best approximation product spaces von deutsch frank (8 Ergebnisse)

Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. C-02648 9780387951560 Sprache: Englisch Gewicht in Gramm: 1050.

Zustand: Fair. Volume 7. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780387951560.

Hardcover. Zustand: Good. 2001. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.

Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.5.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book evolved from notes originally developed for a graduate course, 'Best Approximation in Normed Linear Spaces,' that I began giving at Penn State Uni versity more than 25 years ago. It soon became evident. that many of the students who wanted to take the course (including engineers, computer scientists, and statis ticians, as well as mathematicians) did not have the necessary prerequisites such as a working knowledge of Lp-spaces and some basic functional analysis. (Today such material is typically contained in the first-year graduate course in analysis. ) To accommodate these students, I usually ended up spending nearly half the course on these prerequisites, and the last half was devoted to the 'best approximation' part. I did this a few times and determined that it was not satisfactory: Too much time was being spent on the presumed prerequisites. To be able to devote most of the course to 'best approximation,' I decided to concentrate on the simplest of the normed linear spaces-the inner product spaces-since the theory in inner product spaces can be taught from first principles in much less time, and also since one can give a convincing argument that inner product spaces are the most important of all the normed linear spaces anyway. The success of this approach turned out to be even better than I had originally anticipated: One can develop a fairly complete theory of best approximation in inner product spaces from first principles, and such was my purpose in writing this book.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book evolved from notes originally developed for a graduate course, 'Best Approximation in Normed Linear Spaces,' that I began giving at Penn State Uni versity more than 25 years ago. It soon became evident. that many of the students who wanted to take the course (including engineers, computer scientists, and statis ticians, as well as mathematicians) did not have the necessary prerequisites such as a working knowledge of Lp-spaces and some basic functional analysis. (Today such material is typically contained in the first-year graduate course in analysis. ) To accommodate these students, I usually ended up spending nearly half the course on these prerequisites, and the last half was devoted to the 'best approximation' part. I did this a few times and determined that it was not satisfactory: Too much time was being spent on the presumed prerequisites. To be able to devote most of the course to 'best approximation,' I decided to concentrate on the simplest of the normed linear spaces-the inner product spaces-since the theory in inner product spaces can be taught from first principles in much less time, and also since one can give a convincing argument that inner product spaces are the most important of all the normed linear spaces anyway. The success of this approach turned out to be even better than I had originally anticipated: One can develop a fairly complete theory of best approximation in inner product spaces from first principles, and such was my purpose in writing this book.

Zustand: New. In.

Hardcover. Zustand: Brand New. 1st edition. 338 pages. 9.25x6.25x0.75 inches. In Stock.