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140122 ||| eng |
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|a 9781475728170
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| 100 |
1 |
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|a DeWilde, Patrick
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| 245 |
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|a Time-Varying Systems and Computations
|h Elektronische Ressource
|c by Patrick DeWilde, Alle-Jan van der Veen
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| 250 |
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|a 1st ed. 1998
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| 260 |
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|a New York, NY
|b Springer US
|c 1998, 1998
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| 300 |
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|a XIV, 460 p
|b online resource
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| 505 |
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|a 1. Introduction -- I Realization -- 2. Notation and Properties of Non-Uniform Spaces -- 3. Time-Varying State Space Realizations -- 4. Diagonal Algebra -- 5. Operator Realization Theory -- 6. Isometric and Inner Operators -- 7. Inner-Outer Factorization and Operator Inversion -- II Interpolation and Approximation -- 8. J-Unitary Operators -- 9. Algebraic Interpolation -- 10. Hankel-Norm Model Reduction -- 11. Low-Rank Matrix Approximation and Subspace Tracking -- III Factorization -- 12. Orthogonal Embedding -- 13. Spectral Factorization -- 14. Lossless Cascade Factorizations -- 15. Conclusion -- Appendices -- A—Hilbert space definitions and properties -- References -- Glossary of notation
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| 653 |
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|a Electrical and Electronic Engineering
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| 653 |
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|a Electrical engineering
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| 653 |
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|a Control theory
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| 653 |
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|a Systems Theory, Control
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| 653 |
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|a Linear Algebra
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| 653 |
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|a System theory
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Algebras, Linear
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| 653 |
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|a Signal processing
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| 700 |
1 |
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|a Veen, Alle-Jan van der
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 028 |
5 |
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|a 10.1007/978-1-4757-2817-0
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4757-2817-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 003
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| 520 |
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|a Complex function theory and linear algebra provide much of the basic mathematics needed by engineers engaged in numerical computations, signal processing or control. The transfer function of a linear time invariant system is a function of the complex vari able s or z and it is analytic in a large part of the complex plane. Many important prop erties of the system for which it is a transfer function are related to its analytic prop erties. On the other hand, engineers often encounter small and large matrices which describe (linear) maps between physically important quantities. In both cases similar mathematical and computational problems occur: operators, be they transfer functions or matrices, have to be simplified, approximated, decomposed and realized. Each field has developed theory and techniques to solve the main common problems encountered. Yet, there is a large, mysterious gap between complex function theory and numerical linear algebra. For example, complex function theory has solved the problem to find analytic functions of minimal complexity and minimal supremum norm that approxi e. g. , as optimal mate given values at strategic points in the complex plane. They serve approximants for a desired behavior of a system to be designed. No similar approxi mation theory for matrices existed until recently, except for the case where the matrix is (very) close to singular
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