scipy.optimize.fmin_powell¶
- scipy.optimize.fmin_powell(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None, direc=None)[source]¶
Minimize a function using modified Powell’s method. This method only uses function values, not derivatives.
Parameters: func : callable f(x,*args)
Objective function to be minimized.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to func.
callback : callable, optional
An optional user-supplied function, called after each iteration. Called as callback(xk), where xk is the current parameter vector.
direc : ndarray, optional
Initial direction set.
xtol : float, optional
Line-search error tolerance.
ftol : float, optional
Relative error in func(xopt) acceptable for convergence.
maxiter : int, optional
Maximum number of iterations to perform.
maxfun : int, optional
Maximum number of function evaluations to make.
full_output : bool, optional
If True, fopt, xi, direc, iter, funcalls, and warnflag are returned.
disp : bool, optional
If True, print convergence messages.
retall : bool, optional
If True, return a list of the solution at each iteration.
Returns: xopt : ndarray
Parameter which minimizes func.
fopt : number
Value of function at minimum: fopt = func(xopt).
direc : ndarray
Current direction set.
iter : int
Number of iterations.
funcalls : int
Number of function calls made.
warnflag : int
- Integer warning flag:
1 : Maximum number of function evaluations. 2 : Maximum number of iterations.
allvecs : list
List of solutions at each iteration.
See also
- minimize
- Interface to unconstrained minimization algorithms for multivariate functions. See the ‘Powell’ method in particular.
Notes
Uses a modification of Powell’s method to find the minimum of a function of N variables. Powell’s method is a conjugate direction method.
The algorithm has two loops. The outer loop merely iterates over the inner loop. The inner loop minimizes over each current direction in the direction set. At the end of the inner loop, if certain conditions are met, the direction that gave the largest decrease is dropped and replaced with the difference between the current estimated x and the estimated x from the beginning of the inner-loop.
The technical conditions for replacing the direction of greatest increase amount to checking that
- No further gain can be made along the direction of greatest increase from that iteration.
- The direction of greatest increase accounted for a large sufficient fraction of the decrease in the function value from that iteration of the inner loop.
References
Powell M.J.D. (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives, Computer Journal, 7 (2):155-162.
Press W., Teukolsky S.A., Vetterling W.T., and Flannery B.P.: Numerical Recipes (any edition), Cambridge University Press