equations Winter 2013 Outline 1 2 in General 3 The Definition & Technique Example Test for
More informationLecture 5 - Fundamental Theorem for Line Integrals and Green s Theorem
Lecture 5 - Fundamental Theorem for Line Integrals and Green s Theorem Math 392, section C September 14, 2016 392, section C Lect 5 September 14, 2016 1 / 22 Last Time: Fundamental Theorem for Line Integrals:
More informationSeries Solutions of Differential Equations
Chapter 6 Series Solutions of Differential Equations In this chapter we consider methods for solving differential equations using power series. Sequences and infinite series are also involved in this treatment.
More informationNumerical Optimization Algorithms
Numerical Optimization Algorithms 1. Overview. Calculus of Variations 3. Linearized Supersonic Flow 4. Steepest Descent 5. Smoothed Steepest Descent Overview 1 Two Main Categories of Optimization Algorithms
More informationThe Double Sum as an Iterated Integral
Unit 2: The Double Sum as an Iterated Integral 1. Overview In Part 1 of our course, we showed that there was a relationship between a certain infinite sum known as the definite integral and the inverse
More informationMultivariable Calculus and Matrix Algebra-Summer 2017
Multivariable Calculus and Matrix Algebra-Summer 017 Homework 4 Solutions Note that the solutions below are for the latest version of the problems posted. For those of you who worked on an earlier version
More informationLecture Notes for PHY 405 Classical Mechanics
Lecture Notes for PHY 45 Classical Mechanics From Thorton & Marion s Classical Mechanics Prepared by Dr. Joseph M. Hahn Saint Mary s University Department of Astronomy & Physics October 11, 25 Chapter
More informationMath 210, Final Exam, Spring 2012 Problem 1 Solution. (a) Find an equation of the plane passing through the tips of u, v, and w.
Math, Final Exam, Spring Problem Solution. Consider three position vectors (tails are the origin): u,, v 4,, w,, (a) Find an equation of the plane passing through the tips of u, v, and w. (b) Find an equation
More informationCalculus of Variations Summer Term 2016
Calculus of Variations Summer Term 2016 Lecture 12 Universität des Saarlandes 17. Juni 2016 c Daria Apushkinskaya (UdS) Calculus of variations lecture 12 17. Juni 2016 1 / 32 Purpose of Lesson Purpose
More information1MA6 Partial Differentiation and Multiple Integrals: I
1MA6/1 1MA6 Partial Differentiation and Multiple Integrals: I Dr D W Murray Michaelmas Term 1994 1. Total differential. (a) State the conditions for the expression P (x, y)dx+q(x, y)dy to be the perfect
More informationTaylor Series and stationary points
Chapter 5 Taylor Series and stationary points 5.1 Taylor Series The surface z = f(x, y) and its derivatives can give a series approximation for f(x, y) about some point (x 0, y 0 ) as illustrated in Figure
More informationHW2 Solutions. MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) October 14, Checklist: Section 2.6: 1, 3, 6, 8, 10, 15, [20, 22]
HW2 Solutions MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) October 14, 2013 Checklist: Section 2.6: 1, 3, 6, 8, 10, 15, [20, 22] Section 3.1: 1, 2, 3, 9, 16, 18, 20, 23 Section 3.2: 1, 2,
More information(x 3)(x + 5) = (x 3)(x 1) = x + 5. sin 2 x e ax bx 1 = 1 2. lim
SMT Calculus Test Solutions February, x + x 5 Compute x x x + Answer: Solution: Note that x + x 5 x x + x )x + 5) = x )x ) = x + 5 x x + 5 Then x x = + 5 = Compute all real values of b such that, for fx)
More informationMATH529 Fundamentals of Optimization Constrained Optimization I
MATH529 Fundamentals of Optimization Constrained Optimization I Marco A. Montes de Oca Mathematical Sciences, University of Delaware, USA 1 / 26 Motivating Example 2 / 26 Motivating Example min cost(b)
More information1. For each function, find all of its critical points and then classify each point as a local extremum or saddle point.
Solutions Review for Exam # Math 6. For each function, find all of its critical points and then classify each point as a local extremum or saddle point. a fx, y x + 6xy + y Solution.The gradient of f is
More informationPhysics 351, Spring 2015, Homework #7. Due at start of class, Friday, March 3, 2017
Physics 351, Spring 2015, Homework #7. Due at start of class, Friday, March 3, 2017 Course info is at positron.hep.upenn.edu/p351 When you finish this homework, remember to visit the feedback page at positron.hep.upenn.edu/q351
More informationA. MT-03, P Solve: (i) = 0. (ii) A. MT-03, P. 17, Solve : (i) + 4 = 0. (ii) A. MT-03, P. 16, Solve : (i)
Program : M.A./M.Sc. (Mathematics) M.A./M.Sc. (Previous) Paper Code:MT-03 Differential Equations, Calculus of Variations & Special Functions Section C (Long Answers Questions) 1. Solve 2x cos y 2x sin
More informationPhysics 351, Spring 2015, Homework #7. Due at start of class, Friday, March 6, 2015
Physics 351, Spring 2015, Homework #7. Due at start of class, Friday, March 6, 2015 Course info is at positron.hep.upenn.edu/p351 When you finish this homework, remember to visit the feedback page at positron.hep.upenn.edu/q351
More informationDefinition of differential equations and their classification. Methods of solution of first-order differential equations
Introduction to differential equations: overview Definition of differential equations and their classification Solutions of differential equations Initial value problems Existence and uniqueness Mathematical
More informationz x = f x (x, y, a, b), z y = f y (x, y, a, b). F(x, y, z, z x, z y ) = 0. This is a PDE for the unknown function of two independent variables.
Chapter 2 First order PDE 2.1 How and Why First order PDE appear? 2.1.1 Physical origins Conservation laws form one of the two fundamental parts of any mathematical model of Continuum Mechanics. These
More informationLagrange Multipliers
Optimization with Constraints As long as algebra and geometry have been separated, their progress have been slow and their uses limited; but when these two sciences have been united, they have lent each
More informationSummary of the simplex method
MVE165/MMG630, The simplex method; degeneracy; unbounded solutions; infeasibility; starting solutions; duality; interpretation Ann-Brith Strömberg 2012 03 16 Summary of the simplex method Optimality condition:
More informationFinal Examination Solutions
Math. 5, Sections 5 53 (Fulling) 7 December Final Examination Solutions Test Forms A and B were the same except for the order of the multiple-choice responses. This key is based on Form A. Name: Section:
More informationNumerical Analysis Solution of Algebraic Equation (non-linear equation) 1- Trial and Error. 2- Fixed point
Numerical Analysis Solution of Algebraic Equation (non-linear equation) 1- Trial and Error In this method we assume initial value of x, and substitute in the equation. Then modify x and continue till we
More informationIntroduction to Real Analysis Alternative Chapter 1
Christopher Heil Introduction to Real Analysis Alternative Chapter 1 A Primer on Norms and Banach Spaces Last Updated: March 10, 2018 c 2018 by Christopher Heil Chapter 1 A Primer on Norms and Banach Spaces
More informationConvex Optimization. Dani Yogatama. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. February 12, 2014
Convex Optimization Dani Yogatama School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA February 12, 2014 Dani Yogatama (Carnegie Mellon University) Convex Optimization February 12,
More informationC2 Differential Equations : Computational Modeling and Simulation Instructor: Linwei Wang
C2 Differential Equations 4040-849-03: Computational Modeling and Simulation Instructor: Linwei Wang Part II Variational Principle Calculus Revisited Partial Derivatives Function of one variable df dx
More information1.11 Some Higher-Order Differential Equations
page 99. Some Higher-Order Differential Equations 99. Some Higher-Order Differential Equations So far we have developed analytical techniques only for solving special types of firstorder differential equations.
More informationMATH H53 : Final exam
MATH H53 : Final exam 11 May, 18 Name: You have 18 minutes to answer the questions. Use of calculators or any electronic items is not permitted. Answer the questions in the space provided. If you run out
More informationMathematical Methods - Lecture 7
Mathematical Methods - Lecture 7 Yuliya Tarabalka Inria Sophia-Antipolis Méditerranée, Titane team, http://www-sop.inria.fr/members/yuliya.tarabalka/ Tel.: +33 (0)4 92 38 77 09 email: yuliya.tarabalka@inria.fr
More informationMathematics (Course B) Lent Term 2005 Examples Sheet 2
N12d Natural Sciences, Part IA Dr M. G. Worster Mathematics (Course B) Lent Term 2005 Examples Sheet 2 Please communicate any errors in this sheet to Dr Worster at M.G.Worster@damtp.cam.ac.uk. Note that
More informationLECTURE NOTES ELEMENTARY NUMERICAL METHODS. Eusebius Doedel
LECTURE NOTES on ELEMENTARY NUMERICAL METHODS Eusebius Doedel TABLE OF CONTENTS Vector and Matrix Norms 1 Banach Lemma 20 The Numerical Solution of Linear Systems 25 Gauss Elimination 25 Operation Count
More informationThe University of British Columbia Midterm 1 Solutions - February 3, 2012 Mathematics 105, 2011W T2 Sections 204, 205, 206, 211.
1. a) Let The University of British Columbia Midterm 1 Solutions - February 3, 2012 Mathematics 105, 2011W T2 Sections 204, 205, 206, 211 fx, y) = x siny). If the value of x, y) changes from 0, π) to 0.1,
More informationFirst-Order Differential Equations
CHAPTER 1 First-Order Differential Equations 1. Diff Eqns and Math Models Know what it means for a function to be a solution to a differential equation. In order to figure out if y = y(x) is a solution
More informationMath 2a Prac Lectures on Differential Equations
Math 2a Prac Lectures on Differential Equations Prof. Dinakar Ramakrishnan 272 Sloan, 253-37 Caltech Office Hours: Fridays 4 5 PM Based on notes taken in class by Stephanie Laga, with a few added comments
More informationVariational Principles
Part IB Variational Principles Year 218 217 216 215 214 213 212 211 21 218 Paper 1, Section I 4B 46 Variational Principles Find, using a Lagrange multiplier, the four stationary points in R 3 of the function
More informationVARIATIONAL PRINCIPLES
CHAPTER - II VARIATIONAL PRINCIPLES Unit : Euler-Lagranges s Differential Equations: Introduction: We have seen that co-ordinates are the tools in the hands of a mathematician. With the help of these co-ordinates
More information24. x 2 y xy y sec(ln x); 1 e x y 1 cos(ln x), y 2 sin(ln x) 25. y y tan x 26. y 4y sec 2x 28.
16 CHAPTER 4 HIGHER-ORDER DIFFERENTIAL EQUATIONS 11. y 3y y 1 4. x yxyy sec(ln x); 1 e x y 1 cos(ln x), y sin(ln x) ex 1. y y y 1 x 13. y3yy sin e x 14. yyy e t arctan t 15. yyy e t ln t 16. y y y 41x
More informationChapter 4: Partial differentiation
Chapter 4: Partial differentiation It is generally the case that derivatives are introduced in terms of functions of a single variable. For example, y = f (x), then dy dx = df dx = f. However, most of
More informationPartial Derivatives Formulas. KristaKingMath.com
Partial Derivatives Formulas KristaKingMath.com Domain and range of a multivariable function A function f of two variables is a rule that assigns to each ordered pair of real numbers (x, y) in a set D
More informationUNIVERSITY OF MANITOBA
Question Points Score INSTRUCTIONS TO STUDENTS: This is a 6 hour examination. No extra time will be given. No texts, notes, or other aids are permitted. There are no calculators, cellphones or electronic
More informationMA22S3 Summary Sheet: Ordinary Differential Equations
MA22S3 Summary Sheet: Ordinary Differential Equations December 14, 2017 Kreyszig s textbook is a suitable guide for this part of the module. Contents 1 Terminology 1 2 First order separable 2 2.1 Separable
More informationMath 201 Solutions to Assignment 1. 2ydy = x 2 dx. y = C 1 3 x3
Math 201 Solutions to Assignment 1 1. Solve the initial value problem: x 2 dx + 2y = 0, y(0) = 2. x 2 dx + 2y = 0, y(0) = 2 2y = x 2 dx y 2 = 1 3 x3 + C y = C 1 3 x3 Notice that y is not defined for some
More informationBasic Theory of Linear Differential Equations
Basic Theory of Linear Differential Equations Picard-Lindelöf Existence-Uniqueness Vector nth Order Theorem Second Order Linear Theorem Higher Order Linear Theorem Homogeneous Structure Recipe for Constant-Coefficient
More informationRelation of Pure Minimum Cost Flow Model to Linear Programming
Appendix A Page 1 Relation of Pure Minimum Cost Flow Model to Linear Programming The Network Model The network pure minimum cost flow model has m nodes. The external flows given by the vector b with m
More informationDIFFERENTIAL EQUATIONS
DIFFERENTIAL EQUATIONS Chapter 1 Introduction and Basic Terminology Most of the phenomena studied in the sciences and engineering involve processes that change with time. For example, it is well known
More informationAPPENDIX : PARTIAL FRACTIONS
APPENDIX : PARTIAL FRACTIONS Appendix : Partial Fractions Given the expression x 2 and asked to find its integral, x + you can use work from Section. to give x 2 =ln( x 2) ln( x + )+c x + = ln k x 2 x+
More informationthen the substitution z = ax + by + c reduces this equation to the separable one.
7 Substitutions II Some of the topics in this lecture are optional and will not be tested at the exams. However, for a curious student it should be useful to learn a few extra things about ordinary differential
More informationEngg. Math. I. Unit-I. Differential Calculus
Dr. Satish Shukla 1 of 50 Engg. Math. I Unit-I Differential Calculus Syllabus: Limits of functions, continuous functions, uniform continuity, monotone and inverse functions. Differentiable functions, Rolle
More informationMath 1431 Final Exam Review
Math 1431 Final Exam Review Comprehensive exam. I recommend you study all past reviews and practice exams as well. Know all rules/formulas. Make a reservation for the final exam. If you miss it, go back
More informationMTAEA Implicit Functions
School of Economics, Australian National University February 12, 2010 Implicit Functions and Their Derivatives Up till now we have only worked with functions in which the endogenous variables are explicit
More information