diff --git a/README.md b/README.md index f158da4..7702ebe 100644 --- a/README.md +++ b/README.md @@ -10,55 +10,9 @@ A lightweight, from-scratch, object-oriented Python package implementing classic - Lightweight, no dependencies. - Consistent object-oriented API (.solve() etc). ---- - ## Tutorial Series -This package comes with a set of Jupyter notebooks designed as a structured tutorial series in **numerical methods**, both mathematically rigorous and hands-on with code. - -### Core Tutorials - -1. [Tutorial 1: Vectors and Matrices](tutorials/tutorial1_vectors.ipynb) - - - Definitions of vectors and matrices. - - Vector operations: addition, scalar multiplication, dot product, norms. - - Matrix operations: addition, multiplication, transpose, inverse. - - Matrix and vector norms. - - Examples with `numethods.linalg`. - -2. [Tutorial 2: Linear Systems of Equations](tutorials/tutorial2_linear_systems.ipynb) - - - Gaussian elimination and Gauss–Jordan. - - LU decomposition. - - Cholesky decomposition. - - Iterative methods: Jacobi and Gauss-Seidel. - - Examples with `numethods.solvers`. - -3. [Tutorial 3: Orthogonalization and QR Factorization](tutorials/tutorial3_orthogonalization.ipynb) - - - Inner products and orthogonality. - - Gram–Schmidt process (classical and modified). - - Householder reflections. - - QR decomposition and applications. - - Examples with `numethods.orthogonal`. - -4. [Tutorial 4: Root-Finding Methods](tutorials/tutorial4_root_finding.ipynb) - - - Bisection method. - - Fixed-point iteration. - - Newton’s method. - - Secant method. - - Convergence analysis and error behavior. - - Trace outputs for iteration history. - - Examples with `numethods.roots`. - -- [Polynomial Regression Demo](tutorials/polynomial_regression.ipynb) - -- Step-by-step example of polynomial regression. -- Shows how to fit polynomials of different degrees to data. -- Visualizes fitted curves against the original data. - ---- +This package comes with a set of Jupyter notebooks designed as a structured tutorial series in **numerical methods**, both mathematically rigorous and hands-on with code. See [Tutorials](./tutorials/README.md). ## Features