How medelbou3/analyse_numerique_tp works
This repository is a set of standalone Python numerical-method demonstrations, with iterative solution and convergence visualization as the documented main workflow. Separate scripts demonstrate direct linear-system solvers and Lagrange interpolation. The sampled scripts show the calculations and plotting; the Gauss–Seidel script imports comparison data from jacobi2, which is absent from the supplied tree, so that dependency is not mapped.
Main entry points: jacobi.py Jacobi solver · gauss-siedel.py Gauss–Seidel solver · interpolation_lagrange.py Lagrange interpolation · elimunation-gs.py Gauss elimination
Components 9
Iterative solvers
- Jacobi solver
jacobi.py - Gauss–Seidel solver
gauss-siedel.py
Direct solvers
- Gauss elimination
elimunation-gs.py - LU decomposition
decompositioLU.py - LU substitution
decompositioLU.py
Interpolation
- Lagrange interpolation
interpolation_lagrange.py
Other components
- User
- NumPy
- Matplotlib
Connections 13
- User → Jacobi solver: runs
- User → Gauss–Seidel solver: runs
- User → Gauss elimination: runs
- User → LU decomposition: runs
- User → Lagrange interpolation: runs
- Jacobi solver → NumPy: computes with
- Jacobi solver → Matplotlib: plots with
- Gauss–Seidel solver → NumPy: computes with
- Gauss–Seidel solver → Matplotlib: plots with
- Gauss elimination → NumPy: computes with
- LU decomposition → LU substitution: produces factors for
- Lagrange interpolation → Matplotlib: plots with
- Lagrange interpolation → NumPy: samples with