English Choose a language for shopping. Ratings and Reviews 0 0 star ratings 0 reviews. Francisco marked it as to-read May 26, Overall, this is an excellent resource for the applied anxlysis, engineer, or scientist who wants an accessible introduction to functional analysis. The two just mentioned actually teach you how to think as a mathematician, relatively painlessly. In practical terms, it means that Griffel shows how the tools of functional analysis can be applied to differential equations, dynamical systems, and fourier analysis.

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I am grateful to many people who have pointed out errors in the first edition, and urge readers to follow their example. I wish also to acknowledge the helpful and friendly collaboration of Ellis Horwood Ltd in the production of this book. Part IV can be regarded as a coda, and Part I as a prelude. In Part IV, however, the two theories will be unified. The theory of distributions is essentially a new foundation for mathematical analysis; that is, a new structure, replacing functions of a real variable by new objects, defined quite differently, but having many of the same properties and usable for many of the same purposes as ordinary functions.

This theory gives a useful technique for analysing linear problems in applied mathematics, but is less useful for nonlinear problems which is why it is not used in Parts II and III. And it forms a useful prelude to Parts II and III because it introduces fairly abstract ideas in a fairly concrete setting.

Our treatment of distribution theory is intended to be detailed enough to give a good general understanding of the subject, but it does not aim at completeness. Many details and many worthwhile topics are omitted for the sake of digestibility. A full account would take a full-length book; see the references given in the last section of each chapter. The plan of Part I is as follows.

In Chapter 1 we set out the basic theory of distributions. Chapter 1 Generalised Functions In this chapter we lay the theoretical foundations for the treatment of differential equations in Chapters 2 and 3. We begin in section 1. In section 1. The ideas and definitions of the theory are more elaborate than those of ordinary calculus; this is the price paid for developing a theory which is in many ways simpler as well as more comprehensive.

In particular, the theorems about convergence and differentiation of series of generalised functions are simpler than in ordinary analysis. This is illustrated by examples in sections 1. References to other accounts of this subject are given in section 1.

Consider a rod of nonuniform thickness. But if the mass is concentrated at a finite number of points instead of being distributed continuously, then the above description breaks down. Suppose that the bead has unit mass and is so small that it is reasonable to represent it mathematically as a point. Then the total mass in the interval a,b is zero if 0 is outside the interval, and is one if zero is inside the interval.

There is no function p that can represent this mass-distribution. But if a function vanishes everywhere except at a single point, it is easy to prove that its integral over any interval must be zero, so that integrating it over an interval including the origin cannot give the correct value, 1.

This makes good physical sense, though it is mathematically absurd. It can be considered as a technical trick, or short cut, for obtaining results for discrete point particles from the continuous theory, results which can always be verified if desired by working out the discrete case from first principles. A point particle can be considered as the limit of a sequence of continuous distributions which become more and more concentrated. The delta function can similarly be considered as the limit of a sequence of ordinary functions.

Consider, for example, 1. The delta function can be considered as a kind of mathematical shorthand representing that procedure, and results obtained by its use can always be verified if desired by working with dn and then evaluating the limit.

The point of view described above is that of many physicists, engineers, and applied mathematicians who use the delta function. The situation is reminiscent of the use of complex numbers in the 16th century for solving algebraic equations.

It was only much later that complex numbers were given a solid mathematical foundation, and then with the development of the theory of functions of a complex variable their applications far transcended the simple algebra which led to their introduction. The solid foundation was developed by Sobolev in and Schwartz in the s , and again goes far beyond merely propping up the delta function. The theory of generalised functions that they developed can be used to replace ordinary analysis, and is in many ways simpler.

Every generalised function is differentiable, for example; and one can differentiate and integrate series term by term without worrying about uniform convergence. The theory also has limitations: that is, it shows clearly what you cannot do with the delta function as well as what you can — namely, you cannot multiply it by itself, or by a discontinuous function.

The other disadvantage of the theory is that it involves a certain amount of formal machinery. However, we must be careful about what functions we allow as weighting functions. The definitions below may at first seem arbitrary and needlessly complicated; but they are carefully framed, as you will see, to make the resulting theory as simple as possible. The reader unfamiliar with the notation of set theory should consult Appendix A.

Definition 1. Example 1. This is probably the simplest example of a test function. They are bound to have somewhat complicated forms, for the following reason. Test functions are thus peculiar functions; they are smooth, yet Taylor expansions are not valid. Fortunately, we never really need explicit formulas for test functions.

They are used for theoretical purposes only, and are well-behaved smooth etc. The following result gives another nice mathematical property. Proposition 1. Examples 1. For some purposes pointwise convergence is suitable; for other purposes uniform convergence is needed an outline of the theory of uniform convergence is given in Appendix B. One of the characteristics of functional analysis is its use of many different kinds of convergence, as demanded by different problems.

The most useful for our present purpose is the following. This is a stringent definition, much stronger than ordinary convergence. We do not offer an example because specific examples are never needed: test functions are only the scaffolding upon which the main part of the theory is built. Notation 1. This is the functional defined in Example 1. To justify calling it a distribution, we must show that the functional is continuous, i. In fact it follows immediately from Definition 1. Any continuous function is locally integrable; so is any piecewise continuous function, as defined below.

Theorem 1. Proof We must first show that the integral exists. Linearity is obvious.

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