Class FminLBFGSB
source code
object --+
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FminWrapper --+
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FminLBFGSB
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 func using the L-BFGS-B algorithm.
Parameters
----------
func : callable f(x, *args)
Function to minimise.
x0 : ndarray
Initial guess.
fprime : callable fprime(x, *args)
The gradient of `func`. If None, then `func` returns the function
value and the gradient (``f, g = func(x, *args)``), unless
`approx_grad` is True in which case `func` returns only ``f``.
args : tuple
Arguments to pass to `func` and `fprime`.
approx_grad : bool
Whether to approximate the gradient numerically (in which case
`func` returns only the function value).
bounds : list
``(min, max)`` pairs for each element in ``x``, defining
the bounds on that parameter. Use None for one of ``min`` or
``max`` when there is no bound in that direction.
m : int
The maximum number of variable metric corrections
used to define the limited memory matrix. (The limited memory BFGS
method does not store the full hessian but uses this many terms in an
approximation to it.)
factr : float
The iteration stops when
``(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps``,
where ``eps`` is the machine precision, which is automatically
generated by the code. Typical values for `factr` are: 1e12 for
low accuracy; 1e7 for moderate accuracy; 10.0 for extremely
high accuracy.
pgtol : float
The iteration will stop when
``max{|proj g_i | i = 1, ..., n} <= pgtol``
where ``pg_i`` is the i-th component of the projected gradient.
epsilon : float
Step size used when `approx_grad` is True, for numerically
calculating the gradient
iprint : int
Controls the frequency of output. ``iprint < 0`` means no output.
disp : int, optional
If zero, then no output. If positive number, then this over-rides
`iprint`.
maxfun : int
Maximum number of function evaluations.
Returns
-------
x : ndarray
Estimated position of the minimum.
f : float
Value of `func` at the minimum.
d : dict
Information dictionary.
* d['warnflag'] is
- 0 if converged,
- 1 if too many function evaluations,
- 2 if stopped for another reason, given in d['task']
* d['grad'] is the gradient at the minimum (should be 0 ish)
* d['funcalls'] is the number of function calls made.
Notes
-----
License of L-BFGS-B (Fortran code):
The version included here (in fortran code) is 2.1 (released in 1997).
It was written by Ciyou Zhu, Richard Byrd, and Jorge Nocedal
<nocedal@ece.nwu.edu>. It carries the following condition for use:
This software is freely available, but we expect that all publications
describing work using this software , or all commercial products using it,
quote at least one of the references given below.
References
----------
* R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound
Constrained Optimization, (1995), SIAM Journal on Scientific and
Statistical Computing , 16, 5, pp. 1190-1208.
* C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778: L-BFGS-B,
FORTRAN routines for large scale bound constrained optimization (1997),
ACM Transactions on Mathematical Software, Vol 23, Num. 4, pp. 550 - 560.
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Inherited from FminWrapper :
__init__ ,
first_guess
Inherited from object :
__delattr__ ,
__format__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__sizeof__ ,
__str__ ,
__subclasshook__
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__doc__ = FminWrapper.__doc__+ opt.fmin_l_bfgs_b.__doc__
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Inherited from object :
__class__
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