Mathematical models are used to simulate, and sometimes control, the behavior of physical and artificial processes such as the weather and very large-scale integration (VLSI) circuits. The increasing need for accuracy has led to the development of highly complex models. However, in the presence of limited computational accuracy and storage capabilities model reduction (system approximation) is often necessary. Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.
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Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.
Athanasios C. Antoulas is a Professor in the Department of Electrical and Computer Engineering at Rice University. He has served on the editorial board of many journals, including IEEE Transactions on Automatic Control, SIAM Journal on Control and Optimization, and Linear Algebra and Its Applications, and for the past eight years he has been Co-Editor-in-Chief of Systems and Control Letters. Professor Antoulas serves on the editorial board of the SIAM Advances in Design and Control book series. His research interests are in the broad area of dynamical systems and computation, including model reduction of large-scale systems.
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