SciPy

scipy.optimize.brute

scipy.optimize.brute(func, ranges, args=(), Ns=20, full_output=0, finish=<function fmin at 0x4b1b668>, disp=False)[source]

Minimize a function over a given range by brute force.

Uses the “brute force” method, i.e. computes the function’s value at each point of a multidimensional grid of points, to find the global minimum of the function.

Parameters :

func : callable

The objective function to be minimized. Must be in the form f(x, *args), where x is the argument in the form of a 1-D array and args is a tuple of any additional fixed parameters needed to completely specify the function.

ranges : tuple

Each component of the ranges tuple must be either a “slice object” or a range tuple of the form (low, high). The program uses these to create the grid of points on which the objective function will be computed. See Note 2 for more detail.

args : tuple, optional

Any additional fixed parameters needed to completely specify the function.

Ns : int, optional

Number of grid points along the axes, if not otherwise specified. See Note2.

full_output : bool, optional

If True, return the evaluation grid and the objective function’s values on it.

finish : callable, optional

An optimization function that is called with the result of brute force minimization as initial guess. finish should take the initial guess as positional argument, and take args, full_output and disp as keyword arguments. Use None if no “polishing” function is to be used. See Notes for more details.

disp : bool, optional

Set to True to print convergence messages.

Returns :

x0 : ndarray

A 1-D array containing the coordinates of a point at which the objective function had its minimum value. (See Note 1 for which point is returned.)

fval : float

Function value at the point x0.

grid : tuple

Representation of the evaluation grid. It has the same length as x0. (Returned when full_output is True.)

Jout : ndarray

Function values at each point of the evaluation grid, i.e., Jout = func(*grid). (Returned when full_output is True.)

See also

anneal
Another approach to seeking the global minimum of

multivariate, multimodal

Notes

Note 1: The program finds the gridpoint at which the lowest value of the objective function occurs. If finish is None, that is the point returned. When the global minimum occurs within (or not very far outside) the grid’s boundaries, and the grid is fine enough, that point will be in the neighborhood of the gobal minimum.

However, users often employ some other optimization program to “polish” the gridpoint values, i.e., to seek a more precise (local) minimum near brute’s best gridpoint. The brute function’s finish option provides a convenient way to do that. Any polishing program used must take brute’s output as its initial guess as a positional argument, and take brute’s input values for args and full_output as keyword arguments, otherwise an error will be raised.

brute assumes that the finish function returns a tuple in the form: (xmin, Jmin, ... , statuscode), where xmin is the minimizing value of the argument, Jmin is the minimum value of the objective function, ”...” may be some other returned values (which are not used by brute), and statuscode is the status code of the finish program.

Note that when finish is not None, the values returned are those of the finish program, not the gridpoint ones. Consequently, while brute confines its search to the input grid points, the finish program’s results usually will not coincide with any gridpoint, and may fall outside the grid’s boundary.

Note 2: The grid of points is a numpy.mgrid object. For brute the ranges and Ns inputs have the following effect. Each component of the ranges tuple can be either a slice object or a two-tuple giving a range of values, such as (0, 5). If the component is a slice object, brute uses it directly. If the component is a two-tuple range, brute internally converts it to a slice object that interpolates Ns points from its low-value to its high-value, inclusive.

Examples

We illustrate the use of brute to seek the global minimum of a function of two variables that is given as the sum of a positive-definite quadratic and two deep “Gaussian-shaped” craters. Specifically, define the objective function f as the sum of three other functions, f = f1 + f2 + f3. We suppose each of these has a signature (z, *params), where z = (x, y), and params and the functions are as defined below.

>>> params = (2, 3, 7, 8, 9, 10, 44, -1, 2, 26, 1, -2, 0.5)
>>> def f1(z, *params):
...     x, y = z
...     a, b, c, d, e, f, g, h, i, j, k, l, scale = params
...     return (a * x**2 + b * x * y + c * y**2 + d*x + e*y + f)
>>> def f2(z, *params):
...     x, y = z
...     a, b, c, d, e, f, g, h, i, j, k, l, scale = params
...     return (-g*np.exp(-((x-h)**2 + (y-i)**2) / scale))
>>> def f3(z, *params):
...     x, y = z
...     a, b, c, d, e, f, g, h, i, j, k, l, scale = params
...     return (-j*np.exp(-((x-k)**2 + (y-l)**2) / scale))
>>> def f(z, *params):
...     x, y = z
...     a, b, c, d, e, f, g, h, i, j, k, l, scale = params
...     return f1(z, *params) + f2(z, *params) + f3(z, *params)

Thus, the objective function may have local minima near the minimum of each of the three functions of which it is composed. To use fmin to polish its gridpoint result, we may then continue as follows:

>>> rranges = (slice(-4, 4, 0.25), slice(-4, 4, 0.25))
>>> from scipy import optimize
>>> resbrute = optimize.brute(f, rranges, args=params, full_output=True,
                              finish=optimize.fmin)
>>> resbrute[0]  # global minimum
array([-1.05665192,  1.80834843])
>>> resbrute[1]  # function value at global minimum
-3.4085818767

Note that if finish had been set to None, we would have gotten the gridpoint [-1.0 1.75] where the rounded function value is -2.892.