This book is the first on the topic and explains the most
cutting-edge methods needed for precise calculations and explores
the development of powerful algorithms to solve research problems.
Multipoint methods have an extensive range of practical
applications significant in research areas such as signal
processing, analysis of convergence rate, fluid mechanics, solid
state physics, and many others. The book takes an introductory
approach in making qualitative comparisons of different multipoint
methods from various viewpoints to help the reader understand
applications of more complex methods. Evaluations are made to
determine and predict efficiency and accuracy of presented models
useful to wide a range of research areas along with many numerical
examples for a deep understanding of the usefulness of each method.
This book will make it possible for the researchers to tackle
difficult problems and deepen their understanding of problem
solving using numerical methods. Multipoint methods are of great
practical importance, as they determine sequences of successive
approximations for evaluative purposes. This is especially helpful
in achieving the highest computational efficiency. The rapid
development of digital computers and advanced computer arithmetic
have provided a need for new methods useful to solving practical
problems in a multitude of disciplines such as applied mathematics,
computer science, engineering, physics, financial mathematics, and
biology.
Provides a succinct way of implementing a wide range of useful and
important numerical algorithms for solving research problems
Illustrates how numerical methods can be used to study problems
which have applications in engineering and sciences, including
signal processing, and control theory, and financial computation
Facilitates a deeper insight into the development of methods,
numerical analysis of convergence rate, and very detailed analysis
of computational efficiencyProvides a powerful means of learning by
systematic experimentation with some of the many fascinating
problems in scienceIncludes highly efficient algorithms convenient
for the implementation into the most common computer algebra
systems such as Mathematica, MatLab, and Maple
General
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