R9482/Homework19ce81b294cf4master
README.md
SP4E - Homework 1
General info
This file provides a brief documentation and information related to the first Homework of the course Scientific Programming for Engineers, fall 2019.
This homework is done by O. Ashtari and A. Sieber
Last update: 16.10.2019
Project Description
This project is intended to solve a minimization problem on n-dimensional quadratic functions defined as S(X)= XᵀAX - Xᵀb where Xᵀ =[x1, x2, ..., xn], T represents the transpose operation, A is an n by n square matrix and b a column vector of size n.
Requirements
The project is created with Python 3.7. It moreover requires the following libraries to work properly:
- numpy
- argparse
- scipy
- matplotlib
- sys
- math
Use
optimizer.py
This script solves minimization problems using the scipy.optimize.minimize routine. The minimization solver can be specified as an input argumeent by entering the following command line:
$ python3 optimizer.py method
Where the variable method is one of the following:
- Nelder-Mead
- Powell
- CG (default)
- BFGS
- L-BFGS-B
- TNC
- SLSQP
The initial guess of the minimization process has to be specified directly in the optimizer.py file by changing the value of the variable X0. The matrix A and vector B can also be modified directly in the file.
conjugate_gradient.py
post_processing.py
Post-processing file that takes as inputs the value of A, B as well as the intermediate solutions of the iterative minimization process and the method used. The file generates a 3D plot displaying the function to be minimized as well as the intermediate solutions.