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Customer Reviews
By reading and absorbing the material in this book, the reader is left with the tools and the insights necessary to derive their own numerical methods.
No longer will numerical methods be memorized as textbook formulas -- now the reader can adapt and derive a formula to solve a specific problem, instead of trying to fit one of a small number of textbook formulas to a problem.
The distinction is made between numerical analysis and numerical methods, with emphasis on the latter.
The book is roughly divided into two parts. The first part covers classical numerical methods, using classical error analysis (truncation error, roundoff error). The second part reexamines these methods under the frequency domain, analyzing how numerical methods affect various frequencies (the "transfer function" approach).
Numerical methods are derived under an information theory model, such as by finding a quadrature formula of the highest polynomial degree of accuracy, given limited information about the function and its derivatives.
Matrices and linear systems are not discussed as much as one might expect, although one chapter convincingly leads the reader to question some classical methods.
The content is well-rounded, introducing many readers to topics such as random number generators, difference equations and summation formulas, digital filters and quantization, discrete fourier transforms and the FFT, and orthogonal polynomials. A background in calculus is all that is needed.
Many real-world examples and anecdotes are cited, but without too much detail or too many illustrations given.
This book encourages the reader to ask: "What information is available about the problem? How can it be used to solve the problem? What are the limits of this information?" The approach is practical, not merely analytical.
This book teaches what most other numerical books fail to teach: How to derive your own formulas, and thus your own solutions to problems. And that is perhaps the most important lesson of all.
The book starts with an essay on numerical methods that discusses the book's five main ideas starting with its motto, which is the first idea - "The purpose of computing is insight, not numbers." The second idea is that it is necessary to study families of numbers and algorithms and to relate one family to another. The third and fourth major ideas are that of roundoff and truncation error, each of which is an effect of computing on finite machines. The final main idea is that of feedback and stability, where numbers produced at one stage are looped back to feed other stages of computations, and the result may or may not be stable.
The remainder of the book then studies many families of calculations and numbers based on these insights. The book is divided into five parts - Fundamentals and Algorithms, Polynomial Approximation, Fourier Approximation, Exponential Approximation, and finally a miscellaneous section which talks about approximations to singularities, optimization, linear independence, and eigenvalues and eigenvectors of Hermitian matrices. As you can see, the idea throughout this book is that since numerical methods are the use of numbers to simulate mathematical processes, then all of these algorithms are actually approximations. Throughout the book there are clearly worked out examples with plenty of illustrations and also many exercises, some with solutions. Highly recommended.
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