This is an exhaustive PDF written by Lars Elden, Linde Wittmeyer-Koch,
and Hans Bruun Nielsen. In fact, this really is an exemplary introduction to
numerical computation. The text begins with the basics like error analysis and
computer arithmetic, which gives a very solid ground for the origin and handling
of numerical errors. It proceeds further to explain more basic topics related to
function evaluation, solutions of nonlinear equations, and interpolation
techniques. Next, it describes procedures for improving numerical estimates
using differentiation and Richardson extrapolation. This is followed by full
details of integration, systems of linear equations, and approximation. Finally,
ordinary differential equations complete a thorough course of study that will
prepare the reader with both the theory and practice that will serve in carrying
out numerical computations.
Author(s): Lars Elden, Linde Wittmeyer-Koch, Hans
Bruun Nielsen
This
PDF is prepared by Gonzalo Galiano Casas and Esperanza Garcia Gonzalo from the
Department of Mathematics at Oviedo University. With a view to keeping things
compact, this document initiates with finite arithmetic and error analysis,
which forms the basis necessary for understanding the issue of numerical
precision and limitations. It considers methods for nonlinear equations,
interpolation, and approximation. Key sections on numerical differentiation and
integration give hands-on tools both for data analysis and the solution of
mathematical problems. It also covers systems of linear equations and
optimization, rounding it out for students and practitioners who might want to
apply the numerical methods through a variety of problems.
Author(s): Gonzalo Galiano Casas, Esperanza Garcia Gonzalo, Dept. of Mathematics, Oviedo University
Prof. L. Vandenberghe's lecture note is on applied numerical
computing but brings out the practical application aspect. The text covers most
areas of numerical linear algebra, nonlinear optimization nonlinear least
squares, introduction to floating-point numbers, and rounding errors that are to
be needed for understanding the issues of numerical precision. Examples are
drawn from signal and image processing, control systems, and machine learning,
among other areas, to indicate how these numerical methods are actually applied.
This resource aims to fill the gap between theory and practice by providing a
practical method for solving computational problems.
This book is a technical
reference to the floating-point environment supported on SPARCTM and x86
platforms running under the Solaris operating system. The book describes the
Floating-Point Environment, the representation and computation of floating point
numbers and how the results of arithmetic operations are rounded. The Software
and Hardware Support section describes how numerical operations are passed
between the hardware and software of the system. The book should be
indispensable to anyone seeking an understanding of how numerical computations
are executed and optimized on Solaris systems. In particular, it will be an
asset worth having in real life for developers and engineers working in the
field of numerical algorithms within this particular environment of computing
and offers a deep view into performance and accuracy considerations.
It gives an explanation
of all the different numerical methods of scientific computing. It starts with
the basics, which is Root Finding and Orthogonal Functions, solving equations
and analyzing functions. Finite Differences and Divided Differences included for
the needs in the process of numerical differentiation and interpolation.
Interpolation and Curve Fitting are given to outline estimation and modeling. It
also includes Z-Transforms and Summation Formulas for signal processing and
numerical summation. Quadrature Formulas and Ordinary Differential Equations are
explained for integration and the solution of differential equations. Partial
Differential Equations, Integral Equations, and Stability and Error Analysis
form the advanced topics for numerical methods coverage. Further, Monte Carlo
Techniques, Message Passing Interface, and Simulation Modeling are included to
point out methods for probabilistic simulations and parallel computing.