Methods Of Numerical Integration


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Methods of Numerical Integration


Methods of Numerical Integration

Author: Philip J. Davis

language: en

Publisher: Courier Corporation

Release Date: 2007-01-01


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Useful to programmers and stimulating for theoreticians, this text offers a balanced presentation accessible to those with a background in calculus. Topics include approximate integration over finite and infinite intervals, error analysis, approximate integration in two or more dimensions, and automatic integration. Includes five helpful appendixes. 1984 edition.

Geometric Numerical Integration


Geometric Numerical Integration

Author: Ernst Hairer

language: en

Publisher: Springer Science & Business Media

Release Date: 2013-03-09


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Numerical methods that preserve properties of Hamiltonian systems, reversible systems, differential equations on manifolds and problems with highly oscillatory solutions are the subject of this book. A complete self-contained theory of symplectic and symmetric methods, which include Runge-Kutta, composition, splitting, multistep and various specially designed integrators, is presented and their construction and practical merits are discussed. The long-time behaviour of the numerical solutions is studied using a backward error analysis (modified equations) combined with KAM theory. The book is illustrated by many figures, it treats applications from physics and astronomy and contains many numerical experiments and comparisons of different approaches.

Numerical Integration of Stochastic Differential Equations


Numerical Integration of Stochastic Differential Equations

Author: G.N. Milstein

language: en

Publisher: Springer Science & Business Media

Release Date: 1994-11-30


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This book is devoted to mean-square and weak approximations of solutions of stochastic differential equations (SDE). These approximations represent two fundamental aspects in the contemporary theory of SDE. Firstly, the construction of numerical methods for such systems is important as the solutions provided serve as characteristics for a number of mathematical physics problems. Secondly, the employment of probability representations together with a Monte Carlo method allows us to reduce the solution of complex multidimensional problems of mathematical physics to the integration of stochastic equations. Along with a general theory of numerical integrations of such systems, both in the mean-square and the weak sense, a number of concrete and sufficiently constructive numerical schemes are considered. Various applications and particularly the approximate calculation of Wiener integrals are also dealt with. This book is of interest to graduate students in the mathematical, physical and engineering sciences, and to specialists whose work involves differential equations, mathematical physics, numerical mathematics, the theory of random processes, estimation and control theory.