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# TNC Python interface
# @(#) $Jeannot: tnc.py,v 1.11 2005/01/28 18:27:31 js Exp $
# Copyright (c) 2004-2005, Jean-Sebastien Roy (js@jeannot.org)
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
TNC: A Python interface to the TNC non-linear optimizer
TNC is a non-linear optimizer. To use it, you must provide a function to
minimize. The function must take one argument: the list of coordinates where to
evaluate the function; and it must return either a tuple, whose first element is the
value of the function, and whose second argument is the gradient of the function
(as a list of values); or None, to abort the minimization.
"""
from scipy.optimize import moduleTNC
from .optimize import (MemoizeJac, OptimizeResult, _check_unknown_options,
_prepare_scalar_function)
from ._constraints import old_bound_to_new
from numpy import inf, array, zeros, asfarray
__all__ = ['fmin_tnc']
MSG_NONE = 0 # No messages
MSG_ITER = 1 # One line per iteration
MSG_INFO = 2 # Informational messages
MSG_VERS = 4 # Version info
MSG_EXIT = 8 # Exit reasons
MSG_ALL = MSG_ITER + MSG_INFO + MSG_VERS + MSG_EXIT
MSGS = {
MSG_NONE: "No messages",
MSG_ITER: "One line per iteration",
MSG_INFO: "Informational messages",
MSG_VERS: "Version info",
MSG_EXIT: "Exit reasons",
MSG_ALL: "All messages"
}
INFEASIBLE = -1 # Infeasible (lower bound > upper bound)
LOCALMINIMUM = 0 # Local minimum reached (|pg| ~= 0)
FCONVERGED = 1 # Converged (|f_n-f_(n-1)| ~= 0)
XCONVERGED = 2 # Converged (|x_n-x_(n-1)| ~= 0)
MAXFUN = 3 # Max. number of function evaluations reached
LSFAIL = 4 # Linear search failed
CONSTANT = 5 # All lower bounds are equal to the upper bounds
NOPROGRESS = 6 # Unable to progress
USERABORT = 7 # User requested end of minimization
RCSTRINGS = {
INFEASIBLE: "Infeasible (lower bound > upper bound)",
LOCALMINIMUM: "Local minimum reached (|pg| ~= 0)",
FCONVERGED: "Converged (|f_n-f_(n-1)| ~= 0)",
XCONVERGED: "Converged (|x_n-x_(n-1)| ~= 0)",
MAXFUN: "Max. number of function evaluations reached",
LSFAIL: "Linear search failed",
CONSTANT: "All lower bounds are equal to the upper bounds",
NOPROGRESS: "Unable to progress",
USERABORT: "User requested end of minimization"
}
# Changes to interface made by Travis Oliphant, Apr. 2004 for inclusion in
# SciPy
def fmin_tnc(func, x0, fprime=None, args=(), approx_grad=0,
bounds=None, epsilon=1e-8, scale=None, offset=None,
messages=MSG_ALL, maxCGit=-1, maxfun=None, eta=-1,
stepmx=0, accuracy=0, fmin=0, ftol=-1, xtol=-1, pgtol=-1,
rescale=-1, disp=None, callback=None):
"""
Minimize a function with variables subject to bounds, using
gradient information in a truncated Newton algorithm. This
method wraps a C implementation of the algorithm.
Parameters
----------
func : callable ``func(x, *args)``
Function to minimize. Must do one of:
1. Return f and g, where f is the value of the function and g its
gradient (a list of floats).
2. Return the function value but supply gradient function
separately as `fprime`.
3. Return the function value and set ``approx_grad=True``.
If the function returns None, the minimization
is aborted.
x0 : array_like
Initial estimate of minimum.
fprime : callable ``fprime(x, *args)``, optional
Gradient of `func`. If None, then either `func` must return the
function value and the gradient (``f,g = func(x, *args)``)
or `approx_grad` must be True.
args : tuple, optional
Arguments to pass to function.
approx_grad : bool, optional
If true, approximate the gradient numerically.
bounds : list, optional
(min, max) pairs for each element in x0, defining the
bounds on that parameter. Use None or +/-inf for one of
min or max when there is no bound in that direction.
epsilon : float, optional
Used if approx_grad is True. The stepsize in a finite
difference approximation for fprime.
scale : array_like, optional
Scaling factors to apply to each variable. If None, the
factors are up-low for interval bounded variables and
1+|x| for the others. Defaults to None.
offset : array_like, optional
Value to subtract from each variable. If None, the
offsets are (up+low)/2 for interval bounded variables
and x for the others.
messages : int, optional
Bit mask used to select messages display during
minimization values defined in the MSGS dict. Defaults to
MGS_ALL.
disp : int, optional
Integer interface to messages. 0 = no message, 5 = all messages
maxCGit : int, optional
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, optional
Maximum number of function evaluation. If None, maxfun is
set to max(100, 10*len(x0)). Defaults to None.
eta : float, optional
Severity of the line search. If < 0 or > 1, set to 0.25.
Defaults to -1.
stepmx : float, optional
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, optional
Relative precision for finite difference calculations. If
<= machine_precision, set to sqrt(machine_precision).
Defaults to 0.
fmin : float, optional
Minimum function value estimate. Defaults to 0.
ftol : float, optional
Precision goal for the value of f in the stopping criterion.
If ftol < 0.0, ftol is set to 0.0 defaults to -1.
xtol : float, optional
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, optional
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, optional
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.
callback : callable, optional
Called after each iteration, as callback(xk), where xk is the
current parameter vector.
Returns
-------
x : ndarray
The solution.
nfeval : int
The number of function evaluations.
rc : int
Return code, see below
See also
--------
minimize: Interface to minimization algorithms for multivariate
functions. See the 'TNC' `method` in particular.
Notes
-----
The underlying algorithm is truncated Newton, also called
Newton Conjugate-Gradient. This method differs from
scipy.optimize.fmin_ncg in that
1. it wraps a C implementation of the algorithm
2. it allows each variable to be given an upper and lower bound.
The algorithm incorporates the bound constraints by determining
the descent direction as in an unconstrained truncated Newton,
but never taking a step-size large enough to leave the space
of feasible x's. The algorithm keeps track of a set of
currently active constraints, and ignores them when computing
the minimum allowable step size. (The x's associated with the
active constraint are kept fixed.) If the maximum allowable
step size is zero then a new constraint is added. At the end
of each iteration one of the constraints may be deemed no
longer active and removed. A constraint is considered
no longer active is if it is currently active
but the gradient for that variable points inward from the
constraint. The specific constraint removed is the one
associated with the variable of largest index whose
constraint is no longer active.
Return codes are defined as follows::
-1 : Infeasible (lower bound > upper bound)
0 : Local minimum reached (|pg| ~= 0)
1 : Converged (|f_n-f_(n-1)| ~= 0)
2 : Converged (|x_n-x_(n-1)| ~= 0)
3 : Max. number of function evaluations reached
4 : Linear search failed
5 : All lower bounds are equal to the upper bounds
6 : Unable to progress
7 : User requested end of minimization
References
----------
Wright S., Nocedal J. (2006), 'Numerical Optimization'
Nash S.G. (1984), "Newton-Type Minimization Via the Lanczos Method",
SIAM Journal of Numerical Analysis 21, pp. 770-778
"""
# handle fprime/approx_grad
if approx_grad:
fun = func
jac = None
elif fprime is None:
fun = MemoizeJac(func)
jac = fun.derivative
else:
fun = func
jac = fprime
if disp is not None: # disp takes precedence over messages
mesg_num = disp
else:
mesg_num = {0:MSG_NONE, 1:MSG_ITER, 2:MSG_INFO, 3:MSG_VERS,
4:MSG_EXIT, 5:MSG_ALL}.get(messages, MSG_ALL)
# build options
opts = {'eps': epsilon,
'scale': scale,
'offset': offset,
'mesg_num': mesg_num,
'maxCGit': maxCGit,
'maxfun': maxfun,
'eta': eta,
'stepmx': stepmx,
'accuracy': accuracy,
'minfev': fmin,
'ftol': ftol,
'xtol': xtol,
'gtol': pgtol,
'rescale': rescale,
'disp': False}
res = _minimize_tnc(fun, x0, args, jac, bounds, callback=callback, **opts)
return res['x'], res['nfev'], res['status']
def _minimize_tnc(fun, x0, args=(), jac=None, bounds=None,
eps=1e-8, scale=None, offset=None, mesg_num=None,
maxCGit=-1, maxiter=None, eta=-1, stepmx=0, accuracy=0,
minfev=0, ftol=-1, xtol=-1, gtol=-1, rescale=-1, disp=False,
callback=None, finite_diff_rel_step=None, maxfun=None,
**unknown_options):
"""
Minimize a scalar function of one or more variables using a truncated
Newton (TNC) algorithm.
Options
-------
eps : float or ndarray
If `jac is None` the absolute step size used for numerical
approximation of the jacobian via forward differences.
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 subtract from each variable. If None, the
offsets are (up+low)/2 for interval bounded variables
and x for the others.
disp : bool
Set to True to print convergence 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.
maxiter : int, optional
Maximum number of function evaluations. This keyword is deprecated
in favor of `maxfun`. Only if `maxfun` is None is this keyword used.
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.
minfev : float
Minimum function value estimate. Defaults to 0.
ftol : float
Precision goal for the value of f in the stopping 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.
gtol : float
Precision goal for the value of the projected gradient in
the stopping criterion (after applying x scaling factors).
If gtol < 0.0, gtol 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.
finite_diff_rel_step : None or array_like, optional
If `jac in ['2-point', '3-point', 'cs']` the relative step size to
use for numerical approximation of the jacobian. The absolute step
size is computed as ``h = rel_step * sign(x0) * max(1, abs(x0))``,
possibly adjusted to fit into the bounds. For ``method='3-point'``
the sign of `h` is ignored. If None (default) then step is selected
automatically.
maxfun : int
Maximum number of function evaluations. If None, `maxfun` is
set to max(100, 10*len(x0)). Defaults to None.
"""
_check_unknown_options(unknown_options)
fmin = minfev
pgtol = gtol
x0 = asfarray(x0).flatten()
n = len(x0)
if bounds is None:
bounds = [(None,None)] * n
if len(bounds) != n:
raise ValueError('length of x0 != length of bounds')
new_bounds = old_bound_to_new(bounds)
if mesg_num is not None:
messages = {0:MSG_NONE, 1:MSG_ITER, 2:MSG_INFO, 3:MSG_VERS,
4:MSG_EXIT, 5:MSG_ALL}.get(mesg_num, MSG_ALL)
elif disp:
messages = MSG_ALL
else:
messages = MSG_NONE
sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
finite_diff_rel_step=finite_diff_rel_step,
bounds=new_bounds)
func_and_grad = sf.fun_and_grad
"""
low, up : the bounds (lists of floats)
if low is None, the lower bounds are removed.
if up is None, the upper bounds are removed.
low and up defaults to None
"""
low = zeros(n)
up = zeros(n)
for i in range(n):
if bounds[i] is None:
l, u = -inf, inf
else:
l,u = bounds[i]
if l is None:
low[i] = -inf
else:
low[i] = l
if u is None:
up[i] = inf
else:
up[i] = u
if scale is None:
scale = array([])
if offset is None:
offset = array([])
if maxfun is None:
if maxiter is not None:
maxfun = maxiter
else:
maxfun = max(100, 10*len(x0))
rc, nf, nit, x = moduleTNC.minimize(func_and_grad, x0, low, up, scale,
offset, messages, maxCGit, maxfun,
eta, stepmx, accuracy, fmin, ftol,
xtol, pgtol, rescale, callback)
funv, jacv = func_and_grad(x)
return OptimizeResult(x=x, fun=funv, jac=jacv, nfev=sf.nfev,
nit=nit, status=rc, message=RCSTRINGS[rc],
success=(-1 < rc < 3))
if __name__ == '__main__':
# Examples for TNC
def example():
print("Example")
# A function to minimize
def function(x):
f = pow(x[0],2.0)+pow(abs(x[1]),3.0)
g = [0,0]
g[0] = 2.0*x[0]
g[1] = 3.0*pow(abs(x[1]),2.0)
if x[1] < 0:
g[1] = -g[1]
return f, g
# Optimizer call
x, nf, rc = fmin_tnc(function, [-7, 3], bounds=([-10, 1], [10, 10]))
print("After", nf, "function evaluations, TNC returned:", RCSTRINGS[rc])
print("x =", x)
print("exact value = [0, 1]")
print()
example()