Optimization By Vector Space Methods 9780471181170 mobi 下载 网盘 caj lrf pdf txt 阿里云

　　Engineers must make decisi*** regarding the distribution of

expensive resources in a manner that will be economically

beneficial. This problem can be realistically formulated and

logically ***yzed with optimization theory. This book shows

engineers how to use optimization theory to solve complex problems.

Unifies the large field of optimization with a few geometric

principles. Covers functional ***ysis with a minimum of

mathematics. Contains problems that relate to the applicati*** in

the book.

Linear Spaces.

Hilbert Space.

Least-Squares Estimation.

Dual Spaces.

Linear ***s and Adjoints.

Optimization of Functionals.

Global Theory of C***trained Optimization.

Local Theory of C***trained Optimization.

I***tive Methods of Optimization.

Indexes.

　　D***ID G. LUENBERGER is a professor in the School of

Engineering at Stanford University. He has published four textbooks

and over 70 technical papers. Professor Luenberger is a Fellow of

the Institute of Electrical and Electronics Engineers and recipient

of the 1990 Bode Lecture Award. His current research is mainly in

investment science, economics, and planning.

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Proposition 1. Let X be a Banach space, M a closed subspace of X, and X/M the quotient space with the quotient norm defined as above. Then X/M is also a Banach space.

Since the solution to the approximation problem is equivalent to solution to the normal equati***, it is clear that the Gram-Schmidt procedure can be interpreted as a procedure for inverting the Gram matrix. Conversely, many effective algorithms for solving the normal equati*** have an interpretation in terms of the minimum norm problem in Hilbert space. (p. 63)

书籍介绍

Engineers must make decisi*** regarding the distribution of expensive resources in a manner that will be economically beneficial. This problem can be realistically formulated and logically ***yzed with optimization theory. This book shows engineers how to use optimization theory to solve complex problems. Unifies the large field of optimization with a few geometric principles. Covers functional ***ysis with a minimum of mathematics. Contains problems that relate to the applicati*** in the book.