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hobeg – reasonable initial changes to the variables
rhoend – final accuracy in the optimization (not precisely guaranteed)
iprint – controls the frequency of output: 0 (no output),1,2,3
maxfun – maximum number of function evaluations.
Returns:
x – the minimum
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
OpenOpt – a tool which offers a unified syntax to call this and
other solvers with possibility of automatic differentiation
SciPy Reference Guide, Release 0.8.dev
fmin_slsqp(func, x0, eqcons=, [], f_eqcons=None, ieqcons=, [], f_ieqcons=None, bounds=, [], fprime=None,
fprime_eqcons=None, fprime_ieqcons=None, args=(), iter=100, acc=9.9999999999999995e-07,
iprint=1, full_output=0, epsilon=1.4901161193847656e-08)
Minimize a function using Sequential Least SQuares Programming
Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft.
Parameters
func : callable f(x,*args)
Objective function.
x0 : ndarray of float
Initial guess for the independent variable(s).
eqcons : list
A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully
optimized problem.
f_eqcons : callable f(x,*args)
Returns an array in which each element must equal 0.0 in a successfully optimized
problem. If f_eqcons is specified, eqcons is ignored.
ieqcons : list
A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 in a successfully
optimized problem.
f_ieqcons : callable f(x0,*args)
3.12. Optimization and root finding (scipy.optimize) 309