Class FminTNC

source code

 object --+    
          |    
FminWrapper --+
              |
             FminTNC

Abstract class to generate wrappers around scipy.optimize fmin_*
functions.

Parameters
-----------

criterion : Criterion
    A criterion function with __call__ and gradient methods.
x0 : ndarray (None)
    First guess
args=() : tuple
    Extra arguments for the criterion function
kwargs : dict
    Parameters of the fmin_function

fmin function docstring
------------------------

Minimize a function with variables subject to bounds, using
gradient information.

Parameters
----------
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.
disp : int
    Integer interface to messages.  0 = no message, 5 = all messages
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.

Returns
-------
x : list of floats
    The solution.
nfeval : int
    The number of function evaluations.
rc : int
    Return code as defined in the RCSTRINGS dict.