AMATH 731: Applied Functional Analysis Lecture Notes
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On the Use of Boundary Integral Equations and Linear Operators in Room Acoustics
Guimarães - Portugal paper ID: 169 /p.1 On the Use of Boundary Integral Equations and Linear Operators in Room Acoustics D. Alarcão and J. L. Bento Coelho CAPS – Instituto Superior Técnico, Av. Rovisco Pais, P-1049-001 Lisbon, Portugal, [email protected] RESUMO: Este artigo introduz alguns conceitos de base sobre equações integrais e mostra a sua aplicação na determinação da propagação de energia acústica em recintos fechados. Mostra-se que as equações integrais resultantes podem ser formuladas na linguagem dos operadores lineares, daí resultando uma notação simplificada em que as propriedades algébricas das equações que determinam a propagação da energia são mais facilmente caracterizadas. São apresentados alguns métodos gerais de resolução de equações integrais tal como métodos aproximados e métodos de bases vectoriais finitas. Apresentam-se, ainda, as definições necessárias envolvidas na descrição de campos de energia acústica, que servem de ponto de partida à aplicação das técnicas apresentadas. ABSTRACT: This paper introduces some basic concepts of boundary integral equations and their application for the determination of the propagation of sound energy inside enclosures. Linear operators are shown to provide a simplified notation and to emphasize the algebraic properties of the resulting integral equations. Some general methods of solving linear operator and integral equations are reviewed and discussed, such as approximation methods and finite basis methods. In addition, some of the necessary definitions involved in describing acoustic energy fields for applying these techniques in the field of room acoustics prediction are presented. 1. INTRODUCTION Energy based methods offer an interesting and valid alternative mid and high frequency technique to classical predictive methods such as FEM, BEM and others. -
Fundamentals of Functional Analysis Kluwer Texts in the Mathematical Sciences
Fundamentals of Functional Analysis Kluwer Texts in the Mathematical Sciences VOLUME 12 A Graduate-Level Book Series The titles published in this series are listed at the end of this volume. Fundamentals of Functional Analysis by S. S. Kutateladze Sobolev Institute ofMathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Springer-Science+Business Media, B.V. A C.I.P. Catalogue record for this book is available from the Library of Congress ISBN 978-90-481-4661-1 ISBN 978-94-015-8755-6 (eBook) DOI 10.1007/978-94-015-8755-6 Translated from OCHOBbI Ij)YHK~HOHaJIl>HODO aHaJIHsa. J/IS;l\~ 2, ;l\OIIOJIHeHHoe., Sobo1ev Institute of Mathematics, Novosibirsk, © 1995 S. S. Kutate1adze Printed on acid-free paper All Rights Reserved © 1996 Springer Science+Business Media Dordrecht Originally published by Kluwer Academic Publishers in 1996. Softcover reprint of the hardcover 1st edition 1996 No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without written permission from the copyright owner. Contents Preface to the English Translation ix Preface to the First Russian Edition x Preface to the Second Russian Edition xii Chapter 1. An Excursion into Set Theory 1.1. Correspondences . 1 1.2. Ordered Sets . 3 1.3. Filters . 6 Exercises . 8 Chapter 2. Vector Spaces 2.1. Spaces and Subspaces ... ......................... 10 2.2. Linear Operators . 12 2.3. Equations in Operators ........................ .. 15 Exercises . 18 Chapter 3. Convex Analysis 3.1. -
FUNCTIONAL ANALYSIS 1. Banach and Hilbert Spaces in What
FUNCTIONAL ANALYSIS PIOTR HAJLASZ 1. Banach and Hilbert spaces In what follows K will denote R of C. Definition. A normed space is a pair (X, k · k), where X is a linear space over K and k · k : X → [0, ∞) is a function, called a norm, such that (1) kx + yk ≤ kxk + kyk for all x, y ∈ X; (2) kαxk = |α|kxk for all x ∈ X and α ∈ K; (3) kxk = 0 if and only if x = 0. Since kx − yk ≤ kx − zk + kz − yk for all x, y, z ∈ X, d(x, y) = kx − yk defines a metric in a normed space. In what follows normed paces will always be regarded as metric spaces with respect to the metric d. A normed space is called a Banach space if it is complete with respect to the metric d. Definition. Let X be a linear space over K (=R or C). The inner product (scalar product) is a function h·, ·i : X × X → K such that (1) hx, xi ≥ 0; (2) hx, xi = 0 if and only if x = 0; (3) hαx, yi = αhx, yi; (4) hx1 + x2, yi = hx1, yi + hx2, yi; (5) hx, yi = hy, xi, for all x, x1, x2, y ∈ X and all α ∈ K. As an obvious corollary we obtain hx, y1 + y2i = hx, y1i + hx, y2i, hx, αyi = αhx, yi , Date: February 12, 2009. 1 2 PIOTR HAJLASZ for all x, y1, y2 ∈ X and α ∈ K. For a space with an inner product we define kxk = phx, xi . Lemma 1.1 (Schwarz inequality). -
Functional Analysis Lecture Notes Chapter 2. Operators on Hilbert Spaces
FUNCTIONAL ANALYSIS LECTURE NOTES CHAPTER 2. OPERATORS ON HILBERT SPACES CHRISTOPHER HEIL 1. Elementary Properties and Examples First recall the basic definitions regarding operators. Definition 1.1 (Continuous and Bounded Operators). Let X, Y be normed linear spaces, and let L: X Y be a linear operator. ! (a) L is continuous at a point f X if f f in X implies Lf Lf in Y . 2 n ! n ! (b) L is continuous if it is continuous at every point, i.e., if fn f in X implies Lfn Lf in Y for every f. ! ! (c) L is bounded if there exists a finite K 0 such that ≥ f X; Lf K f : 8 2 k k ≤ k k Note that Lf is the norm of Lf in Y , while f is the norm of f in X. k k k k (d) The operator norm of L is L = sup Lf : k k kfk=1 k k (e) We let (X; Y ) denote the set of all bounded linear operators mapping X into Y , i.e., B (X; Y ) = L: X Y : L is bounded and linear : B f ! g If X = Y = X then we write (X) = (X; X). B B (f) If Y = F then we say that L is a functional. The set of all bounded linear functionals on X is the dual space of X, and is denoted X0 = (X; F) = L: X F : L is bounded and linear : B f ! g We saw in Chapter 1 that, for a linear operator, boundedness and continuity are equivalent. -
On the Origin and Early History of Functional Analysis
U.U.D.M. Project Report 2008:1 On the origin and early history of functional analysis Jens Lindström Examensarbete i matematik, 30 hp Handledare och examinator: Sten Kaijser Januari 2008 Department of Mathematics Uppsala University Abstract In this report we will study the origins and history of functional analysis up until 1918. We begin by studying ordinary and partial differential equations in the 18th and 19th century to see why there was a need to develop the concepts of functions and limits. We will see how a general theory of infinite systems of equations and determinants by Helge von Koch were used in Ivar Fredholm’s 1900 paper on the integral equation b Z ϕ(s) = f(s) + λ K(s, t)f(t)dt (1) a which resulted in a vast study of integral equations. One of the most enthusiastic followers of Fredholm and integral equation theory was David Hilbert, and we will see how he further developed the theory of integral equations and spectral theory. The concept introduced by Fredholm to study sets of transformations, or operators, made Maurice Fr´echet realize that the focus should be shifted from particular objects to sets of objects and the algebraic properties of these sets. This led him to introduce abstract spaces and we will see how he introduced the axioms that defines them. Finally, we will investigate how the Lebesgue theory of integration were used by Frigyes Riesz who was able to connect all theory of Fredholm, Fr´echet and Lebesgue to form a general theory, and a new discipline of mathematics, now known as functional analysis. -
A Appendix: Linear Algebra and Functional Analysis
A Appendix: Linear Algebra and Functional Analysis In this appendix, we have collected some results of functional analysis and matrix algebra. Since the computational aspects are in the foreground in this book, we give separate proofs for the linear algebraic and functional analyses, although finite-dimensional spaces are special cases of Hilbert spaces. A.1 Linear Algebra A general reference concerning the results in this section is [47]. The following theorem gives the singular value decomposition of an arbitrary real matrix. Theorem A.1. Every matrix A ∈ Rm×n allows a decomposition A = UΛV T, where U ∈ Rm×m and V ∈ Rn×n are orthogonal matrices and Λ ∈ Rm×n is diagonal with nonnegative diagonal elements λj such that λ1 ≥ λ2 ≥ ··· ≥ λmin(m,n) ≥ 0. Proof: Before going to the proof, we recall that a diagonal matrix is of the form ⎡ ⎤ λ1 0 ... 00... 0 ⎢ ⎥ ⎢ . ⎥ ⎢ 0 λ2 . ⎥ Λ = = [diag(λ ,...,λm), 0] , ⎢ . ⎥ 1 ⎣ . .. ⎦ 0 ... λm 0 ... 0 if m ≤ n, and 0 denotes a zero matrix of size m×(n−m). Similarly, if m>n, Λ is of the form 312 A Appendix: Linear Algebra and Functional Analysis ⎡ ⎤ λ1 0 ... 0 ⎢ ⎥ ⎢ . ⎥ ⎢ 0 λ2 . ⎥ ⎢ ⎥ ⎢ . .. ⎥ ⎢ . ⎥ diag(λ ,...,λn) Λ = ⎢ ⎥ = 1 , ⎢ λn ⎥ ⎢ ⎥ 0 ⎢ 0 ... 0 ⎥ ⎢ . ⎥ ⎣ . ⎦ 0 ... 0 where 0 is a zero matrix of the size (m − n) × n. Briefly, we write Λ = diag(λ1,...,λmin(m,n)). n Let A = λ1, and we assume that λ1 =0.Let x ∈ R be a unit vector m with Ax = A ,andy =(1/λ1)Ax ∈ R , i.e., y is also a unit vector. We n m pick vectors v2,...,vn ∈ R and u2,...,um ∈ R such that {x, v2,...,vn} n is an orthonormal basis in R and {y,u2,...,um} is an orthonormal basis in Rm, respectively. -
Functional Analysis
Functional Analysis Individualized Behavior Intervention for Early Education Behavior Assessments • Purpose – Analyze and understand environmental factors contributing to challenging or maladaptive behaviors – Determine function of behaviors – Develop best interventions based on function – Determine best replacement behaviors to teach student Types of Behavior Assessments • Indirect assessment – Interviews and questionnaires • Direct observation/Descriptive assessment – Observe behaviors and collect data on antecedents and consequences • Functional analysis/Testing conditions – Experimental manipulations to determine function What’s the Big Deal About Function? • Function of behavior is more important than what the behavior looks like • Behaviors can serve multiple functions Why bother with testing? • Current understanding of function is not correct • Therefore, current interventions are not working Why bother with testing? • What the heck is the function?! • Observations alone have not been able to determine function Functional Analysis • Experimental manipulations and testing for function of behavior • Conditions – Test for Attention – Test for Escape – Test for Tangible – Test for Self Stimulatory – “Play” condition, which serves as the control Test for Attention • Attention or Self Stim? • http://www.youtube.com/watch? v=dETNNYxXAOc&feature=related • Ignore student, but stay near by • Pay attention each time he screams, see if behavior increases Test for Escape • Escape or attention? • http://www.youtube.com/watch? v=wb43xEVx3W0 (second -
List of Theorems from Geometry 2321, 2322
List of Theorems from Geometry 2321, 2322 Stephanie Hyland [email protected] April 24, 2010 There’s already a list of geometry theorems out there, but the course has changed since, so here’s a new one. They’re in order of ‘appearance in my notes’, which corresponds reasonably well to chronolog- ical order. 24 onwards is Hilary term stuff. The following theorems have actually been asked (in either summer or schol papers): 5, 7, 8, 17, 18, 20, 21, 22, 26, 27, 30 a), 31 (associative only), 35, 36 a), 37, 38, 39, 43, 45, 47, 48. Definitions are also asked, which aren’t included here. 1. On a finite-dimensional real vector space, the statement ‘V is open in M’ is independent of the choice of norm on M. ′ f f i 2. Let Rn ⊃ V −→ Rm be differentiable with f = (f 1, ..., f m). Then Rn −→ Rm, and f ′ = ∂f ∂xj f 3. Let M ⊃ V −→ N,a ∈ V . Then f is differentiable at a ⇒ f is continuous at a. 4. f = (f 1, ...f n) continuous ⇔ f i continuous, and same for differentiable. 5. The chain rule for functions on finite-dimensional real vector spaces. 6. The chain rule for functions of several real variables. f 7. Let Rn ⊃ V −→ R, V open. Then f is C1 ⇔ ∂f exists and is continuous, for i =1, ..., n. ∂xi 2 2 Rn f R 2 ∂ f ∂ f 8. Let ⊃ V −→ , V open. f is C . Then ∂xi∂xj = ∂xj ∂xi 9. (φ ◦ ψ)∗ = φ∗ ◦ ψ∗, where φ, ψ are maps of manifolds, and φ∗ is the push-forward of φ. -
Calculus I – Math
Math 380: Low-Dimensional Topology Instructor: Aaron Heap Office: South 330C E-mail: [email protected] Web Page: http://www.geneseo.edu/math/heap Textbook: Topology Now!, by Robert Messer & Philip Straffin. Course Info: We will cover topological equivalence, deformations, knots and links, surfaces, three- dimensional manifolds, and the fundamental group. Topics are subject to change depending on the progress of the class, and various topics may be skipped due to time constraints. An accurate reading schedule will be posted on the website, and you should check it often. By the time you take this course, most of you should be fairly comfortable with mathematical proofs. Although this course only has multivariable calculus, elementary linear algebra, and mathematical proofs as prerequisites, students are strongly encouraged to take abstract algebra (Math 330) prior to this course or concurrently. It requires a certain level of mathematical sophistication. There will be a lot of new terminology you must learn, and we will be doing a significant number of proofs. Please note that we will work on developing your independent reading skills in Mathematics and your ability to learn and use definitions and theorems. I certainly won't be able to cover in class all the material you will be required to learn. As a result, you will be expected to do a lot of reading. The reading assignments will be on topics to be discussed in the following lecture to enable you to ask focused questions in the class and to better understand the material. It is imperative that you keep up with the reading assignments. -
Fact Sheet Functional Analysis
Fact Sheet Functional Analysis Literature: Hackbusch, W.: Theorie und Numerik elliptischer Differentialgleichungen. Teubner, 1986. Knabner, P., Angermann, L.: Numerik partieller Differentialgleichungen. Springer, 2000. Triebel, H.: H¨ohere Analysis. Harri Deutsch, 1980. Dobrowolski, M.: Angewandte Funktionalanalysis, Springer, 2010. 1. Banach- and Hilbert spaces Let V be a real vector space. Normed space: A norm is a mapping k · k : V ! [0; 1), such that: kuk = 0 , u = 0; (definiteness) kαuk = jαj · kuk; α 2 R; u 2 V; (positive scalability) ku + vk ≤ kuk + kvk; u; v 2 V: (triangle inequality) The pairing (V; k · k) is called a normed space. Seminorm: In contrast to a norm there may be elements u 6= 0 such that kuk = 0. It still holds kuk = 0 if u = 0. Comparison of two norms: Two norms k · k1, k · k2 are called equivalent if there is a constant C such that: −1 C kuk1 ≤ kuk2 ≤ Ckuk1; u 2 V: If only one of these inequalities can be fulfilled, e.g. kuk2 ≤ Ckuk1; u 2 V; the norm k · k1 is called stronger than the norm k · k2. k · k2 is called weaker than k · k1. Topology: In every normed space a canonical topology can be defined. A subset U ⊂ V is called open if for every u 2 U there exists a " > 0 such that B"(u) = fv 2 V : ku − vk < "g ⊂ U: Convergence: A sequence vn converges to v w.r.t. the norm k · k if lim kvn − vk = 0: n!1 1 A sequence vn ⊂ V is called Cauchy sequence, if supfkvn − vmk : n; m ≥ kg ! 0 for k ! 1. -
Fundamental Theorems in Mathematics
SOME FUNDAMENTAL THEOREMS IN MATHEMATICS OLIVER KNILL Abstract. An expository hitchhikers guide to some theorems in mathematics. Criteria for the current list of 243 theorems are whether the result can be formulated elegantly, whether it is beautiful or useful and whether it could serve as a guide [6] without leading to panic. The order is not a ranking but ordered along a time-line when things were writ- ten down. Since [556] stated “a mathematical theorem only becomes beautiful if presented as a crown jewel within a context" we try sometimes to give some context. Of course, any such list of theorems is a matter of personal preferences, taste and limitations. The num- ber of theorems is arbitrary, the initial obvious goal was 42 but that number got eventually surpassed as it is hard to stop, once started. As a compensation, there are 42 “tweetable" theorems with included proofs. More comments on the choice of the theorems is included in an epilogue. For literature on general mathematics, see [193, 189, 29, 235, 254, 619, 412, 138], for history [217, 625, 376, 73, 46, 208, 379, 365, 690, 113, 618, 79, 259, 341], for popular, beautiful or elegant things [12, 529, 201, 182, 17, 672, 673, 44, 204, 190, 245, 446, 616, 303, 201, 2, 127, 146, 128, 502, 261, 172]. For comprehensive overviews in large parts of math- ematics, [74, 165, 166, 51, 593] or predictions on developments [47]. For reflections about mathematics in general [145, 455, 45, 306, 439, 99, 561]. Encyclopedic source examples are [188, 705, 670, 102, 192, 152, 221, 191, 111, 635]. -
Math 025-1,2 List of Theorems and Definitions Fall 2010
Math 025-1,2 List of Theorems and Definitions Fall 2010 Here are the 19 theorems of Math 25: Domination Law (aka Monotonicity Law) If f and g are integrable functions on [a; b] such that R b R b f(x) ≤ g(x) for all x 2 [a; b], then a f(x) dx ≤ a g(x) dx. R b Positivity Law If f is a nonnegative, integrable function on [a; b], then a f(x) dx ≥ 0. Also, if f(x) is R b a nonnegative, continuous function on [a; b] and a f(x) dx = 0, then f(x) = 0 for all x 2 [a; b]. Max-Min Inequality If f(x) is an integrable function on [a; b], then Z b (min value of f(x) on [a; b]) · (b − a) ≤ f(x) dx ≤ (max value of f(x) on [a; b]) · (b − a): a Triangle Inequality (for Integrals) Let f :[a; b] ! R be integrable. Then Z b Z b f(x) dx ≤ jf(x)j dx: a a Mean Value Theorem for Integrals Let f : R ! R be continuous on [a; b]. Then there exists a number c 2 (a; b) such that 1 Z b f(c) = f(x) dx: b − a a Fundamental Theorem of Calculus (one part) Let f :[a; b] ! R be continuous and define F : R x [a; b] ! R by F (x) = a f(t) dt. Then F is differentiable (hence continuous) on [a; b] and F 0(x) = f(x). Fundamental Theorem of Calculus (other part) Let f :[a; b] ! R be continuous and let F be any antiderivative of f on [a; b].