Linear-algo

Learning process

Assignment 1-vector calculation and visualize

Assignment 2-Gauss Elimination

Challege project-hilbert matrix:

Initial n =index2;

While relative error percentage(err_percentage) of x_delta=x_true-x_head<=100%;

Create nxn Hilbert Matrix;

Initial x_true value= [ n size matrix with all ones];

Create b=Hx_trure;

Using Gauss elimination to get x_headl

Calculate residual: r=b-dot(H,x_head);

Calculate residual norm inf;

Calculate error: x_delta=x_true-x_head;

Calculate x_delta norm inf;

Calculate relative error percentage(err_percentage);

If err_percentage>100%, break loop;

Challenge Project-Householder Transformation

Use Householder Transform nxm matrix to solve linear square system.

Obtain Q, R matrix, and use back subsitution method to obtain x_head.

Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
.idea		.idea
Assignment 4-Eigenvalue.ipynb		Assignment 4-Eigenvalue.ipynb
Assignment1_vector.py		Assignment1_vector.py
Assignment2_Gaussian_elimination.ipynb		Assignment2_Gaussian_elimination.ipynb
CP1_hilbert_matrix.ipynb		CP1_hilbert_matrix.ipynb
CP_2&_Assignment3_Householder_Transformation.ipynb		CP_2&_Assignment3_Householder_Transformation.ipynb
Challenge 3.ipynb		Challenge 3.ipynb
Challenge_Project_4.ipynb		Challenge_Project_4.ipynb
Newton_system.csv		Newton_system.csv
README.md		README.md
assignment 5-non-linear equation.ipynb		assignment 5-non-linear equation.ipynb
eigentable.csv		eigentable.csv
output-Assigment4.txt		output-Assigment4.txt
output-Assignment 2.txt		output-Assignment 2.txt
output-assignment3(1).txt		output-assignment3(1).txt
output-assignment3.txt		output-assignment3.txt
output-challenge project1.txt		output-challenge project1.txt
output-project2.txt		output-project2.txt

Provide feedback