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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. 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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