- func : callable func(x, *args)
Function to minimize. Should return f and g, where f is
the value of the function and g its gradient (a list of
floats). If the function returns None, the minimization
is aborted.
- x0 : list of floats
Initial estimate of minimum.
- fprime : callable fprime(x, *args)
Gradient of func. If None, then func must return the
function value and the gradient (f,g = func(x, *args)).
- args : tuple
Arguments to pass to function.
- approx_grad : bool
If true, approximate the gradient numerically.
- bounds : list
(min, max) pairs for each element in x, defining the
bounds on that parameter. Use None or +/-inf for one of
min or max when there is no bound in that direction.
- scale : list of floats
Scaling factors to apply to each variable. If None, the
factors are up-low for interval bounded variables and
1+|x] fo the others. Defaults to None
- offset : float
Value to substract from each variable. If None, the
offsets are (up+low)/2 for interval bounded variables
and x for the others.
- messages :
Bit mask used to select messages display during
minimization values defined in the MSGS dict. Defaults to
MGS_ALL.
- maxCGit : int
Maximum number of hessian*vector evaluations per main
iteration. If maxCGit == 0, the direction chosen is
-gradient if maxCGit < 0, maxCGit is set to
max(1,min(50,n/2)). Defaults to -1.
- maxfun : int
Maximum number of function evaluation. if None, maxfun is
set to max(100, 10*len(x0)). Defaults to None.
- eta : float
Severity of the line search. if < 0 or > 1, set to 0.25.
Defaults to -1.
- stepmx : float
Maximum step for the line search. May be increased during
call. If too small, it will be set to 10.0. Defaults to 0.
- accuracy : float
Relative precision for finite difference calculations. If
<= machine_precision, set to sqrt(machine_precision).
Defaults to 0.
- fmin : float
Minimum function value estimate. Defaults to 0.
- ftol : float
Precision goal for the value of f in the stoping criterion.
If ftol < 0.0, ftol is set to 0.0 defaults to -1.
- xtol : float
Precision goal for the value of x in the stopping
criterion (after applying x scaling factors). If xtol <
0.0, xtol is set to sqrt(machine_precision). Defaults to
-1.
- pgtol : float
Precision goal for the value of the projected gradient in
the stopping criterion (after applying x scaling factors).
If pgtol < 0.0, pgtol is set to 1e-2 * sqrt(accuracy).
Setting it to 0.0 is not recommended. Defaults to -1.
- rescale : float
Scaling factor (in log10) used to trigger f value
rescaling. If 0, rescale at each iteration. If a large
value, never rescale. If < 0, rescale is set to 1.3.