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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
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References

4.9.1 Solving several linear equation systems with LU

Let be an matrix and  be a sequence of  vectors. We consider the problem to find  vectors  such that:

We assume that the vectors  are not known simultaneously. In particular, it is quite a common situation that the th problem has to be solved before becomes available, for example in the context of the simplified Newton iteration, see [24].

factorization is a way to organize the classical Gauss elimination method in such a way that the computation is done in two steps:

  • A factorization step of the matrix  to get matrices in triangular form
  • A relatively cheap backward and forward elimination step that works on the instances of and benefits from the more time-consuming factorization step

The method also uses the fact that if  is a permutation matrix such that  is the original matrix with its rows permuted, the two systems and have the same...

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