# Source code for sherpa.optmethods

```
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"""Optimization classes.
The `OptMethod` class provides an interface to a number of optimisers.
When creating an optimizer an optional name can be added; this name is
only used in string representations of the class:
>>> from sherpa.optmethods import NelderMead
>>> opt = NelderMead()
>>> print(opt)
name = simplex
ftol = 1.1920928955078125e-07
maxfev = None
initsimplex = 0
finalsimplex = 9
step = None
iquad = 1
verbose = 0
A model is fit by providing the ``fit`` method a callback, the starting
point (parameter values), and parameter ranges. The callback should
match::
callback(pars, *statargs, **statkwargs)
and return the statistic value to minimise.
Notes
-----
Each optimizer has certain classes of problem where it is more, or
less, successful. For instance, the `NelderMead` class should
only be used with chi-square based statistics.
Examples
--------
Using Sherpa classes for data, models, and statistics we can create a
callback, in this case using a least-squared statistic to fit a
constant model to a 1D dataset (we do not need to send any extra
arguments to the callback other than the parameter values in this
case):
>>> from sherpa.data import Data1D
>>> from sherpa.models.basic import Const1D
>>> from sherpa.stats import LeastSq
>>> x = np.asarray([1, 2, 5])
>>> y = np.asarray([3, 2, 7])
>>> d = Data1D('data', x, y)
>>> mdl = Const1D()
>>> stat = LeastSq()
>>> def cb(pars):
... mdl.thawedpars = pars
... return stat.calc_stat(d, mdl)
We can check the model before the optimisaton run:
>>> print(mdl)
const1d
Param Type Value Min Max Units
----- ---- ----- --- --- -----
const1d.c0 thawed 1 -3.40282e+38 3.40282e+38
The model can be fit using the ``fit`` method:
>>> from sherpa.optmethods import NelderMead
>>> opt = NelderMead()
>>> res = opt.fit(cb, mdl.thawedpars, mdl.thawedparmins, mdl.thawedparmaxes)
The return from ``fit`` is a tuple where the first element indicates
whether the fit was successful, then the best-fit parameters, the
best-fit statistic, a string message, along with a dictionary
depending on the optimiser:
>>> print(res)
(True, array([4.]), 14.0, 'Optimization terminated successfully', {'info': True, 'nfev': 98})
>>> print(f"Best-fit value: {res[1][0]}")
Best-fit value: 4.0
We can see that the model has been updated thanks to this:
>>> print(mdl)
const1d
Param Type Value Min Max Units
----- ---- ----- --- --- -----
const1d.c0 thawed 4 -3.40282e+38 3.40282e+38
"""
import logging
import numpy
from sherpa.utils import NoNewAttributesAfterInit, \
get_keyword_names, get_keyword_defaults, print_fields
from sherpa.optmethods.optfcts import grid_search, lmdif, montecarlo, \
neldermead
warning = logging.getLogger(__name__).warning
__all__ = ('GridSearch', 'OptMethod', 'LevMar', 'MonCar', 'NelderMead')
[docs]class OptMethod(NoNewAttributesAfterInit):
"""Base class for the optimisers.
Parameters
----------
name : str
The name of the optimiser.
optfunc : function
The function which optimises the model: its arguments are
a function which evalutes the statistic given a list of parameter
values, the starting parameters, minima, and maxima, followed
by keyword arguments matching the configuration data.
"""
def __init__(self, name, optfunc):
self.name = name
self._optfunc = optfunc
self.config = self.default_config
NoNewAttributesAfterInit.__init__(self)
def __getattr__(self, name):
if name in self.__dict__.get('config', ()):
return self.config[name]
raise AttributeError("'%s' object has no attribute '%s'" %
(type(self).__name__, name))
def __setattr__(self, name, val):
if name in self.__dict__.get('config', ()):
self.config[name] = val
else:
NoNewAttributesAfterInit.__setattr__(self, name, val)
def __repr__(self):
return ("<%s optimization method instance '%s'>" %
(type(self).__name__, self.name))
# Need to support users who have pickled sessions < CIAO 4.2
def __setstate__(self, state):
new_config = get_keyword_defaults(state.get('_optfunc'))
old_config = state.get('config', {})
# remove old kw args from opt method dict
for key in old_config.keys():
if key not in new_config:
old_config.pop(key)
# add new kw args with defaults
for key, val in new_config.items():
if key not in old_config:
old_config[key] = val
self.__dict__.update(state)
def __str__(self):
names = ['name']
names.extend(get_keyword_names(self._optfunc))
# names.remove('full_output')
# Add the method's name to printed output
# Don't add to self.config b/c name isn't a
# fit function config setting
add_name_config = {}
add_name_config['name'] = self.name
add_name_config.update(self.config)
return print_fields(names, add_name_config)
def _get_default_config(self):
args = get_keyword_defaults(self._optfunc)
return args
default_config = property(_get_default_config,
doc='The default settings for the optimiser.')
[docs] def fit(self, statfunc, pars, parmins, parmaxes, statargs=(),
statkwargs={}):
"""Run the optimiser.
Parameters
----------
statfunc : function
Given a list of parameter values as the first argument and,
as the remaining positional arguments, `statargs` and
`statkwargs` as keyword arguments, return the statistic
value.
pars : sequence
The start position of the model parameter values.
parmins : sequence
The minimum allowed values for each model parameter. This
must match the length of `pars`.
parmaxes : sequence
The maximum allowed values for each model parameter. This
must match the length of `pars`.
statargs : optional
Additional positional arguments to send to `statfunc`.
statkwargs : optional
Additional keyword arguments to send to `statfunc`.
Returns
-------
newpars : tuple
The tuple contains: boolean indicating whether the
optimization succeeded or not, the best fit parameters as a
NumPy array, the statistic value at the best-fit location,
a string message indicating the status, and a dictionary
containing information about the optimisation (this depends
on the optimiser).
"""
def cb(pars):
return statfunc(pars, *statargs, **statkwargs)
output = self._optfunc(cb, pars, parmins, parmaxes, **self.config)
success = output[0]
msg = output[3]
if not success:
warning('fit failed: %s' % msg)
# Ensure that the best-fit parameters are in an array. (If there's
# only one, it might be returned as a bare float.)
output = list(output)
output[1] = numpy.asarray(output[1]).ravel()
output = tuple(output)
return output
# ## DOC-TODO: better description of the sequence argument; what happens
# ## with multiple free parameters.
# ## DOC-TODO: what does the method attribute take: string or class instance?
# ## DOC-TODO: it looks like there's no error checking on the method attribute
[docs]class GridSearch(OptMethod):
"""Grid Search optimization method.
This method evaluates the fit statistic for each point in the
parameter space grid; the best match is the grid point with the
lowest value of the fit statistic. It is intended for use with
template models as it is very inefficient for general models.
Attributes
----------
num : int
The size of the grid for each parameter when `sequence` is
`None`, so ``npar^num`` fits will be evaluated, where `npar` is
the number of free parameters. The grid spacing is uniform.
sequence : sequence of numbers or `None`
The list through which to evaluate. Leave as `None` to use
a uniform grid spacing as determined by the `num` attribute.
numcores : int or `None`
The number of CPU cores to use. The default is `1` and a
value of `None` will use all the cores on the machine.
maxfev : int or `None`
The `maxfev` attribute if `method` is not `None`.
ftol : number
The `ftol` attribute if `method` is not `None`.
method : str or `None`
The optimization method to use to refine the best-fit
location found using the grid search. If `None` then
this step is not run.
verbose: int
The amount of information to print during the fit. The default
is `0`, which means no output.
"""
def __init__(self, name='gridsearch'):
OptMethod.__init__(self, name, grid_search)
"""
LMDIF.
Page 1
Documentation for MINPACK subroutine LMDIF
Double precision version
Argonne National Laboratory
Burton S. Garbow, Kenneth E. Hillstrom, Jorge J. More
March 1980
1. Purpose.
The purpose of LMDIF is to minimize the sum of the squares of M
nonlinear functions in N variables by a modification of the
Levenberg-Marquardt algorithm. The user must provide a subrou-
tine which calculates the functions. The Jacobian is then cal-
culated by a forward-difference approximation.
2. Subroutine and type statements.
SUBROUTINE LMDIF(FCN,M,N,X,FVEC,FTOL,XTOL,GTOL,MAXFEV,EPSFCN,
* DIAG,MODE,FACTOR,NPRINT,INFO,NFEV,FJAC,LDFJAC,
* IPVT,QTF,WA1,WA2,WA3,WA4)
INTEGER M,N,MAXFEV,MODE,NPRINT,INFO,NFEV,LDFJAC
INTEGER IPVT(N)
DOUBLE PRECISION FTOL,XTOL,GTOL,EPSFCN,FACTOR
DOUBLE PRECISION X(N),FVEC(M),DIAG(N),FJAC(LDFJAC,N),QTF(N),
* WA1(N),WA2(N),WA3(N),WA4(M)
EXTERNAL FCN
3. Parameters.
Parameters designated as input parameters must be specified on
entry to LMDIF and are not changed on exit, while parameters
designated as output parameters need not be specified on entry
and are set to appropriate values on exit from LMDIF.
FCN is the name of the user-supplied subroutine which calculates
the functions. FCN must be declared in an EXTERNAL statement
in the user calling program, and should be written as follows.
SUBROUTINE FCN(M,N,X,FVEC,IFLAG)
INTEGER M,N,IFLAG
DOUBLE PRECISION X(N),FVEC(M)
----------
CALCULATE THE FUNCTIONS AT X AND
RETURN THIS VECTOR IN FVEC.
----------
RETURN
END
Page 2
The value of IFLAG should not be changed by FCN unless the
user wants to terminate execution of LMDIF. In this case set
IFLAG to a negative integer.
M is a positive integer input variable set to the number of
functions.
N is a positive integer input variable set to the number of
variables. N must not exceed M.
X is an array of length N. On input X must contain an initial
estimate of the solution vector. On output X contains the
final estimate of the solution vector.
FVEC is an output array of length M which contains the functions
evaluated at the output X.
FTOL is a nonnegative input variable. Termination occurs when
both the actual and predicted relative reductions in the sum
of squares are at most FTOL. Therefore, FTOL measures the
relative error desired in the sum of squares. Section 4 con-
tains more details about FTOL.
XTOL is a nonnegative input variable. Termination occurs when
the relative error between two consecutive iterates is at most
XTOL. Therefore, XTOL measures the relative error desired in
the approximate solution. Section 4 contains more details
about XTOL.
GTOL is a nonnegative input variable. Termination occurs when
the cosine of the angle between FVEC and any column of the
Jacobian is at most GTOL in absolute value. Therefore, GTOL
measures the orthogonality desired between the function vector
and the columns of the Jacobian. Section 4 contains more
details about GTOL.
MAXFEV is a positive integer input variable. Termination occurs
when the number of calls to FCN is at least MAXFEV by the end
of an iteration.
EPSFCN is an input variable used in determining a suitable step
for the forward-difference approximation. This approximation
assumes that the relative errors in the functions are of the
order of EPSFCN. If EPSFCN is less than the machine preci-
sion, it is assumed that the relative errors in the functions
are of the order of the machine precision.
DIAG is an array of length N. If MODE = 1 (see below), DIAG is
internally set. If MODE = 2, DIAG must contain positive
entries that serve as multiplicative scale factors for the
variables.
MODE is an integer input variable. If MODE = 1, the variables
will be scaled internally. If MODE = 2, the scaling is
Page 3
specified by the input DIAG. Other values of MODE are equiva-
lent to MODE = 1.
FACTOR is a positive input variable used in determining the ini-
tial step bound. This bound is set to the product of FACTOR
and the Euclidean norm of DIAG*X if nonzero, or else to FACTOR
itself. In most cases FACTOR should lie in the interval
(.1,100.). 100. is a generally recommended value.
NPRINT is an integer input variable that enables controlled
printing of iterates if it is positive. In this case, FCN is
called with IFLAG = 0 at the beginning of the first iteration
and every NPRINT iterations thereafter and immediately prior
to return, with X and FVEC available for printing. If NPRINT
is not positive, no special calls of FCN with IFLAG = 0 are
made.
INFO is an integer output variable. If the user has terminated
execution, INFO is set to the (negative) value of IFLAG. See
description of FCN. Otherwise, INFO is set as follows.
INFO = 0 Improper input parameters.
INFO = 1 Both actual and predicted relative reductions in the
sum of squares are at most FTOL.
INFO = 2 Relative error between two consecutive iterates is
at most XTOL.
INFO = 3 Conditions for INFO = 1 and INFO = 2 both hold.
INFO = 4 The cosine of the angle between FVEC and any column
of the Jacobian is at most GTOL in absolute value.
INFO = 5 Number of calls to FCN has reached or exceeded
MAXFEV.
INFO = 6 FTOL is too small. No further reduction in the sum
of squares is possible.
INFO = 7 XTOL is too small. No further improvement in the
approximate solution X is possible.
INFO = 8 GTOL is too small. FVEC is orthogonal to the
columns of the Jacobian to machine precision.
Sections 4 and 5 contain more details about INFO.
NFEV is an integer output variable set to the number of calls to
FCN.
FJAC is an output M by N array. The upper N by N submatrix of
FJAC contains an upper triangular matrix R with diagonal ele-
ments of nonincreasing magnitude such that
Page 4
T T T
P *(JAC *JAC)*P = R *R,
where P is a permutation matrix and JAC is the final calcu-
lated J
"""
[docs]class LevMar(OptMethod):
"""Levenberg-Marquardt optimization method.
The Levenberg-Marquardt method is an interface to the MINPACK
subroutine lmdif to find the local minimum of nonlinear least
squares functions of several variables by a modification of the
Levenberg-Marquardt algorithm [1]_.
Attributes
----------
ftol : number
The function tolerance to terminate the search for the minimum;
the default is FLT_EPSILON ~ 1.19209289551e-07, where
FLT_EPSILON is the smallest number x such that ``1.0 != 1.0 +
x``. The conditions are satisfied when both the actual and
predicted relative reductions in the sum of squares are, at
most, ftol.
xtol : number
The relative error desired in the approximate solution; default
is FLT_EPSILON ~ 1.19209289551e-07, where FLT_EPSILON
is the smallest number x such that ``1.0 != 1.0 + x``. The
conditions are satisfied when the relative error between two
consecutive iterates is, at most, `xtol`.
gtol : number
The orthogonality desired between the function vector and the
columns of the jacobian; default is FLT_EPSILON ~
1.19209289551e-07, where FLT_EPSILON is the smallest number x
such that ``1.0 != 1.0 + x``. The conditions are satisfied when
the cosine of the angle between fvec and any column of the
jacobian is, at most, `gtol` in absolute value.
maxfev : int or `None`
The maximum number of function evaluations; the default value
of `None` means to use ``1024 * n``, where `n` is the number of
free parameters.
epsfcn : number
This is used in determining a suitable step length for the
forward-difference approximation; default is FLT_EPSILON
~ 1.19209289551e-07, where FLT_EPSILON is the smallest number
x such that ``1.0 != 1.0 + x``. This approximation assumes that
the relative errors in the functions are of the order of
`epsfcn`. If `epsfcn` is less than the machine precision, it is
assumed that the relative errors in the functions are of the
order of the machine precision.
factor : int
Used in determining the initial step bound; default is 100. The
initial step bound is set to the product of `factor` and the
euclidean norm of diag*x if nonzero, or else to factor itself.
In most cases, `factor` should be from the interval (.1,100.).
numcores : int
The number of CPU cores to use. The default is `1`.
verbose: int
The amount of information to print during the fit. The default
is `0`, which means no output.
References
----------
.. [1] J.J. More, "The Levenberg Marquardt algorithm:
implementation and theory," in Lecture Notes in Mathematics
630: Numerical Analysis, G.A. Watson (Ed.),
Springer-Verlag: Berlin, 1978, pp.105-116.
"""
def __init__(self, name='levmar'):
OptMethod.__init__(self, name, lmdif)
[docs]class MonCar(OptMethod):
"""Monte Carlo optimization method.
This is an implementation of the differential-evolution algorithm
from Storn and Price (1997) [1]_. A population of fixed size -
which contains n-dimensional vectors, where n is the number of
free parameters - is randomly initialized. At each iteration, a
new n-dimensional vector is generated by combining vectors from
the pool of population, the resulting trial vector is selected if
it lowers the objective function.
Attributes
----------
ftol : number
The function tolerance to terminate the search for the minimum;
the default is sqrt(DBL_EPSILON) ~ 1.19209289551e-07, where
DBL_EPSILON is the smallest number x such that ``1.0 != 1.0 +
x``.
maxfev : int or `None`
The maximum number of function evaluations; the default value
of `None` means to use ``8192 * n``, where `n` is the number of
free parameters.
verbose: int
The amount of information to print during the fit. The default
is `0`, which means no output.
seed : int
The seed for the random number generator.
population_size : int or `None`
The population of potential solutions is allowed to evolve to
search for the minimum of the fit statistics. The trial
solution is randomly chosen from a combination from the current
population, and it is only accepted if it lowers the
statistics. A value of `None` means to use a value ``16 * n``,
where `n` is the number of free parameters.
xprob : num
The crossover probability should be within the range [0.5,1.0];
default value is 0.9. A high value for the crossover
probability should result in a faster convergence rate;
conversely, a lower value should make the differential
evolution method more robust.
weighting_factor: num
The weighting factor should be within the range [0.5, 1.0];
default is 0.8. Differential evolution is more sensitive to the
weighting_factor then the xprob parameter. A lower value for
the weighting_factor, coupled with an increase in the
population_size, gives a more robust search at the cost of
efficiency.
numcores : int
The number of CPU cores to use. The default is `1`.
References
----------
.. [1] Storn, R. and Price, K. "Differential Evolution: A Simple
and Efficient Adaptive Scheme for Global Optimization over
Continuous Spaces." J. Global Optimization 11, 341-359,
1997.
http://www.icsi.berkeley.edu/~storn/code.html
"""
def __init__(self, name='moncar'):
OptMethod.__init__(self, name, montecarlo)
# ## DOC-TODO: finalximplex=4 and 5 list the same conditions, it is likely
# ## a cut-n-paste error, so what is the correct description?
[docs]class NelderMead(OptMethod):
r"""Nelder-Mead Simplex optimization method.
The Nelder-Mead Simplex algorithm, devised by J.A. Nelder and
R. Mead [1]_, is a direct search method of optimization for
finding a local minimum of an objective function of several
variables. The implementation of the Nelder-Mead Simplex algorithm is
a variation of the algorithm outlined in [2]_ and [3]_. As noted,
terminating the simplex is not a simple task:
"For any non-derivative method, the issue of termination is
problematical as well as highly sensitive to problem scaling.
Since gradient information is unavailable, it is provably
impossible to verify closeness to optimality simply by sampling f
at a finite number of points. Most implementations of direct
search methods terminate based on two criteria intended to reflect
the progress of the algorithm: either the function values at the
vertices are close, or the simplex has become very small."
"Either form of termination-close function values or a small
simplex-can be misleading for badly scaled functions."
Attributes
----------
ftol : number
The function tolerance to terminate the search for the minimum;
the default is sqrt(DBL_EPSILON) ~ 1.19209289551e-07, where
DBL_EPSILON is the smallest number x such that ``1.0 != 1.0 +
x``.
maxfev : int or `None`
The maximum number of function evaluations; the default value
of `None` means to use ``1024 * n``, where `n` is the number of
free parameters.
initsimplex : int
Dictates how the non-degenerate initial simplex is to be
constructed. Default is `0`; see the "cases for initsimplex"
section below for details.
finalsimplex : int
At each iteration, a combination of one of the following
stopping criteria is tested to see if the simplex has converged
or not. Full details are in the "cases for finalsimplex"
section below.
step : array of number or `None`
A list of length `n` (number of free parameters) to initialize
the simplex; see the `initsimplex` for details. The default of
`None` means to use a step of 0.4 for each free parameter.
iquad : int
A boolean flag which indicates whether a fit to a quadratic
surface is done. If iquad is set to `1` (the default) then a
fit to a quadratic surface is done; if iquad is set to `0` then
the quadratic surface fit is not done. If the fit to the
quadratic surface is not positive semi-definitive, then the
search terminated prematurely. The code to fit the quadratic
surface was written by D. E. Shaw, CSIRO, Division of
Mathematics & Statistics, with amendments by
R. W. M. Wedderburn, Rothamsted Experimental Station, and Alan
Miller, CSIRO, Division of Mathematics & Statistics. See also
[1]_.
verbose : int
The amount of information to print during the fit. The default
is `0`, which means no output.
reflect : bool
When a parameter exceeds a limit should the parameter be
reflected, so moved back within bounds (`True`, the default) or
should the model evaluation return DBL_MAX, causing the current
set of parameters to be excluded from the simplex.
Notes
-----
The `initsimplex` option determines how the non-degenerate initial
simplex is to be constructed:
- when `initsimplex` is `0`:
Then x_(user_supplied) is one of the vertices of the simplex.
The other `n` vertices are::
for ( int i = 0; i < n; ++i ) {
for ( int j = 0; j < n; ++j )
x[ i + 1 ][ j ] = x_[ j ];
x[ i + 1 ][ i ] = x_[ i ] + step[ i ];
}
where step[i] is the ith element of the option step.
- if `initsimplex` is `1`:
Then x_(user_supplied) is one of the vertices of the simplex.
The other `n` vertices are::
{ x_[j] + pn, if i - 1 != j
{
x[i][j] = {
{
{ x_[j] + qn, otherwise
for 1 <= i <= n, 0 <= j < n and::
pn = ( sqrt( n + 1 ) - 1 + n ) / ( n * sqrt(2) )
qn = ( sqrt( n + 1 ) - 1 ) / ( n * sqrt(2) )
The `finalsimplex` option determines whether the simplex has
converged:
- case a (if the max length of the simplex is small enough)::
max( | x_i - x_0 | ) <= ftol max( 1, | x_0 | )
1 <= i <= n
- case b (if the standard deviation the simplex is < `ftol`)::
n - 2
=== ( f - f )
\ i 2
/ ----------- <= ftol
==== sqrt( n )
i = 0
- case c (if the function values are close enough)::
f_0 < f_(n-1) within ftol
The combination of the above stopping criteria are:
- case 0: same as case a
- case 1: case a, case b and case c have to be met
- case 2: case a and either case b or case c have to be met.
The `finalsimplex` value controls which of these criteria need to
hold:
- if ``finalsimplex=0`` then convergence is assumed if case 1 is met.
- if ``finalsimplex=1`` then convergence is assumed if case 2 is met.
- if ``finalsimplex=2`` then convergence is assumed if case 0 is met
at two consecutive iterations.
- if ``finalsimplex=3`` then convergence is assumed if case 0 then
case 1 are met on two consecutive iterations.
- if ``finalsimplex=4`` then convergence is assumed if case 0 then
case 1 then case 0 are met on three consecutive iterations.
- if ``finalsimplex=5`` then convergence is assumed if case 0 then
case 1 then case 0 are met on three consecutive iterations.
- if ``finalsimplex=6`` then convergence is assumed if case 1 then
case 1 then case 0 are met on three consecutive iterations.
- if ``finalsimplex=7`` then convergence is assumed if case 2 then
case 1 then case 0 are met on three consecutive iterations.
- if ``finalsimplex=8`` then convergence is assumed if case 0 then
case 2 then case 0 are met on three consecutive iterations.
- if ``finalsimplex=9`` then convergence is assumed if case 0 then
case 1 then case 1 are met on three consecutive iterations.
- if ``finalsimplex=10`` then convergence is assumed if case 0 then
case 2 then case 1 are met on three consecutive iterations.
- if ``finalsimplex=11`` then convergence is assumed if case 1 is
met on three consecutive iterations.
- if ``finalsimplex=12`` then convergence is assumed if case 1 then
case 2 then case 1 are met on three consecutive iterations.
- if ``finalsimplex=13`` then convergence is assumed if case 2 then
case 1 then case 1 are met on three consecutive iterations.
- otherwise convergence is assumed if case 2 is met on three
consecutive iterations.
References
----------
.. [1] "A simplex method for function minimization", J.A. Nelder
and R. Mead (Computer Journal, 1965, vol 7, pp 308-313)
https://doi.org/10.1093%2Fcomjnl%2F7.4.308
.. [2] "Convergence Properties of the Nelder-Mead Simplex
Algorithm in Low Dimensions", Jeffrey C. Lagarias, James
A. Reeds, Margaret H. Wright, Paul E. Wright , SIAM Journal
on Optimization, Vol. 9, No. 1 (1998), pages 112-147.
http://citeseer.ist.psu.edu/3996.html
.. [3] "Direct Search Methods: Once Scorned, Now Respectable"
Wright, M. H. (1996) in Numerical Analysis 1995
(Proceedings of the 1995 Dundee Biennial Conference in
Numerical Analysis, D.F. Griffiths and G.A. Watson, eds.),
191-208, Addison Wesley Longman, Harlow, United Kingdom.
http://citeseer.ist.psu.edu/155516.html
"""
def __init__(self, name='simplex'):
OptMethod.__init__(self, name, neldermead)
###############################################################################
# # from sherpa.optmethods.fminpowell import *
# # from sherpa.optmethods.nmpfit import *
# # from sherpa.optmethods.odrpack import odrpack
# # from sherpa.optmethods.stogo import stogo
# # from sherpa.optmethods.chokkan import chokkanlbfgs
# # from sherpa.optmethods.odr import odrf77
# # def myall( targ, arg ):
# # fubar = list( targ )
# # fubar.append( arg )
# # return tuple( fubar )
# # __all__ = myall( __all__, 'Bobyqa' )
# # __all__ = myall( __all__, 'Chokkan' )
# # __all__ = myall( __all__, 'cppLevMar' )
# # __all__ = myall( __all__, 'Dif_Evo' )
# # __all__ = myall( __all__, 'MarLev' )
# # __all__ = myall( __all__, 'MyMinim' )
# # __all__ = myall( __all__, 'Nelder_Mead' )
# # __all__ = myall( __all__, 'NMPFIT' )
# # __all__ = myall( __all__, 'Newuoa' )
# # __all__ = myall( __all__, 'Odr' )
# # __all__ = myall( __all__, 'OdrPack' )
# # __all__ = myall( __all__, 'PortChi' )
# # __all__ = myall( __all__, 'PortFct' )
# # __all__ = myall( __all__, 'ScipyPowell' )
# # __all__ = myall( __all__, 'StoGo' )
# # class Chokkan(OptMethod):
# # def __init__(self, name='chokkan'):
# # OptMethod.__init__(self, name, chokkanlbfgs)
# # class cppLevMar(OptMethod):
# # def __init__(self, name='clevmar'):
# # OptMethod.__init__(self, name, optfcts.lmdif_cpp)
# # class MyMinim(OptMethod):
# # def __init__(self, name='simplex'):
# # OptMethod.__init__(self, name, minim)
# # class NMPFIT(OptMethod):
# # def __init__(self, name='pytools_nmpfit'):
# # OptMethod.__init__(self, name, nmpfit.pytools_nmpfit)
# # class OdrPack(OptMethod):
# # def __init__(self, name='odrpack'):
# # OptMethod.__init__(self, name, odrpack)
# # class Odr(OptMethod):
# # def __init__(self, name='odr'):
# # OptMethod.__init__(self, name, odrf77)
# # class ScipyPowell(OptMethod):
# # def __init__(self, name='scipypowell'):
# # OptMethod.__init__(self, name, my_fmin_powell)
# # class StoGo(OptMethod):
# # def __init__(self, name='stogo'):
# # OptMethod.__init__(self, name, stogo)
###############################################################################
```