**Numerical Linear Algebra**
refers to the use of matrices to solve computational science problems. In this chapter, we start by learning how to construct these objects effectively in Python. We make an emphasis on importing large sparse matrices from repositories online. We then proceed to reviewing basic manipulation and operations on them. The next step is a study of the different matrix functions implemented in SciPy. We continue on to exploring different factorizations for the solution of matrix equations, and for the computation of eigenvalues and their corresponding eigenvectors.

#### Mastering SciPy

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#### Mastering SciPy

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#### Overview of this book

Table of Contents (16 chapters)

Mastering SciPy

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Numerical Linear Algebra

Interpolation and Approximation

Differentiation and Integration

Nonlinear Equations and Optimization

Initial Value Problems for Ordinary Differential Equations

Computational Geometry

Descriptive Statistics

Inference and Data Analysis

Mathematical Imaging

Index

Customer Reviews