DantasB/computational-linear-algebra

A Numerical Calculus Library Built for the UFRJ Computational Linear Algebra course (COC473)

Computational-Linear-Algebra

A simple library constructed using Python 3 and some other libraries made by Bruno Dantas.

Table of Contents

• How to use
• Matrix utils
• Functions
• Case Studies

How to use

You will need to install all the requirements.txt and run the main.py python program.

Matrix utils

The matrix_utils.py library was created with all the secondary functions needed to run properly the functions. It contains base matrix functions, derivate functions, and another necessary secondary routines.

Functions

Below you can see all the functions covered by this library:

• LU Decomposition
• Cholesky Decomposition
• Solve Matrix by Cholesky and LU Decomposition
• Iterative Jacobi Method
• Gauss-Seidel Method
• Power Method
• Jacobi Method for EigenValues and EigenVectors
• MMQ
• Bissection Method
• Newton Method
• Secant Method
• Inverse Interpolation
• Non Linear Newton Method
• Broyden Method
• Non Linear MMQ
• Integrate Method by Polinomial Integration and Gauss-Laguerre, Gauss-Legendre and Gauss-Hermite
• Integrate by Mid-Point, Trapeze and Simpson Method
• First Order EDO Solver by Runge-Kutta and Euler Method
• Second Order EDO Solver by Taylor Series approximation and Runge-Kutta-Nystron
• Forward Richard Extrapolation
• Backward Richard Extrapolation
• Central Richard Extrapolation

Case Studies

All the case studies pdf's are in every folder named as Lista de Exercícios...