Comprehensive Coverage of the New, Easy-to-Learn C#
Although C, C++, Java, and Fortran are well-established programming languages, the relatively new C# is much easier to use for solving complex scientific and engineering problems. Numerical Methods, Algorithms and Tools in C# presents a broad collection of practical, ready-to-use mathematical routines employing the exciting, easy-to-learn C# programming language from Microsoft.
The book focuses on standard numerical methods, novel object-oriented techniques, and the latest Microsoft .NET programming environment. It covers complex number functions, data sorting and searching algorithms, bit manipulation, interpolation methods, numerical manipulation of linear algebraic equations, and numerical methods for calculating approximate solutions of non-linear equations. The author discusses alternative ways to obtain computer-generated pseudo-random numbers and real random numbers generated by naturally occurring physical phenomena. He also describes various methods for approximating integrals and special functions, routines for performing statistical analyses of data, and least squares and numerical curve fitting methods for analyzing experimental data, along with numerical methods for solving ordinary and partial differential equations. The final chapter offers optimization methods for the minimization or maximization of functions.
Exploiting the useful features of C#, this book shows how to write efficient, mathematically intense object-oriented computer programs. The vast array of practical examples presented can be easily customized and implemented to solve complex engineering and scientific problems typically found in real-world computer applications.
Rezensionen / Stimmen
"...the book does present some clear and neat classes and methods that implement the standard algorithms. ...The advantage of owning a copy of this book is to have these classical algorithms collected together on your bookshelf in a language that you want to use. ..."
-I-Programmer, December 2009
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Electrical engineers, physicists, applied mathematicians, and programmers; supplemental textbook for students in numerical analysis and scientific computing.
Produkt-Hinweis
Illustrationen
300 s/w Abbildungen
20 Tables, black and white; 3 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 159 mm
Gewicht
ISBN-13
978-0-8493-7479-1 (9780849374791)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Waldemar Dos Passos is a computer programming consultant in the Silicon Valley area of California. After completing his undergraduate education at the University of California, Berkeley, Dr. Dos Passos earned an M.S. in computer science and engineering along with a Ph.D. in physics from the University of Michigan, Ann Arbor. With more than twenty years of computer programming experience, he has published several papers in physics journals.
Introduction
C# and the .NET Framework
Installing C# and the .NET Framework
Overview of Object-Oriented Programming (OOP)
Your First C# Program
Overview of the IDE Debugger
Overview of the C# Language
The .NET Framework Math Class Library
Introduction
A .NET Framework Math Class-Fields
A .NET Framework Math Class-Methods
Vectors and Matrices
Introduction
The C# Vector Library
The C# Matrix Library
Complex Numbers
Introduction
Fundamental Concepts
Complex Number Arithmetic
Elementary Functions of a Complex Number
A Complex Number Library in C#
Complex Number Vectors
Complex Number Matrices
Generic vs. Non-Generic Coding
Sorting and Searching Algorithms
Introduction
Sorting Algorithms
Comparison Sorts
Count Sort
Radix Sort
Search Algorithms
Bits and Bytes
Introduction
Numeric Systems
Bit Manipulation and Bitwise Operators
Assorted Bits and Bytes
Interpolation
Introduction
Linear Interpolation
Bilinear Interpolation
Polynomial Interpolation
Cubic Spline Interpolation
Linear Equations
Introduction
Gaussian Elimination
Gauss-Jordan Elimination
LU Decomposition
Iteration Methods
Eigenvalues and Jacobi's Algorithm
Non-Linear Equations
Introduction
Linear Incremental Method
Bisection Method
The Secant Method
False Positioning Method
Fixed Point Iteration
Newton-Raphson Method
Random Numbers
Introduction
The C# Built-In Random Number Generator
Other Random Number Generators
True Random Number Generators
Probability Distribution Functions
Histograms
Discrete Distributions
Continuous Distributions
Shuffling Algorithms
Numerical Differentiation
Introduction
Finite Difference Formulas
Richardson Extrapolation
Derivatives by Polynomial Interpolation
Numerical Integration
Introduction
Newton-Cotes Formulas
Romberg Integration
Gaussian Quadrature Methods
Monte Carlo Methods
Convolution Integrals
Statistical Functions
Introduction
Some Useful Tools
Basic Statistical Functions
Special Functions
Introduction
Factorials
Combinations and Permutations
Gamma Function
Beta Function
Error Function
Sine and Cosine Integral Functions
Laguerre Polynomials
Hermite Polynomials
Chebyshev Polynomials
Legendre Polynomials
Bessel Functions
Curve Fitting Methods
Introduction
Least Squares Fit
Weighted Least Squares Fit
Linear Regression
The x2 Test for Goodness of Fit
Ordinary Differential Equations
Introduction
Euler Method
Runge-Kutta Methods
Coupled Differential Equations
Partial Differential Equations
Introduction
The Finite Difference Method
Parabolic Partial Differential Equations
Hyperbolic Partial Differential Equations
Elliptic Partial Differential Equations
Optimization Methods
Introduction
Gradient Descent Method
Linear Programming
Simulated Annealing Method
Genetic Algorithms
References
Index
