Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

Arrow left icon
Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (4):
Arrow left icon
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
Author Profile Icon Claus Fuhrer
Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
Author Profile Icon Claus Führer
Claus Führer
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Sparse matrices


Matrices with a small number of nonzero entries are called sparse matrices. Sparse matrices occur, for example, in scientific computing when describing discrete differential operators in the context of numerically solving partial differential equations.

Sparse matrices often have large dimensions, sometimes so large that the entire matrix (with zero entries) would not even fit in the available memory. This is one motivation for a special type for sparse matrices. Another motivation is better performance of operations where zero matrix entries can be avoided.

There are only a very limited number of algorithms for general, unstructured sparse matrices in linear algebra. Most of them are iterative in nature and based on efficient implementations of matrix-vector multiplication for sparse matrices.

Examples for sparse matrices are diagonal or banded matrices. The simple pattern of these matrices allows straightforward storing strategies; the principal diagonal and the sub- and super...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image