A First Course in the Calculus of Variations
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Computational Thermodynamics: a Mature Scientific Tool for Industry and Academia*
Pure Appl. Chem., Vol. 83, No. 5, pp. 1031–1044, 2011. doi:10.1351/PAC-CON-10-12-06 © 2011 IUPAC, Publication date (Web): 4 April 2011 Computational thermodynamics: A mature scientific tool for industry and academia* Klaus Hack GTT Technologies, Kaiserstrasse 100, D-52134 Herzogenrath, Germany Abstract: The paper gives an overview of the general theoretical background of computa- tional thermochemistry as well as recent developments in the field, showing special applica- tion cases for real world problems. The established way of applying computational thermo- dynamics is the use of so-called integrated thermodynamic databank systems (ITDS). A short overview of the capabilities of such an ITDS is given using FactSage as an example. However, there are many more applications that go beyond the closed approach of an ITDS. With advanced algorithms it is possible to include explicit reaction kinetics as an additional constraint into the method of complex equilibrium calculations. Furthermore, a method of interlinking a small number of local equilibria with a system of materials and energy streams has been developed which permits a thermodynamically based approach to process modeling which has proven superior to detailed high-resolution computational fluid dynamic models in several cases. Examples for such highly developed applications of computational thermo- dynamics will be given. The production of metallurgical grade silicon from silica and carbon will be used to demonstrate the application of several calculation methods up to a full process model. Keywords: complex equilibria; Gibbs energy; phase diagrams; process modeling; reaction equilibria; thermodynamics. INTRODUCTION The concept of using Gibbsian thermodynamics as an approach to tackle problems of industrial or aca- demic background is not new at all. -
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 ........................ -
Covariant Hamiltonian Field Theory 3
December 16, 2020 2:58 WSPC/INSTRUCTION FILE kfte COVARIANT HAMILTONIAN FIELD THEORY JURGEN¨ STRUCKMEIER and ANDREAS REDELBACH GSI Helmholtzzentrum f¨ur Schwerionenforschung GmbH Planckstr. 1, 64291 Darmstadt, Germany and Johann Wolfgang Goethe-Universit¨at Frankfurt am Main Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany [email protected] Received 18 July 2007 Revised 14 December 2020 A consistent, local coordinate formulation of covariant Hamiltonian field theory is pre- sented. Whereas the covariant canonical field equations are equivalent to the Euler- Lagrange field equations, the covariant canonical transformation theory offers more gen- eral means for defining mappings that preserve the form of the field equations than the usual Lagrangian description. It is proved that Poisson brackets, Lagrange brackets, and canonical 2-forms exist that are invariant under canonical transformations of the fields. The technique to derive transformation rules for the fields from generating functions is demonstrated by means of various examples. In particular, it is shown that the infinites- imal canonical transformation furnishes the most general form of Noether’s theorem. We furthermore specify the generating function of an infinitesimal space-time step that conforms to the field equations. Keywords: Field theory; Hamiltonian density; covariant. PACS numbers: 11.10.Ef, 11.15Kc arXiv:0811.0508v6 [math-ph] 15 Dec 2020 1. Introduction Relativistic field theories and gauge theories are commonly formulated on the basis of a Lagrangian density L1,2,3,4. The space-time evolution of the fields is obtained by integrating the Euler-Lagrange field equations that follow from the four-dimensional representation of Hamilton’s action principle. -
1 Approximating Integrals Using Taylor Polynomials 1 1.1 Definitions
Seunghee Ye Ma 8: Week 7 Nov 10 Week 7 Summary This week, we will learn how we can approximate integrals using Taylor series and numerical methods. Topics Page 1 Approximating Integrals using Taylor Polynomials 1 1.1 Definitions . .1 1.2 Examples . .2 1.3 Approximating Integrals . .3 2 Numerical Integration 5 1 Approximating Integrals using Taylor Polynomials 1.1 Definitions When we first defined the derivative, recall that it was supposed to be the \instantaneous rate of change" of a function f(x) at a given point c. In other words, f 0 gives us a linear approximation of f(x) near c: for small values of " 2 R, we have f(c + ") ≈ f(c) + "f 0(c) But if f(x) has higher order derivatives, why stop with a linear approximation? Taylor series take this idea of linear approximation and extends it to higher order derivatives, giving us a better approximation of f(x) near c. Definition (Taylor Polynomial and Taylor Series) Let f(x) be a Cn function i.e. f is n-times continuously differentiable. Then, the n-th order Taylor polynomial of f(x) about c is: n X f (k)(c) T (f)(x) = (x − c)k n k! k=0 The n-th order remainder of f(x) is: Rn(f)(x) = f(x) − Tn(f)(x) If f(x) is C1, then the Taylor series of f(x) about c is: 1 X f (k)(c) T (f)(x) = (x − c)k 1 k! k=0 Note that the first order Taylor polynomial of f(x) is precisely the linear approximation we wrote down in the beginning. -
A Quotient Rule Integration by Parts Formula Jennifer Switkes ([email protected]), California State Polytechnic Univer- Sity, Pomona, CA 91768
A Quotient Rule Integration by Parts Formula Jennifer Switkes ([email protected]), California State Polytechnic Univer- sity, Pomona, CA 91768 In a recent calculus course, I introduced the technique of Integration by Parts as an integration rule corresponding to the Product Rule for differentiation. I showed my students the standard derivation of the Integration by Parts formula as presented in [1]: By the Product Rule, if f (x) and g(x) are differentiable functions, then d f (x)g(x) = f (x)g(x) + g(x) f (x). dx Integrating on both sides of this equation, f (x)g(x) + g(x) f (x) dx = f (x)g(x), which may be rearranged to obtain f (x)g(x) dx = f (x)g(x) − g(x) f (x) dx. Letting U = f (x) and V = g(x) and observing that dU = f (x) dx and dV = g(x) dx, we obtain the familiar Integration by Parts formula UdV= UV − VdU. (1) My student Victor asked if we could do a similar thing with the Quotient Rule. While the other students thought this was a crazy idea, I was intrigued. Below, I derive a Quotient Rule Integration by Parts formula, apply the resulting integration formula to an example, and discuss reasons why this formula does not appear in calculus texts. By the Quotient Rule, if f (x) and g(x) are differentiable functions, then ( ) ( ) ( ) − ( ) ( ) d f x = g x f x f x g x . dx g(x) [g(x)]2 Integrating both sides of this equation, we get f (x) g(x) f (x) − f (x)g(x) = dx. -
Operations on Power Series Related to Taylor Series
Operations on Power Series Related to Taylor Series In this problem, we perform elementary operations on Taylor series – term by term differen tiation and integration – to obtain new examples of power series for which we know their sum. Suppose that a function f has a power series representation of the form: 1 2 X n f(x) = a0 + a1(x − c) + a2(x − c) + · · · = an(x − c) n=0 convergent on the interval (c − R; c + R) for some R. The results we use in this example are: • (Differentiation) Given f as above, f 0(x) has a power series expansion obtained by by differ entiating each term in the expansion of f(x): 1 0 X n−1 f (x) = a1 + a2(x − c) + 2a3(x − c) + · · · = nan(x − c) n=1 • (Integration) Given f as above, R f(x) dx has a power series expansion obtained by by inte grating each term in the expansion of f(x): 1 Z a1 a2 X an f(x) dx = C + a (x − c) + (x − c)2 + (x − c)3 + · · · = C + (x − c)n+1 0 2 3 n + 1 n=0 for some constant C depending on the choice of antiderivative of f. Questions: 1. Find a power series representation for the function f(x) = arctan(5x): (Note: arctan x is the inverse function to tan x.) 2. Use power series to approximate Z 1 2 sin(x ) dx 0 (Note: sin(x2) is a function whose antiderivative is not an elementary function.) Solution: 1 For question (1), we know that arctan x has a simple derivative: , which then has a power 1 + x2 1 2 series representation similar to that of , where we subsitute −x for 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. -
Thermodynamics
ME346A Introduction to Statistical Mechanics { Wei Cai { Stanford University { Win 2011 Handout 6. Thermodynamics January 26, 2011 Contents 1 Laws of thermodynamics 2 1.1 The zeroth law . .3 1.2 The first law . .4 1.3 The second law . .5 1.3.1 Efficiency of Carnot engine . .5 1.3.2 Alternative statements of the second law . .7 1.4 The third law . .8 2 Mathematics of thermodynamics 9 2.1 Equation of state . .9 2.2 Gibbs-Duhem relation . 11 2.2.1 Homogeneous function . 11 2.2.2 Virial theorem / Euler theorem . 12 2.3 Maxwell relations . 13 2.4 Legendre transform . 15 2.5 Thermodynamic potentials . 16 3 Worked examples 21 3.1 Thermodynamic potentials and Maxwell's relation . 21 3.2 Properties of ideal gas . 24 3.3 Gas expansion . 28 4 Irreversible processes 32 4.1 Entropy and irreversibility . 32 4.2 Variational statement of second law . 32 1 In the 1st lecture, we will discuss the concepts of thermodynamics, namely its 4 laws. The most important concepts are the second law and the notion of Entropy. (reading assignment: Reif x 3.10, 3.11) In the 2nd lecture, We will discuss the mathematics of thermodynamics, i.e. the machinery to make quantitative predictions. We will deal with partial derivatives and Legendre transforms. (reading assignment: Reif x 4.1-4.7, 5.1-5.12) 1 Laws of thermodynamics Thermodynamics is a branch of science connected with the nature of heat and its conver- sion to mechanical, electrical and chemical energy. (The Webster pocket dictionary defines, Thermodynamics: physics of heat.) Historically, it grew out of efforts to construct more efficient heat engines | devices for ex- tracting useful work from expanding hot gases (http://www.answers.com/thermodynamics). -
Derivative of Power Series and Complex Exponential
LECTURE 4: DERIVATIVE OF POWER SERIES AND COMPLEX EXPONENTIAL The reason of dealing with power series is that they provide examples of analytic functions. P1 n Theorem 1. If n=0 anz has radius of convergence R > 0; then the function P1 n F (z) = n=0 anz is di®erentiable on S = fz 2 C : jzj < Rg; and the derivative is P1 n¡1 f(z) = n=0 nanz : Proof. (¤) We will show that j F (z+h)¡F (z) ¡ f(z)j ! 0 as h ! 0 (in C), whenever h ¡ ¢ n Pn n k n¡k jzj < R: Using the binomial theorem (z + h) = k=0 k h z we get F (z + h) ¡ F (z) X1 (z + h)n ¡ zn ¡ hnzn¡1 ¡ f(z) = a h n h n=0 µ ¶ X1 a Xn n = n ( hkzn¡k) h k n=0 k=2 µ ¶ X1 Xn n = a h( hk¡2zn¡k) n k n=0 k=2 µ ¶ X1 Xn¡2 n = a h( hjzn¡2¡j) (by putting j = k ¡ 2): n j + 2 n=0 j=0 ¡ n ¢ ¡n¡2¢ By using the easily veri¯able fact that j+2 · n(n ¡ 1) j ; we obtain µ ¶ F (z + h) ¡ F (z) X1 Xn¡2 n ¡ 2 j ¡ f(z)j · jhj n(n ¡ 1)ja j( jhjjjzjn¡2¡j) h n j n=0 j=0 X1 n¡2 = jhj n(n ¡ 1)janj(jzj + jhj) : n=0 P1 n¡2 We already know that the series n=0 n(n ¡ 1)janjjzj converges for jzj < R: Now, for jzj < R and h ! 0 we have jzj + jhj < R eventually. -
Finite Elements for the Treatment of the Inextensibility Constraint
Comput Mech DOI 10.1007/s00466-017-1437-9 ORIGINAL PAPER Fiber-reinforced materials: finite elements for the treatment of the inextensibility constraint Ferdinando Auricchio1 · Giulia Scalet1 · Peter Wriggers2 Received: 5 April 2017 / Accepted: 29 May 2017 © Springer-Verlag GmbH Germany 2017 Abstract The present paper proposes a numerical frame- 1 Introduction work for the analysis of problems involving fiber-reinforced anisotropic materials. Specifically, isotropic linear elastic The use of fiber-reinforcements for the development of novel solids, reinforced by a single family of inextensible fibers, and efficient materials can be traced back to the 1960s are considered. The kinematic constraint equation of inex- and, to date, it is an active research topic. Such materi- tensibility in the fiber direction leads to the presence of als are composed of a matrix, reinforced by one or more an undetermined fiber stress in the constitutive equations. families of fibers which are systematically arranged in the To avoid locking-phenomena in the numerical solution due matrix itself. The interest towards fiber-reinforced mate- to the presence of the constraint, mixed finite elements rials stems from the mechanical properties of the fibers, based on the Lagrange multiplier, perturbed Lagrangian, and which offer a significant increase in structural efficiency. penalty method are proposed. Several boundary-value prob- Particularly, these materials possess high resistance and lems under plane strain conditions are solved and numerical stiffness, despite the low specific weight, together with an results are compared to analytical solutions, whenever the anisotropic mechanical behavior determined by the fiber derivation is possible. The performed simulations allow to distribution. -
A Variational Approach to Lagrange Multipliers
JOTA manuscript No. (will be inserted by the editor) A Variational Approach to Lagrange Multipliers Jonathan M. Borwein · Qiji J. Zhu Received: date / Accepted: date Abstract We discuss Lagrange multiplier rules from a variational perspective. This allows us to highlight many of the issues involved and also to illustrate how broadly an abstract version can be applied. Keywords Lagrange multiplier Variational method Convex duality Constrained optimiza- tion Nonsmooth analysis · · · · Mathematics Subject Classification (2000) 90C25 90C46 49N15 · · 1 Introduction The Lagrange multiplier method is fundamental in dealing with constrained optimization prob- lems and is also related to many other important results. There are many different routes to reaching the fundamental result. The variational approach used in [1] provides a deep under- standing of the nature of the Lagrange multiplier rule and is the focus of this survey. David Gale's seminal paper [2] provides a penetrating explanation of the economic meaning of the Lagrange multiplier in the convex case. Consider maximizing the output of an economy with resource constraints. Then the optimal output is a function of the level of resources. It turns out the derivative of this function, if exists, is exactly the Lagrange multiplier for the constrained optimization problem. A Lagrange multiplier, then, reflects the marginal gain of the output function with respect to the vector of resource constraints. Following this observation, if we penalize the resource utilization with a (vector) Lagrange multiplier then the constrained optimization problem can be converted to an unconstrained one. One cannot emphasize enough the importance of this insight. In general, however, an optimal value function for a constrained optimization problem is nei- ther convex nor smooth. -
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