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	code created by Marti Bosch (marti.bosch@epfl.ch) and Marc Schwaerzel (marc.schwaerzel@epfl.ch)
	For this exercice `python3` was used with `-- coding: utf-8 --`. The `pip` dependences can be found in the `requirements.txt` file in the parent directory of this repo.

	# Exercise 1

	1. run the `optimizer.py`: it will calculate and plot on its own.

	# Exercise 2

	1. run the `conjugate_gradient.py` code, it will plot on its own. You might edit `A`, `b` and `x0` in the `__main__` of the `conjugate_gradient.py` file as you wish.
	2. To solve the function
	<a href="https://www.codecogs.com/eqnedit.php?latex=S(\vec{x})" target="_blank"><img src="https://latex.codecogs.com/gif.latex?S(\vec{x})" title="S(\vec{x})" /></a>
	from Exercise 1, the `__main__` from `conjugate_gradient.py` sets by default `A` to the function's Jacobian and `b` to
	<a href="https://www.codecogs.com/eqnedit.php?latex=A \vec{x} - \nabla S(\vec{x})" target="_blank"><img src="https://latex.codecogs.com/gif.latex?A \vec{x} - \nabla S(\vec{x})" title="A \vec{x} - \nabla S(\vec{x})" /></a>
	3. just run and appreciate :)