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 = -Const(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'
Inputs:
func -- Function to be optimized
x0 -- Parameters to be optimized over
args -- Extra parameters to function
schedule -- Annealing schedule to use (a class)
full_output -- Return optional outputs
T0 -- Initial Temperature (estimated as 1.2 times the largest
cost-function deviation over random points in the range)
Tf -- Final goal temperature
maxeval -- Maximum function evaluations
maxaccept -- Maximum changes to accept
maxiter -- Maximum cooling iterations
learn_rate -- scale constant for adjusting guesses
boltzmann -- Boltzmann constant in acceptance test
(increase for less stringent test at each temperature).
feps -- Stopping relative error tolerance for the function value in
last four coolings.
quench, m, n -- Parameters to alter fast_sa schedule
lower, upper -- lower and upper bounds on x0 (scalar or array).
dwell -- The number of times to search the space at each temperature.
Outputs: (xmin, {Jmin, T, feval, iters, accept,} retval)
xmin -- Point giving smallest value found
retval -- 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 -- Minimum value of function found
T -- final temperature
feval -- Number of function evaluations
iters -- Number of cooling iterations
accept -- Number of tests accepted.
See also:
fmin, fmin_powell, fmin_cg,
fmin_bfgs, fmin_ncg -- multivariate local optimizers
leastsq -- nonlinear least squares minimizer
fmin_l_bfgs_b, fmin_tnc,
fmin_cobyla -- constrained multivariate optimizers
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17