scipy.optimize.anneal(func, x0, args=(), schedule='fast', full_output=0, T0=None, Tf=9.9999999999999998e-13, maxeval=None, maxaccept=None, maxiter=400, boltzmann=1.0, learn_rate=0.5, feps=9.9999999999999995e-07, quench=1.0, m=1.0, n=1.0, lower=-100, upper=100, dwell=50)

Minimize a function using simulated annealing.

Schedule is a schedule class implementing the annealing schedule. Available ones are ‘fast’, ‘cauchy’, ‘boltzmann’

Parameters :

func : callable f(x, *args)

Function to be optimized.

x0 : ndarray

Initial guess.

args : tuple

Extra parameters to func.

schedule : base_schedule

Annealing schedule to use (a class).

full_output : bool

Whether to return optional outputs.

T0 : float

Initial Temperature (estimated as 1.2 times the largest cost-function deviation over random points in the range).

Tf : float

Final goal temperature.

maxeval : int

Maximum function evaluations.

maxaccept : int

Maximum changes to accept.

maxiter : int

Maximum cooling iterations.

learn_rate : float

Scale constant for adjusting guesses.

boltzmann : float

Boltzmann constant in acceptance test (increase for less stringent test at each temperature).

feps : float

Stopping relative error tolerance for the function value in last four coolings.

quench, m, n : float

Parameters to alter fast_sa schedule.

lower, upper : float or ndarray

Lower and upper bounds on x.

dwell : int

The number of times to search the space at each temperature.

Returns :

xmin : ndarray

Point giving smallest value found.

retval : int

Flag indicating stopping condition:

0 : Cooled to global optimum
1 : Cooled to final temperature
2 : Maximum function evaluations
3 : Maximum cooling iterations reached
4 : Maximum accepted query locations reached

Jmin : float

Minimum value of function found.

T : float

Final temperature.

feval : int

Number of function evaluations.

iters : int

Number of cooling iterations.

accept : int

Number of tests accepted.

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