DESCRIPTION
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.
- Learn the key problem formulations and when classical methods need adjustments
- See how to handle distinct versus multiple eigenvalues in a unified framework
- Understand how to modify approaches to achieve quadratic convergence
- Review numerical experiments that illustrate performance and limits
Ideal for readers of advanced undergraduate and graduate courses in numerical analysis, applied mathematics, and engineering who seek solid, implementation-ready insight into inverse eigenvalue computations.
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