This is one of the 100 recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python.

- We import the libraries.

In [ ]:

```
import numpy as np
import scipy as sp
import scipy.optimize as opt
import matplotlib.pyplot as plt
%matplotlib inline
```

- First, let's define a simple mathematical function (the opposite of the
**cardinal sine**). This function has many local minima but a single global minimum. (http://en.wikipedia.org/wiki/Sinc_function)

In [ ]:

```
f = lambda _: 1-np.sin(_)/_
```

- Let's plot this function on the interval $[-20, 20]$.

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```
x = np.linspace(-20., 20., 1000)
y = f(x)
```

In [ ]:

```
plt.figure(figsize=(5,5));
plt.plot(x, y);
```

- The
`scipy.optimize`

module comes with many function minimization routines. The`minimize`

function offers a unified interface to many algorithms. The**Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm**(default in`minimize`

) gives good results in general. The`minimize`

function requires an initial point as argument. For scalar univariate functions, we can also use`minimize_scalar`

.

In [ ]:

```
x0 = 3
xmin = opt.minimize(f, x0).x
```

Starting from $x_0=3$, the algorithm was able to find the actual global minimum, as shown on the following figure.

In [ ]:

```
plt.figure(figsize=(5,5));
plt.plot(x, y);
plt.scatter(x0, f(x0), marker='o', s=300);
plt.scatter(xmin, f(xmin), marker='v', s=300);
plt.xlim(-20, 20);
```

- Now, if we start from an initial point that is further away from the actual global minimum, the algorithm converges towards a
*local*minimum only.

In [ ]:

```
x0 = 10
xmin = opt.minimize(f, x0).x
```

In [ ]:

```
plt.figure(figsize=(5,5));
plt.plot(x, y);
plt.scatter(x0, f(x0), marker='o', s=300);
plt.scatter(xmin, f(xmin), marker='v', s=300);
plt.xlim(-20, 20);
```

- Like most function minimization algorithms, the BFGS algorithm is efficient at finding
*local*minima, but not necessarily*global*minima, especially on complicated or noisy objective functions. A general strategy to overcome this problem consists in combining such algorithms with an exploratory grid search on the initial points. Another option is to use a different class of algorithms based on heuristics and stochastic methods. A popular example is the**simulated annealing method**.

In [ ]:

```
xmin = opt.minimize(f, x0, method='Anneal').x
```

In [ ]:

```
plt.figure(figsize=(5,5));
plt.plot(x, y);
plt.scatter(x0, f(x0), marker='o', s=300);
plt.scatter(xmin, f(xmin), marker='v', s=300);
plt.xlim(-20, 20);
```

This time, the algorithm was able to find the global minimum.

- Now, let's define a new function, in two dimensions this time. This function is called the
**Lévi function**. It is defined by

This function is very irregular and may be difficult to minimize in general. It is one of the many **test functions for optimization** that researchers have developed to study and benchmark optimization algorithms. (http://en.wikipedia.org/wiki/Test_functions_for_optimization)

In [ ]:

```
def g(X):
# X is a 2*N matrix, each column contains
# x and y coordinates.
x, y = X
return np.sin(3*np.pi*x)**2+(x-1)**2*(1+np.sin(3*np.pi*y)**2)+(y-1)**2*(1+np.sin(2*np.pi*y)**2)
```

- Let's display this function with
`imshow`

, on the square $[-10,10]^2$.

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```
n = 200
k = 10
X, Y = np.mgrid[-k:k:n*1j,-k:k:n*1j]
```

In [ ]:

```
Z = g(np.vstack((X.ravel(), Y.ravel()))).reshape(n,n)
```

In [ ]:

```
plt.figure(figsize=(5, 5));
# We use a logarithmic scale for the color here.
plt.imshow(np.log(Z), cmap=plt.cm.hot_r);
plt.xticks([]); plt.yticks([]);
```

- The BFGS algorithm also works in multiple dimensions.

In [ ]:

```
x0, y0 = opt.minimize(g, (8, 3)).x
```

In [ ]:

```
plt.figure(figsize=(5, 5));
plt.imshow(np.log(Z), cmap=plt.cm.hot_r,
extent=(-k, k, -k, k), origin=0);
plt.scatter(x0, y0, s=100);
plt.xticks([]); plt.yticks([]);
```

You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).

IPython Cookbook, by Cyrille Rossant, Packt Publishing, 2014 (500 pages).