scipy tutorial - Baustatik-Info-Server

Other Parameters:
gcalls
[int] Number of gradient calls made.
hcalls
[int] Number of hessian calls made.
SciPy Reference Guide, Release 0.8.dev
warnflag
[int] Warnings generated by the algorithm. 1 : Maximum number of iterations exceeded.
allvecs
[list] The result at each iteration, if retall is True (see below).
avextol
[float] Convergence is assumed when the average relative error in the minimizer falls below
this amount.
maxiter
[int] Maximum number of iterations to perform.
full_output
[bool] If True, return the optional outputs.
disp
[bool] If True, print convergence message.
retall
[bool] If True, return a list of results at each iteration.
Notes
1. Only one of fhess_p or fhess need to be given. If fhess is provided, then fhess_p will be
ignored. If neither fhess nor fhess_p is provided, then the hessian product will be approximated
using finite differences on fprime. fhess_p must compute the hessian times an arbitrary vector.
If it is not given, finite-differences on fprime are used to compute it. See Wright, and Nocedal
‘Numerical Optimization’, 1999, pg. 140. 2. Check OpenOpt - a tool which offers a unified
syntax to call this and other solvers with possibility of automatic differentiation.
leastsq(func, x0, args=(), Dfun=None, full_output=0, col_deriv=0, ftol=1.49012e-08, xtol=1.49012e-08,
gtol=0.0, maxfev=0, epsfcn=0.0, factor=100, diag=None, warning=True)
Minimize the sum of squares of a set of equations.
x = arg min(sum(func(y)**2,axis=0))
y
Parameters
func : callable
x0 : :
args : :
should take at least one (possibly length N vector) argument and returns M floating
point numbers.
The starting estimate for the minimization.
Any extra arguments to func are placed in this tuple.
Dfun : callable
A function or method to compute the Jacobian of func with derivatives across the
rows. If this is None, the Jacobian will be estimated.
3.12. Optimization and root finding (scipy.optimize) 303