Calculus of Variations for Integrals Depending on a Convolution Product
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Vector Measures with Variation in a Banach Function Space
VECTOR MEASURES WITH VARIATION IN A BANACH FUNCTION SPACE O. BLASCO Departamento de An´alisis Matem´atico, Universidad de Valencia, Doctor Moliner, 50 E-46100 Burjassot (Valencia), Spain E-mail:[email protected] PABLO GREGORI Departament de Matem`atiques, Universitat Jaume I de Castell´o, Campus Riu Sec E-12071 Castell´o de la Plana (Castell´o), Spain E-mail:[email protected] Let E be a Banach function space and X be an arbitrary Banach space. Denote by E(X) the K¨othe-Bochner function space defined as the set of measurable functions f :Ω→ X such that the nonnegative functions fX :Ω→ [0, ∞) are in the lattice E. The notion of E-variation of a measure —which allows to recover the p- variation (for E = Lp), Φ-variation (for E = LΦ) and the general notion introduced by Gresky and Uhl— is introduced. The space of measures of bounded E-variation VE (X) is then studied. It is shown, amongother thingsand with some restriction ∗ ∗ of absolute continuity of the norms, that (E(X)) = VE (X ), that VE (X) can be identified with space of cone absolutely summingoperators from E into X and that E(X)=VE (X) if and only if X has the RNP property. 1. Introduction The concept of variation in the frame of vector measures has been fruitful in several areas of the functional analysis, such as the description of the duality of vector-valued function spaces such as certain K¨othe-Bochner function spaces (Gretsky and Uhl10, Dinculeanu7), the reformulation of operator ideals such as the cone absolutely summing operators (Blasco4), and the Hardy spaces of harmonic function (Blasco2,3). -
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 ........................ -
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. -
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. -
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. -
Chapter 2. Functions of Bounded Variation §1. Monotone Functions
Chapter 2. Functions of Bounded Variation §1. Monotone Functions Let I be an interval in IR. A function f : I → IR is said to be increasing (strictly increasing) if f(x) ≤ f(y)(f(x) < f(y)) whenever x, y ∈ I and x < y. A function f : I → IR is said to be decreasing (strictly decreasing) if f(x) ≥ f(y)(f(x) > f(y)) whenever x, y ∈ I and x < y. A function f : I → IR is said to be monotone if f is either increasing or decreasing. In this section we will show that a monotone function is differentiable almost every- where. For this purpose, we first establish Vitali covering lemma. Let E be a subset of IR, and let Γ be a collection of closed intervals in IR. We say that Γ covers E in the sense of Vitali if for each δ > 0 and each x ∈ E, there exists an interval I ∈ Γ such that x ∈ I and `(I) < δ. Theorem 1.1. Let E be a subset of IR with λ∗(E) < ∞ and Γ a collection of closed intervals that cover E in the sense of Vitali. Then, for given ε > 0, there exists a finite disjoint collection {I1,...,IN } of intervals in Γ such that ∗ N λ E \ ∪n=1In < ε. Proof. Let G be an open set containing E such that λ(G) < ∞. Since Γ is a Vitali covering of E, we may assume that each I ∈ Γ is contained in G. We choose a sequence (In)n=1,2,... of disjoint intervals from Γ recursively as follows. -
Section 3.5 of Folland’S Text, Which Covers Functions of Bounded Variation on the Real Line and Related Topics
REAL ANALYSIS LECTURE NOTES: 3.5 FUNCTIONS OF BOUNDED VARIATION CHRISTOPHER HEIL 3.5.1 Definition and Basic Properties of Functions of Bounded Variation We will expand on the first part of Section 3.5 of Folland's text, which covers functions of bounded variation on the real line and related topics. We begin with functions defined on finite closed intervals in R (note that Folland's ap- proach and notation is slightly different, as he begins with functions defined on R and uses TF (x) instead of our V [f; a; b]). Definition 1. Let f : [a; b] ! C be given. Given any finite partition Γ = fa = x0 < · · · < xn = bg of [a; b], set n SΓ = jf(xi) − f(xi−1)j: i=1 X The variation of f over [a; b] is V [f; a; b] = sup SΓ : Γ is a partition of [a; b] : The function f has bounded variation on [a; b] if V [f; a; b] < 1. We set BV[a; b] = f : [a; b] ! C : f has bounded variation on [a; b] : Note that in this definition we are considering f to be defined at all poin ts, and not just to be an equivalence class of functions that are equal a.e. The idea of the variation of f is that is represents the total vertical distance traveled by a particle that moves along the graph of f from (a; f(a)) to (b; f(b)). Exercise 2. (a) Show that if f : [a; b] ! C, then V [f; a; b] ≥ jf(b) − f(a)j. -
Measure Algebras and Functions of Bounded Variation on Idempotent Semigroupso
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 163, January 1972 MEASURE ALGEBRAS AND FUNCTIONS OF BOUNDED VARIATION ON IDEMPOTENT SEMIGROUPSO BY STEPHEN E. NEWMAN Abstract. Our main result establishes an isomorphism between all functions on an idempotent semigroup S with identity, under the usual addition and multiplication, and all finitely additive measures on a certain Boolean algebra of subsets of S, under the usual addition and a convolution type multiplication. Notions of a function of bounded variation on 5 and its variation norm are defined in such a way that the above isomorphism, restricted to the functions of bounded variation, is an isometry onto the set of all bounded measures. Our notion of a function of bounded variation is equivalent to the classical notion in case S is the unit interval and the "product" of two numbers in S is their maximum. 1. Introduction. This paper is motivated by the pair of closely related Banach algebras described below. The interval [0, 1] is an idempotent semigroup when endowed with the operation of maximum multiplication (.vj = max (x, y) for all x, y in [0, 1]). We let A denote the Boolean algebra (or set algebra) consisting of all finite unions of left-open, right-closed intervals (including the single point 0) contained in the interval [0, 1]. The space M(A) of all bounded, finitely additive measures on A is a Banach space with the usual total variation norm. The nature of the set algebra A and the idempotent multiplication given the interval [0, 1] make it possible to define a convolution multiplication in M(A) which makes it a Banach algebra. -
On the Convolution of Functions of Λp – Bounded Variation and a Locally
IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728,p-ISSN: 2319-765X, Volume 8, Issue 3 (Sep. - Oct. 2013), PP 70-74 www.iosrjournals.org On the Convolution of Functions of p – Bounded Variation and a Locally Compact Hausdorff Topological Group S. M. Nengem1, D. Samaila1 and M. Solomon1 1 Department of Mathematical Sciences, Adamawa State University, P. M. B. 25, Mubi, Nigeria Abstract: The smoothness-increasing operator “convolution” is well known for inheriting the best properties of each parent function. It is also well known that if f L1 and g is a Bounded Variation (BV) function, then f g inherits the properties from the parent’s spaces. This aspect of BV can be generalized in many ways and many generalizations are obtained. However, in this paper we introduce the notion of p – Bounded variation function. In relation to that we show that the convolution of two functions f g is the inverse Fourier transforms of the two functions. Moreover, we prove that if f, g BV(p)[0, 2], then f g BV(p)[0, 2], and that on any locally compact Abelian group, a version of the convolution theorem holds. Keywords: Convolution, p-Bounded variation, Fourier transform, Abelian group. I. Introduction In mathematics and, in particular, functional analysis, convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modified version of one of the original functions, giving the area overlap between the two functions as a function of the amount that one of the original functions is translated. -
Fonctions on Bounded Variations in Hilbert Spaces
Fonctions on bounded variations in Hilbert spaces Giuseppe Da Prato (SNS, Pisa) Newton Institute, March 31, 2010 Giuseppe Da Prato (SNS, Pisa) Fonctions on bounded variations in Hilbert spaces Introduction We recall that a function u : Rn ! R is said to be of bounded variation (BV) if there exists an n-dimensional vector measure Du with finite total variation such that Z Z 1 u(x)divF(x)dx = − hF(x); Du(dx)i; 8 F 2 C0 (H; H): H H The set of all BV functions is denoted by BV (Rn). Moreover, the following result holds, see E. De Giorgi, Ann. Mar. Pura Appl. 1954. Giuseppe Da Prato (SNS, Pisa) Fonctions on bounded variations in Hilbert spaces Theorem 1 Letu 2 L1(Rn). Then (i) , (ii). (i)u 2 BV (Rn). (ii) We have Z lim jDTt u(x)jdx < 1; (1) t!0 H whereT t is the heat semigroup Z 2 1 − jx−yj Tt u(x) := p e 4t u(y)dy: n 4πt R Giuseppe Da Prato (SNS, Pisa) Fonctions on bounded variations in Hilbert spaces As well known functions from BV (Rn) arise in many mathematical problems as for instance: finite perimeter sets, surface integrals, variational problems in different models in elasto plasticity, image segmentation, and so on. Recently there is also an increasing interest in studyingBV functions in general Banach or Hilbert spaces in order to extend the concepts above in an infinite dimensional situation. Giuseppe Da Prato (SNS, Pisa) Fonctions on bounded variations in Hilbert spaces A definition of BV function in an abstract Wiener space, using a Gaussian measure µ and the corresponding Dirichlet form, has been given by M. -
FUNCTIONS of BOUNDED VARIATION 1. Introduction in This Paper We Discuss Functions of Bounded Variation and Three Related Topics
FUNCTIONS OF BOUNDED VARIATION NOELLA GRADY Abstract. In this paper we explore functions of bounded variation. We discuss properties of functions of bounded variation and consider three re- lated topics. The related topics are absolute continuity, arc length, and the Riemann-Stieltjes integral. 1. Introduction In this paper we discuss functions of bounded variation and three related topics. We begin by defining the variation of a function and what it means for a function to be of bounded variation. We then develop some properties of functions of bounded variation. We consider algebraic properties as well as more abstract properties such as realizing that every function of bounded variation can be written as the difference of two increasing functions. After we have discussed some of the properties of functions of bounded variation, we consider three related topics. We begin with absolute continuity. We show that all absolutely continuous functions are of bounded variation, however, not all continuous functions of bounded variation are absolutely continuous. The Cantor Ternary function provides a counter example. The second related topic we consider is arc length. Here we show that a curve has a finite length if and only if it is of bounded variation. The third related topic that we examine is Riemann-Stieltjes integration. We examine the definition of the Riemann-Stieltjes integral and see when functions of bounded variation are Riemann-Stieltjes integrable. 2. Functions of Bounded Variation Before we can define functions of bounded variation, we must lay some ground work. We begin with a discussion of upper bounds and then define partition.