Bounds For Eigenvalues And The Method Of Intermediate Problems


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Methods of Intermediate Problems for Eigenvalues: Theory and Ramifications


Methods of Intermediate Problems for Eigenvalues: Theory and Ramifications

Author: A. Weinstein

language: en

Publisher: Elsevier

Release Date: 1972-06-23


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In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression.- Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loeve transform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering

Journal of Research of the National Bureau of Standards


Journal of Research of the National Bureau of Standards

Author: United States. National Bureau of Standards

language: en

Publisher:

Release Date: 1964


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Variational Methods for Eigenvalue Approximation


Variational Methods for Eigenvalue Approximation

Author: H. F. Weinberger

language: en

Publisher: SIAM

Release Date: 1974-01-01


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Provides a common setting for various methods of bounding the eigenvalues of a self-adjoint linear operator and emphasizes their relationships. A mapping principle is presented to connect many of the methods. The eigenvalue problems studied are linear, and linearization is shown to give important information about nonlinear problems. Linear vector spaces and their properties are used to uniformly describe the eigenvalue problems presented that involve matrices, ordinary or partial differential operators, and integro-differential operators.