Big Practical Guide to Computer Simulations 2nd Edition by Alexander Hartmann 9814571768 9789814571760

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Product details:

ISBN 10: 9814571768

ISBN 13: 9789814571760

Author: Alexander K Hartmann

This book teaches you all necessary (problem-independent) tools and techniques needed to implement and perform sophisticated scientific numerical simulations. Thus, it is suited for undergraduate and graduate students who want to become experts in computer simulations in Physics, Chemistry, Biology, Engineering, Computer Science and other fields.

Big Practical Guide to Computer Simulations 2nd Table of contents:

1. Programming in C

1.1 Basic C programs

1.1.1 Basic data types

1.1.2 Arithmetic expressions

1.1.3 Control Statements

1.1.4 Complex data types

1.2 Functions

1.3 Input/output

1.4 Pointers and dynamic memory management

1.5 Important C compiler options

1.6 Preprocessor directives and macros

2. Scripts

2.1 Make Files

2.2 Shell Scripts

2.3 Python

3. Software Engineering

3.1 How to manage a (simulation) project

3.1.1 Definition of the problem and solution strategies

3.1.2 Designing data structures

3.1.3 Defining small tasks

3.1.4 Distributing work

3.1.5 Implementing the code

3.1.6 Testing

3.1.7 Writing documentation

3.1.8 Using the code

3.2 Programming Style

3.3 Version management with subversion

4. Debugging and Testing

4.1 gdb

4.2 ddd

4.3 Memory checker

4.4 Profiling with gprof

5. Object-oriented Software Development

5.1 Object-orientation principles

5.2 A sample using C

5.3 Introduction to C++ and an example

6. Algorithms and data structures

6.1 O notation

6.2 Iteration and recursion

6.3 Divide-and-conquer approach

6.4 Dynamic programming

6.5 Backtracking

6.6 Lists

6.7 Trees

6.7.1 Heaps

6.8 Graphs

6.8.1 Basic definitions

6.8.2 Data structures

6.8.3 Generation

6.8.4 Connected Components

7. Libraries

7.1 Standard C library

7.2 Standard Template Library

7.3 GNU scientific library

7.4 Creating your own libraries

8. Randomness and Statistics

8.1 Introduction to probability

8.1.1 Discrete random variables

8.1.2 Continuous random variables

8.2 Generating (pseudo) random numbers

8.2.1 Uniform (pseudo) random numbers

8.2.2 Discrete random variables

8.2.3 Inversion Method

8.2.4 Rejection Method

8.2.5 The Gaussian Distribution

8.3 Basic data analysis

8.3.1 Estimators

8.3.2 Confidence intervals

8.3.3 Histograms

8.3.4 Resampling using Bootstrap

8.4 Data plotting

8.4.1 gnuplot

8.4.2 xmgrace

8.5 Hypothesis testing and (in-)dependence of data

8.5.1 Chi-squared test

8.5.2 Kolmogorov-Smirnov test

8.5.3 ROC analysis

8.5.4 Statistical (in-)dependence

8.5.5 Principal Component Analysis

8.5.6 Clustering Data

8.6 General estimators

8.6.1 Maximum likelihood

8.6.2 Data fitting

9. Information Retrieval, Publishing and Presentations

9.1 Searching for Literature

9.2 Visualization

9.2.1 Presentation-ready figures using gnuplot

9.2.2 Drawing figures using xfig

9.2.3 Drawing graphs

9.2.4 Three-dimensional figures with Povray

9.3 Preparing Publications

9.3.1 LATEX

9.3.1.1 Getting started

9.3.1.2 Fonts, special characters and symbols

9.3.1.3 Chapters, sections and footnotes

9.3.1.4 Lists

9.3.1.5 Mathematical formulas

9.3.1.6 Minipages, tables and arrays

9.3.1.7 Figures

9.3.1.8 Bibliography

9.3.1.9 Self-defined commands and lengths

9.3.1.10 More information

9.3.2 Beamer class

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