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+
+## General info
+This project is intended to allow the user to solve minimization problem on quadratic functions defined as S(X)= X'AX - X'B, with X = [x, y], X' the transform of X,  A a 2x2 matrix and B a  2X1 vector.
+	
+## Setup
+The project is created with Python 3.7. It moreover requires the following libraries to be installed:
+* numpy
+* scipy
+* matplotlib
+* sys
+	
+## 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.
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