How to use scipy.optimization.brute for multivariable function

[Shiladitya]

You need to define ranges where brute will search for an optimum, e.g.

grid = ((-1, 1, 0.1), (-1, 1, 0.1)) 
# Also, you can use slice objects for this:
# grid = (slice(-1, 1, 0.1), slice(-1, 1, 0.1))

Further, you need to define a function to be optimized:

def f(x, T):
    return (x[0] - T) ** 2 + (x[1] - T) ** 2

And, finally, use brute to find the minimum:

from scipy.optimize import brute
brute(f, grid, (1,))

Thanks a lot for your kind reply.


And I have a question-

brute(f, grid, (1,))

What does "(1,)" stand for? Is it the value of parameter 'T'?

And when defined the function, you used x[0] and x[1]; why?
Mathematically the function is 'f(x,y,T)=(x - T)^2 + (y - T)^2.
Am I right?