Calculus of Variations Lecture Notes Riccardo Cristoferi May 9 2016
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33 .1 Implicit Differentiation 33.1.1Implicit Function
Module 11 : Partial derivatives, Chain rules, Implicit differentiation, Gradient, Directional derivatives Lecture 33 : Implicit differentiation [Section 33.1] Objectives In this section you will learn the following : The concept of implicit differentiation of functions. 33 .1 Implicit differentiation \ As we had observed in section , many a times a function of an independent variable is not given explicitly, but implicitly by a relation. In section we had also mentioned about the implicit function theorem. We state it precisely now, without proof. 33.1.1Implicit Function Theorem (IFT): Let and be such that the following holds: (i) Both the partial derivatives and exist and are continuous in . (ii) and . Then there exists some and a function such that is differentiable, its derivative is continuous with and 33.1.2Remark: We have a corresponding version of the IFT for solving in terms of . Here, the hypothesis would be . 33.1.3Example: Let we want to know, when does the implicit expression defines explicitly as a function of . We note that and are both continuous. Since for the points and the implicit function theorem is not applicable. For , and , the equation defines the explicit function and for , the equation defines the explicit function Figure 1. y is a function of x. A result similar to that of theorem holds for function of three variables, as stated next. Theorem : 33.1.4 Let and be such that (i) exist and are continuous at . (ii) and . Then the equation determines a unique function in the neighborhood of such that for , and , Practice Exercises : Show that the following functions satisfy conditions of the implicit function theorem in the neighborhood of (1) the indicated point. -
CALCULUS of VARIATIONS and TENSOR CALCULUS
CALCULUS OF VARIATIONS and TENSOR CALCULUS U. H. Gerlach September 22, 2019 Beta Edition 2 Contents 1 FUNDAMENTAL IDEAS 5 1.1 Multivariable Calculus as a Prelude to the Calculus of Variations. 5 1.2 Some Typical Problems in the Calculus of Variations. ...... 6 1.3 Methods for Solving Problems in Calculus of Variations. ....... 10 1.3.1 MethodofFiniteDifferences. 10 1.4 TheMethodofVariations. 13 1.4.1 Variants and Variations . 14 1.4.2 The Euler-Lagrange Equation . 17 1.4.3 Variational Derivative . 20 1.4.4 Euler’s Differential Equation . 21 1.5 SolvedExample.............................. 24 1.6 Integration of Euler’s Differential Equation. ...... 25 2 GENERALIZATIONS 33 2.1 Functional with Several Unknown Functions . 33 2.2 Extremum Problem with Side Conditions. 38 2.2.1 HeuristicSolution. 40 2.2.2 Solution via Constraint Manifold . 42 2.2.3 Variational Problems with Finite Constraints . 54 2.3 Variable End Point Problem . 55 2.3.1 Extremum Principle at a Moment of Time Symmetry . 57 2.4 Generic Variable Endpoint Problem . 60 2.4.1 General Variations in the Functional . 62 2.4.2 Transversality Conditions . 64 2.4.3 Junction Conditions . 66 2.5 ManyDegreesofFreedom . 68 2.6 Parametrization Invariant Problem . 70 2.6.1 Parametrization Invariance via Homogeneous Function .... 71 2.7 Variational Principle for a Geodesic . 72 2.8 EquationofGeodesicMotion . 76 2.9 Geodesics: TheirParametrization.. 77 3 4 CONTENTS 2.9.1 Parametrization Invariance. 77 2.9.2 Parametrization in Terms of Curve Length . 78 2.10 Physical Significance of the Equation for a Geodesic . ....... 80 2.10.1 Freefloatframe ........................ -
HARMONIC FUNCTIONS for BOUNDED and UNBOUNDED P(X)
UNIVERSITY OF JYVASKYL¨ A¨ UNIVERSITAT¨ JYVASKYL¨ A¨ DEPARTMENT OF MATHEMATICS INSTITUT FUR¨ MATHEMATIK AND STATISTICS UND STATISTIK REPORT 130 BERICHT 130 EXISTENCE AND UNIQUENESS OF p(x)-HARMONIC FUNCTIONS FOR BOUNDED AND UNBOUNDED p(x) JUKKA KEISALA JYVASKYL¨ A¨ 2011 UNIVERSITY OF JYVASKYL¨ A¨ UNIVERSITAT¨ JYVASKYL¨ A¨ DEPARTMENT OF MATHEMATICS INSTITUT FUR¨ MATHEMATIK AND STATISTICS UND STATISTIK REPORT 130 BERICHT 130 EXISTENCE AND UNIQUENESS OF p(x)-HARMONIC FUNCTIONS FOR BOUNDED AND UNBOUNDED p(x) JUKKA KEISALA JYVASKYL¨ A¨ 2011 Editor: Pekka Koskela Department of Mathematics and Statistics P.O. Box 35 (MaD) FI{40014 University of Jyv¨askyl¨a Finland ISBN 978-951-39-4299-1 ISSN 1457-8905 Copyright c 2011, Jukka Keisala and University of Jyv¨askyl¨a University Printing House Jyv¨askyl¨a2011 Contents 0 Introduction 3 0.1 Notation and prerequisities . 6 1 Constant p, 1 < p < 1 8 1.1 The direct method of calculus of variations . 10 1.2 Dirichlet energy integral . 10 2 Infinity harmonic functions, p ≡ 1 13 2.1 Existence of solutions . 14 2.2 Uniqueness of solutions . 18 2.3 Minimizing property and related topics . 22 3 Variable p(x), with 1 < inf p(x) < sup p(x) < +1 24 4 Variable p(x) with p(·) ≡ +1 in a subdomain 30 4.1 Approximate solutions uk ...................... 32 4.2 Passing to the limit . 39 5 One-dimensional case, where p is continuous and sup p(x) = +1 45 5.1 Discussion . 45 5.2 Preliminary results . 47 5.3 Measure of fp(x) = +1g is positive . 48 6 Appendix 51 References 55 0 Introduction In this licentiate thesis we study the Dirichlet boundary value problem ( −∆ u(x) = 0; if x 2 Ω; p(x) (0.1) u(x) = f(x); if x 2 @Ω: Here Ω ⊂ Rn is a bounded domain, p :Ω ! (1; 1] a measurable function, f : @Ω ! R the boundary data, and −∆p(x)u(x) is the p(x)-Laplace operator, which is written as p(x)−2 −∆p(x)u(x) = − div jru(x)j ru(x) for finite p(x). -
Introduction to the Modern Calculus of Variations
MA4G6 Lecture Notes Introduction to the Modern Calculus of Variations Filip Rindler Spring Term 2015 Filip Rindler Mathematics Institute University of Warwick Coventry CV4 7AL United Kingdom [email protected] http://www.warwick.ac.uk/filiprindler Copyright ©2015 Filip Rindler. Version 1.1. Preface These lecture notes, written for the MA4G6 Calculus of Variations course at the University of Warwick, intend to give a modern introduction to the Calculus of Variations. I have tried to cover different aspects of the field and to explain how they fit into the “big picture”. This is not an encyclopedic work; many important results are omitted and sometimes I only present a special case of a more general theorem. I have, however, tried to strike a balance between a pure introduction and a text that can be used for later revision of forgotten material. The presentation is based around a few principles: • The presentation is quite “modern” in that I use several techniques which are perhaps not usually found in an introductory text or that have only recently been developed. • For most results, I try to use “reasonable” assumptions, not necessarily minimal ones. • When presented with a choice of how to prove a result, I have usually preferred the (in my opinion) most conceptually clear approach over more “elementary” ones. For example, I use Young measures in many instances, even though this comes at the expense of a higher initial burden of abstract theory. • Wherever possible, I first present an abstract result for general functionals defined on Banach spaces to illustrate the general structure of a certain result. -
Integrated Calculus/Pre-Calculus
Furman University Department of Mathematics Greenville, SC, 29613 Integrated Calculus/Pre-Calculus Abstract Mark R. Woodard Contents JJ II J I Home Page Go Back Close Quit Abstract This work presents the traditional material of calculus I with some of the material from a traditional precalculus course interwoven throughout the discussion. Pre- calculus topics are discussed at or soon before the time they are needed, in order to facilitate the learning of the calculus material. Miniature animated demonstra- tions and interactive quizzes will be available to help the reader deepen his or her understanding of the material under discussion. Integrated This project is funded in part by the Mellon Foundation through the Mellon Furman- Calculus/Pre-Calculus Wofford Program. Many of the illustrations were designed by Furman undergraduate Mark R. Woodard student Brian Wagner using the PSTricks package. Thanks go to my wife Suzan, and my two daughters, Hannah and Darby for patience while this project was being produced. Title Page Contents JJ II J I Go Back Close Quit Page 2 of 191 Contents 1 Functions and their Properties 11 1.1 Functions & Cartesian Coordinates .................. 13 1.1.1 Functions and Functional Notation ............ 13 1.2 Cartesian Coordinates and the Graphical Representa- tion of Functions .................................. 20 1.3 Circles, Distances, Completing the Square ............ 22 1.3.1 Distance Formula and Circles ................. 22 Integrated 1.3.2 Completing the Square ...................... 23 Calculus/Pre-Calculus 1.4 Lines ............................................ 24 Mark R. Woodard 1.4.1 General Equation and Slope-Intercept Form .... 24 1.4.2 More On Slope ............................. 24 1.4.3 Parallel and Perpendicular Lines ............. -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
Calculus of Variations
MIT OpenCourseWare http://ocw.mit.edu 16.323 Principles of Optimal Control Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 16.323 Lecture 5 Calculus of Variations • Calculus of Variations • Most books cover this material well, but Kirk Chapter 4 does a particularly nice job. • See here for online reference. x(t) x*+ αδx(1) x*- αδx(1) x* αδx(1) −αδx(1) t t0 tf Figure by MIT OpenCourseWare. Spr 2008 16.323 5–1 Calculus of Variations • Goal: Develop alternative approach to solve general optimization problems for continuous systems – variational calculus – Formal approach will provide new insights for constrained solutions, and a more direct path to the solution for other problems. • Main issue – General control problem, the cost is a function of functions x(t) and u(t). � tf min J = h(x(tf )) + g(x(t), u(t), t)) dt t0 subject to x˙ = f(x, u, t) x(t0), t0 given m(x(tf ), tf ) = 0 – Call J(x(t), u(t)) a functional. • Need to investigate how to find the optimal values of a functional. – For a function, we found the gradient, and set it to zero to find the stationary points, and then investigated the higher order derivatives to determine if it is a maximum or minimum. – Will investigate something similar for functionals. June 18, 2008 Spr 2008 16.323 5–2 • Maximum and Minimum of a Function – A function f(x) has a local minimum at x� if f(x) ≥ f(x �) for all admissible x in �x − x�� ≤ � – Minimum can occur at (i) stationary point, (ii) at a boundary, or (iii) a point of discontinuous derivative. -
Calculus of Variations Raju K George, IIST
Calculus of Variations Raju K George, IIST Lecture-1 In Calculus of Variations, we will study maximum and minimum of a certain class of functions. We first recall some maxima/minima results from the classical calculus. Maxima and Minima Let X and Y be two arbitrary sets and f : X Y be a well-defined function having domain → X and range Y . The function values f(x) become comparable if Y is IR or a subset of IR. Thus, optimization problem is valid for real valued functions. Let f : X IR be a real valued → function having X as its domain. Now x X is said to be maximum point for the function f if 0 ∈ f(x ) f(x) x X. The value f(x ) is called the maximum value of f. Similarly, x X is 0 ≥ ∀ ∈ 0 0 ∈ said to be a minimum point for the function f if f(x ) f(x) x X and in this case f(x ) is 0 ≤ ∀ ∈ 0 the minimum value of f. Sufficient condition for having maximum and minimum: Theorem (Weierstrass Theorem) Let S IR and f : S IR be a well defined function. Then f will have a maximum/minimum ⊆ → under the following sufficient conditions. 1. f : S IR is a continuous function. → 2. S IR is a bound and closed (compact) subset of IR. ⊂ Note that the above conditions are just sufficient conditions but not necessary. Example 1: Let f : [ 1, 1] IR defined by − → 1 x = 0 f(x)= − x x = 0 | | 6 1 −1 +1 −1 Obviously f(x) is not continuous at x = 0. -
Berestycki, Introduction to the Gaussian Free Field and Liouville
Introduction to the Gaussian Free Field and Liouville Quantum Gravity Nathanaël Berestycki [Draft lecture notes: July 19, 2016] 1 Contents 1 Definition and properties of the GFF6 1.1 Discrete case∗ ...................................6 1.2 Green function..................................8 1.3 GFF as a stochastic process........................... 10 1.4 GFF as a random distribution: Dirichlet energy∗ ................ 13 1.5 Markov property................................. 15 1.6 Conformal invariance............................... 16 1.7 Circle averages.................................. 16 1.8 Thick points.................................... 18 1.9 Exercises...................................... 19 2 Liouville measure 22 2.1 Preliminaries................................... 23 2.2 Convergence and uniform integrability in the L2 phase............ 24 2.3 The GFF viewed from a Liouville typical point................. 25 2.4 General case∗ ................................... 27 2.5 The phase transition in Gaussian multiplicative chaos............. 29 2.6 Conformal covariance............................... 29 2.7 Random surfaces................................. 32 2.8 Exercises...................................... 32 3 The KPZ relation 34 3.1 Scaling exponents; KPZ theorem........................ 34 3.2 Applications of KPZ to exponents∗ ....................... 36 3.3 Proof in the case of expected Minkowski dimension.............. 37 3.4 Sketch of proof of Hausdorff KPZ using circle averages............ 39 3.5 Proof of multifractal spectrum of Liouville -
Implicit Differentiation
opes. A Neurips 2020 Tut orial by Marc Deisenro A Swamps of integrationnumerical b Monte Carlo Heights C Fo r w w m a r o al r N izi ng Flo d -ba h ckw ime ar Of T d D B ea c h Hills There E Unrolling Backprop Bay f Expectations and Sl and fExpectations B ack Again: A Tale o Tale A Again: ack and th and opes. A Neurips 2020 Tut orial by Marc Deisenro A Swamps of integrationnumerical b Monte Carlo Heights C Fo r w Implicit of Vale w m a r o al r N izi ng Flo d -ba h G ckw ime ar Of T d D B Differentiation ea c h Hills There E Unrolling Backprop Bay f Expectations and Sl and fExpectations B ack Again: A Tale o Tale A Again: ack and th and Implicit differentiation Cheng Soon Ong Marc Peter Deisenroth December 2020 oon n S O g g ’s n Tu e t hC o d r n C i a a h l t M a o t a M r r n c e D e s i Motivation I In machine learning, we use gradients to train predictors I For functions f(x) we can directly obtain its gradient rf(x) I How to represent a constraint? G(x; y) = 0 E.g. conservation of mass 1 dy = 3x2 + 4x + 1 dx Observe that we can write the equation as x3 + 2x2 + x + 4 − y = 0 which is of the form G(x; y) = 0. -
Calculus of Variations
Calculus of Variations The biggest step from derivatives with one variable to derivatives with many variables is from one to two. After that, going from two to three was just more algebra and more complicated pictures. Now the step will be from a finite number of variables to an infinite number. That will require a new set of tools, yet in many ways the techniques are not very different from those you know. If you've never read chapter 19 of volume II of the Feynman Lectures in Physics, now would be a good time. It's a classic introduction to the area. For a deeper look at the subject, pick up MacCluer's book referred to in the Bibliography at the beginning of this book. 16.1 Examples What line provides the shortest distance between two points? A straight line of course, no surprise there. But not so fast, with a few twists on the question the result won't be nearly as obvious. How do I measure the length of a curved (or even straight) line? Typically with a ruler. For the curved line I have to do successive approximations, breaking the curve into small pieces and adding the finite number of lengths, eventually taking a limit to express the answer as an integral. Even with a straight line I will do the same thing if my ruler isn't long enough. Put this in terms of how you do the measurement: Go to a local store and purchase a ruler. It's made out of some real material, say brass. -
Notes on Calculus and Optimization
Economics 101A Section Notes GSI: David Albouy Notes on Calculus and Optimization 1 Basic Calculus 1.1 Definition of a Derivative Let f (x) be some function of x, then the derivative of f, if it exists, is given by the following limit df (x) f (x + h) f (x) = lim − (Definition of Derivative) dx h 0 h → df although often this definition is hard to apply directly. It is common to write f 0 (x),or dx to be shorter, or dy if y = f (x) then dx for the derivative of y with respect to x. 1.2 Calculus Rules Here are some handy formulas which can be derived using the definitions, where f (x) and g (x) are functions of x and k is a constant which does not depend on x. d df (x) [kf (x)] = k (Constant Rule) dx dx d df (x) df (x) [f (x)+g (x)] = + (Sum Rule) dx dx dy d df (x) dg (x) [f (x) g (x)] = g (x)+f (x) (Product Rule) dx dx dx d f (x) df (x) g (x) dg(x) f (x) = dx − dx (Quotient Rule) dx g (x) [g (x)]2 · ¸ d f [g (x)] dg (x) f [g (x)] = (Chain Rule) dx dg dx For specific forms of f the following rules are useful in economics d xk = kxk 1 (Power Rule) dx − d ex = ex (Exponent Rule) dx d 1 ln x = (Logarithm Rule) dx x 1 Finally assuming that we can invert y = f (x) by solving for x in terms of y so that x = f − (y) then the following rule applies 1 df − (y) 1 = 1 (Inverse Rule) dy df (f − (y)) dx Example 1 Let y = f (x)=ex/2, then using the exponent rule and the chain rule, where g (x)=x/2,we df (x) d x/2 d x/2 d x x/2 1 1 x/2 get dx = dx e = d(x/2) e dx 2 = e 2 = 2 e .