Explore how to find the right parameters for matrices so they match given eigenvalues with fast, reliable methods.
This book presents practical algorithms to solve inverse eigenvalue problems, including when eigenvalues are distinct or repeat. It combines formulation, analysis, and numerical experiments to show how quadratic convergence can be achieved and why certain problems are overdetermined or underdetermined in real applications.
The text introduces the core problems, such as adding or scaling parts of a matrix to force it to have specified eigenvalues, and it explains how to adapt methods for various variations found in engineering and science. Readers will see Newton-type strategies, inverse-iteration techniques, and modifications that maintain fast convergence even in challenging cases with multiple eigenvalues. Concrete examples and detailed discussions of convergence help bridge theory and practice."synopsis" may belong to another edition of this title.
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LX-9780656185153
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LX-9780656185153
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