Description
Computational Methods for Numerical Analysis with R
Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series
Author: Howard, II James P
Language: English
Subjects for Computational Methods for Numerical Analysis with R:
Keywords
Finite Differences Method; Reduced Row Echelon Form; differential equations; Ordinary Differential Equations; integration; Numeric Data Type; linear algebra; Midpoint Method; interpolation; Data Frame; optimization; Euler Method; Adams Bashforth Method; Fourth Order Runge Kutta Method; Golden Section Search; Runge Kutta Methods; Row Echelon Form; Midpoint Rule; Local Truncation Error; Simulated Annealing; LU Decomposition; Global Truncation Error; Newton Raphson Method; Piecewise Linear Interpolation; Bilinear Interpolation; Gradient Descent; Double Precision; Polynomial Interpolation; Cubic Spline; Elementary Row Operations
Publication date: 09-2020
· 15.6x23.4 cm · Paperback
Publication date: 06-2017
· 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Biography
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Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use.
Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background.
Preface
Introduction to Numerical Analysis
Error Analysis
Linear Algebra
Interpolation and Extrapolation
Differentiation and Integration
Root Finding and Optimization
Differential Equations
Suggested Reading
Index
James P Howard, II
