David Hyde and Alex Pothen introduce the Special Issue on Quantum Computing and survey some exciting technical developments.
Quantum algorithms can be understood through linear algebra and offer different tradeoffs than classical algorithms.
The fusion of machine learning and quantum computing has created an unprecedented avenue for innovation.
Quantum computing promises enormous computing power at low costs, marking a new chapter for financial mathematics.
It is quite difficult to fully harness the potential of quantum computers and outperform classical computers.
The High School Mathematical Contest in Modeling tasked students with two open-ended, real-world problems.
The Hackathon encouraged participants to tackle questions about worldwide energy data availability and solar resource potential.
Mark Levi draws connections between the conformal equivalence and electrical resistance of annular regions.
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2018 / xii + 144 pages / Softcover / 978-1-611975-35-2 / List $59.00 / SIAM Member $41.30 / Order Code: FA14
Keywords: nonlinear matrix equation, algebraic Riccati equation, eigenvalue problem, doubling algorithm, entruwise accuracy
Contents Preface Index
Nonlinear matrix equations arise frequently in applied science and engineering. This is the first book to provide a unified treatment of structure-preserving doubling algorithms, which have been recently studied and proven effective for notoriously challenging problems, such as fluid queue theory and vibration analysis for high-speed trains. The authors present recent developments and results for the theory of doubling algorithms for nonlinear matrix equations associated with regular matrix pencils, and highlight the use of these algorithms in achieving robust solutions for notoriously challenging problems that other methods cannot.
Audience Structure-Preserving Doubling Algorithms for Nonlinear Matrix Equations is intended for researchers and computational scientists. Graduate students may also find it of interest.
About the Authors Tsung-Ming Huang is a professor in the department of mathematics at National Taiwan Normal University in Taipei, Taiwan. His research interests include large sparse linear systems, eigenvalue problems, and matrix equations. Ren-Cang Li is a professor in the department of mathematics at University of Texas at Arlington. His research interests include floating-point support for scientific computing, large and sparse linear systems, eigenvalue problems, and model reduction, machine learning, and unconventional schemes for differential equations. Wen-Wei Lin is a life-time chair professor in the department of applied mathematics at National Chiao Tung University in Taiwan. His research interests include numerical analysis, matrix computation in linear systems, eigenvalue problems, optimal controls, large-scale optimization in data science, chaotic dynamical systems, and computational conformal geometry with applications.
ISBN 9781611975352