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+# Minimization of a scalar function in Python
+
+(SP4E 2018, Homework 1)<br/>
+Authors: Sajjad Azimi, Alessia Ferraro
+
+## Description and usage
+
+* **optimizer.py** <br/>
+ The program optimizer.py finds the minimum of a given scalar function making use of the scipy.optimize.minimize module.
+ Via the command line options, the user can choose the minimization algorithm and whether the starting point is randomly
+ generated (see the helpmode -h): <br/>
+
+ `-CG` (Conjugate Gradient) <br/>
+ `-BFGS` (Broyden–Fletcher–Goldfarb–Shanno) <br/>
+ `-RI` to generate a random initial condition <br/>
+
+ Once the optimization process reaches the required tolerance a plotting routine shows both the surface and the minimization
+ path of the chosen algorithm.
+
+* **conjugate_gradient.py** <br/>
+To run conjugate_gradient you have to run the following command in the terminal:<br/>
+`python conjugate_gradient.py`
+
+ Once you run the command, the processor will use the function which implements the conjugate gradient method to find
+ the minimum of the function S, given in the HW description. The matrix A and the vector b for this function are calculated
+ and given directly as np.array to the code. The initial condition is a fixed point at [3.0, 1.0].
+ After running the program the number of steps and the final solution are printed on the screen and the 3D plot of the
+ surface and the path of the point during the convergence are shown in a new window.
+ The convergence of the point is exactly like the convergence of the point in execise 1 with the optimizer
+ (with the same initial condition).