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.
- 2017 / Approx. xii + 93 pages / Softcover / ISBN 978-1-611974-72-0 List Price $39.00 / SIAM Member Price $27.30 / Order Code: SL03
Keywords: Saddle-point systems, quasi-definite systems, linear least squares, iterative methods, Krylov methods
Contents Preface Index
Numerous applications, including computational optimization and fluid dynamics, give rise to block linear systems of equations said to have the quasi-definite structure. In practical situations, the size or density of those systems can preclude a factorization approach, leaving only iterative methods as the solution technique. Known iterative methods, however, are not specifically designed to take advantage of the quasi-definite structure.
This book discusses the connection between quasi-definite systems and linear least-squares problems, the most common and best understood problems in applied mathematics, and explains how quasi-definite systems can be solved using tailored iterative methods for linear least squares (with half as much work!). To encourage researchers and students to use the software, it is provided in MATLAB, Python, and Julia.
The authors provide
Audience This book is intended for researchers and advanced graduate students in computational optimization, computational fluid dynamics, computational linear algebra, data assimilation, and virtually any computational field in which saddle-point systems occur. The software should appeal to all practitioners, even those not technically inclined.
About the Authors Dominique Orban is associate professor of computational mathematics at École Polytechnique in Montréal and a member of the GERAD research center for decision analysis. He has a background in continuous optimization and has a keen interest in the linear algebra problems occurring at the core of methods for optimization. He believes one does not fully understand a numerical method until it has been implemented in a programming language. He has authored over 40 papers and several software packages on optimization and linear algebra and is constantly looking for better implementations of crucial methods in those fields. Dominique is a member of SIAM, the Mathematical Optimization Society, and the Association for Computing Machinery.
Mario Arioli is adjunct professor of at the mathematics and computer science department of Emory University. After he retired from Rutherford Appleton Laboratory, UK, he has been visiting scientist and professor at several universities in Germany (TU Berlin and Bergische University of Wuppertal) and in France (Institut de Mathématiques de Toulouse, Université Paul Sabatier, Toulouse, and INP-ENSEEIHT, Toulouse). He has a background in numerical analysis and his interests range from numerical linear algebra and round-off error analysis of algorithms to graph theory and the numerical solution of PDEs. He has authored more than 60 papers and contributed to the HSL software library. Mario is a member of SIAM.
ISBN: 9781611974720
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