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This book mainly explores the use of polynomial preconditioners in iterative solvers for large-scale sparse linear systems Ax = b. It is well known that preconditioners can significantly improve the convergence of solvers, particularly when the coefficient matrix is ill-conditioned. Further, polynomial preconditioners have several advantages over other popular preconditioners ¿ they may be implemented easily, they are highly parallel, and they are extremely agile. Due to the intrinsic disadvantages of polynomial methods (e.g., spectrum information is needed, poor stability of the large-degree…mehr

Produktbeschreibung
This book mainly explores the use of polynomial preconditioners in iterative solvers for large-scale sparse linear systems Ax = b. It is well known that preconditioners can significantly improve the convergence of solvers, particularly when the coefficient matrix is ill-conditioned. Further, polynomial preconditioners have several advantages over other popular preconditioners ¿ they may be implemented easily, they are highly parallel, and they are extremely agile. Due to the intrinsic disadvantages of polynomial methods (e.g., spectrum information is needed, poor stability of the large-degree polynomial preconditioning) and the limitation of computing technologies, the polynomial preconditioning technique was somehow ignored in the past ten years. Fortunately, at present, polynomial preconditioners are attracting more and more attention with the development of computer science.
Autorenporträt
After receiving his Bachelor degree of Computer Science from Tsinghua University in 1990 and Master degree of Computer Science from Beijing Polytechnic University in 1995, Dr. Yu Liang won his Ph.D. degree of Computer Science at the Chinese Academy of Sciences in 1998 and Ph.D degree of Applied Mathematics from University of Ulster in 2005.