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, initial_simplex=None)[source]#
Minimize a function using the downhill simplex algorithm.
This algorithm only uses function values, not derivatives or second derivatives.
- Parameters:
- funccallable func(x,*args)
The objective function to be minimized.
- x0ndarray
Initial guess.
- argstuple, optional
Extra arguments passed to func, i.e.,
f(x,*args)
.- xtolfloat, optional
Absolute error in xopt between iterations that is acceptable for convergence.
- ftolnumber, optional
Absolute error in func(xopt) between iterations that is acceptable for convergence.
- maxiterint, optional
Maximum number of iterations to perform.
- maxfunnumber, optional
Maximum number of function evaluations to make.
- full_outputbool, optional
Set to True if fopt and warnflag outputs are desired.
- dispbool, optional
Set to True to print convergence messages.
- retallbool, optional
Set to True to return list of solutions at each iteration.
- callbackcallable, optional
Called after each iteration, as callback(xk), where xk is the current parameter vector.
- initial_simplexarray_like of shape (N + 1, N), optional
Initial simplex. If given, overrides x0.
initial_simplex[j,:]
should contain the coordinates of the jth vertex of theN+1
vertices in the simplex, whereN
is the dimension.
- Returns:
- xoptndarray
Parameter that minimizes function.
- foptfloat
Value of function at minimum:
fopt = func(xopt)
.- iterint
Number of iterations performed.
- funcallsint
Number of function calls made.
- warnflagint
1 : Maximum number of function evaluations made. 2 : Maximum number of iterations reached.
- allvecslist
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. Both the ftol and xtol criteria must be met for convergence.
References
[1]Nelder, J.A. and Mead, R. (1965), “A simplex method for function minimization”, The Computer Journal, 7, pp. 308-313
[2]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.
Examples
>>> def f(x): ... return x**2
>>> from scipy import optimize
>>> minimum = optimize.fmin(f, 1) Optimization terminated successfully. Current function value: 0.000000 Iterations: 17 Function evaluations: 34 >>> minimum[0] -8.8817841970012523e-16