scipy.optimize.fmin¶
- scipy.optimize.fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None)[source]¶
Minimize a function using the downhill simplex algorithm.
This algorithm only uses function values, not derivatives or second derivatives.
Parameters: func : callable func(x,*args)
The objective function to be minimized.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to func, i.e. f(x,*args).
callback : callable, optional
Called after each iteration, as callback(xk), where xk is the current parameter vector.
xtol : float, optional
Relative error in xopt acceptable for convergence.
ftol : number, optional
Relative error in func(xopt) acceptable for convergence.
maxiter : int, optional
Maximum number of iterations to perform.
maxfun : number, optional
Maximum number of function evaluations to make.
full_output : bool, optional
Set to True if fopt and warnflag outputs are desired.
disp : bool, optional
Set to True to print convergence messages.
retall : bool, optional
Set to True to return list of solutions at each iteration.
Returns: xopt : ndarray
Parameter that minimizes function.
fopt : float
Value of function at minimum: fopt = func(xopt).
iter : int
Number of iterations performed.
funcalls : int
Number of function calls made.
warnflag : int
1 : Maximum number of function evaluations made. 2 : Maximum number of iterations reached.
allvecs : list
Solution at each iteration.
See also
- minimize
- Interface to minimization algorithms for multivariate functions. See the ‘Nelder-Mead’ method in particular.
Notes
Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables.
This algorithm has a long history of successful use in applications. But it will usually be slower than an algorithm that uses first or second derivative information. In practice it can have poor performance in high-dimensional problems and is not robust to minimizing complicated functions. Additionally, there currently is no complete theory describing when the algorithm will successfully converge to the minimum, or how fast it will if it does.
References
[R144] Nelder, J.A. and Mead, R. (1965), “A simplex method for function minimization”, The Computer Journal, 7, pp. 308-313 [R145] Wright, M.H. (1996), “Direct Search Methods: Once Scorned, Now Respectable”, in Numerical Analysis 1995, Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis, D.F. Griffiths and G.A. Watson (Eds.), Addison Wesley Longman, Harlow, UK, pp. 191-208.