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Книга Scientific Data Analysis Richard L. Branham

Scientific Data Analysis

An Introduction to Overdetermined Systems

Автор: Richard L. Branham
Език: Английски език
Корици: С меки корици
Издател: Springer, Berlin
Наличност: Външен склад
Изпращаме след 5-8 дни
52.72 € 103.11 лв
This monograph is concerned with overdetermined systems, inconsistent systems with more equations th...

Информация за книгата

Автор
Richard L. Branham
Език
Английски език
Корици
Книга - С меки корици
Издадена
2012
страници
237
EAN
9781461279815
ISBN
146127981X
Enbook ID
06795516
Издател
Springer, Berlin
Теглоt
387
Размери
155 x 235 x 13

Пълно описание

This monograph is concerned with overdetermined systems, inconsistent systems with more equations than unknowns, in scientific data reduction. It is not a text on statistics, numerical methods, or matrix cOmputations, although elements of all three, especially the latter, enter into the discussion. The reader I have in mind is a scientist or engineer who has gathered data that he or she wants to model by a mathematical system, perhaps linear, perhaps nonlinear, and solve to obtain the best estimates, in some sense of the term "best," of various parameters. Because the calculations will be performed on a digital computer, the first chapter discusses floating-point numbers and their effect on mathematical operations. The chapter ends with some methods for accurately summing floating-point numbers, an operation frequently required in numerical work and one often done by the worst possible method, recursive summation. Chapter 2 gives a brief review of linear algebra and includes vector and matrix norms and condition numbers of matrices and linear systems. ' Chapter 3 presents some ideas for manipulating sparse matrices. Frequently, time or memory can be saved by use of sparse matrix techniques. The subject is extensive and the chapter is only indicative of the many techniques available. Although Chapter 3 is somewhat extraneous to the rest of the book, Chapter 5, on linear least squares, makes use of the compressed storage mode for the symmetric matrices discussed in Chapter 3.

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