431 lines
16 KiB
Python
431 lines
16 KiB
Python
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# TNC Python interface
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# @(#) $Jeannot: tnc.py,v 1.11 2005/01/28 18:27:31 js Exp $
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# Copyright (c) 2004-2005, Jean-Sebastien Roy (js@jeannot.org)
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the
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# "Software"), to deal in the Software without restriction, including
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# without limitation the rights to use, copy, modify, merge, publish,
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# distribute, sublicense, and/or sell copies of the Software, and to
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# permit persons to whom the Software is furnished to do so, subject to
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# the following conditions:
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# The above copyright notice and this permission notice shall be included
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# in all copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
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# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
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# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
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# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
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# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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"""
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TNC: A Python interface to the TNC non-linear optimizer
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TNC is a non-linear optimizer. To use it, you must provide a function to
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minimize. The function must take one argument: the list of coordinates where to
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evaluate the function; and it must return either a tuple, whose first element is the
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value of the function, and whose second argument is the gradient of the function
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(as a list of values); or None, to abort the minimization.
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"""
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from scipy.optimize import _moduleTNC as moduleTNC
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from ._optimize import (MemoizeJac, OptimizeResult, _check_unknown_options,
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_prepare_scalar_function)
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from ._constraints import old_bound_to_new
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from scipy._lib._array_api import atleast_nd, array_namespace
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from numpy import inf, array, zeros
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__all__ = ['fmin_tnc']
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MSG_NONE = 0 # No messages
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MSG_ITER = 1 # One line per iteration
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MSG_INFO = 2 # Informational messages
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MSG_VERS = 4 # Version info
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MSG_EXIT = 8 # Exit reasons
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MSG_ALL = MSG_ITER + MSG_INFO + MSG_VERS + MSG_EXIT
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MSGS = {
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MSG_NONE: "No messages",
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MSG_ITER: "One line per iteration",
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MSG_INFO: "Informational messages",
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MSG_VERS: "Version info",
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MSG_EXIT: "Exit reasons",
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MSG_ALL: "All messages"
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}
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INFEASIBLE = -1 # Infeasible (lower bound > upper bound)
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LOCALMINIMUM = 0 # Local minimum reached (|pg| ~= 0)
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FCONVERGED = 1 # Converged (|f_n-f_(n-1)| ~= 0)
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XCONVERGED = 2 # Converged (|x_n-x_(n-1)| ~= 0)
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MAXFUN = 3 # Max. number of function evaluations reached
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LSFAIL = 4 # Linear search failed
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CONSTANT = 5 # All lower bounds are equal to the upper bounds
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NOPROGRESS = 6 # Unable to progress
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USERABORT = 7 # User requested end of minimization
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RCSTRINGS = {
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INFEASIBLE: "Infeasible (lower bound > upper bound)",
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LOCALMINIMUM: "Local minimum reached (|pg| ~= 0)",
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FCONVERGED: "Converged (|f_n-f_(n-1)| ~= 0)",
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XCONVERGED: "Converged (|x_n-x_(n-1)| ~= 0)",
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MAXFUN: "Max. number of function evaluations reached",
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LSFAIL: "Linear search failed",
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CONSTANT: "All lower bounds are equal to the upper bounds",
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NOPROGRESS: "Unable to progress",
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USERABORT: "User requested end of minimization"
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}
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# Changes to interface made by Travis Oliphant, Apr. 2004 for inclusion in
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# SciPy
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def fmin_tnc(func, x0, fprime=None, args=(), approx_grad=0,
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bounds=None, epsilon=1e-8, scale=None, offset=None,
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messages=MSG_ALL, maxCGit=-1, maxfun=None, eta=-1,
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stepmx=0, accuracy=0, fmin=0, ftol=-1, xtol=-1, pgtol=-1,
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rescale=-1, disp=None, callback=None):
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"""
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Minimize a function with variables subject to bounds, using
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gradient information in a truncated Newton algorithm. This
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method wraps a C implementation of the algorithm.
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Parameters
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----------
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func : callable ``func(x, *args)``
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Function to minimize. Must do one of:
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1. Return f and g, where f is the value of the function and g its
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gradient (a list of floats).
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2. Return the function value but supply gradient function
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separately as `fprime`.
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3. Return the function value and set ``approx_grad=True``.
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If the function returns None, the minimization
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is aborted.
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x0 : array_like
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Initial estimate of minimum.
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fprime : callable ``fprime(x, *args)``, optional
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Gradient of `func`. If None, then either `func` must return the
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function value and the gradient (``f,g = func(x, *args)``)
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or `approx_grad` must be True.
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args : tuple, optional
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Arguments to pass to function.
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approx_grad : bool, optional
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If true, approximate the gradient numerically.
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bounds : list, optional
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(min, max) pairs for each element in x0, defining the
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bounds on that parameter. Use None or +/-inf for one of
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min or max when there is no bound in that direction.
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epsilon : float, optional
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Used if approx_grad is True. The stepsize in a finite
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difference approximation for fprime.
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scale : array_like, optional
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Scaling factors to apply to each variable. If None, the
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factors are up-low for interval bounded variables and
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1+|x| for the others. Defaults to None.
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offset : array_like, optional
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Value to subtract from each variable. If None, the
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offsets are (up+low)/2 for interval bounded variables
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and x for the others.
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messages : int, optional
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Bit mask used to select messages display during
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minimization values defined in the MSGS dict. Defaults to
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MGS_ALL.
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disp : int, optional
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Integer interface to messages. 0 = no message, 5 = all messages
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maxCGit : int, optional
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Maximum number of hessian*vector evaluations per main
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iteration. If maxCGit == 0, the direction chosen is
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-gradient if maxCGit < 0, maxCGit is set to
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max(1,min(50,n/2)). Defaults to -1.
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maxfun : int, optional
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Maximum number of function evaluation. If None, maxfun is
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set to max(100, 10*len(x0)). Defaults to None. Note that this function
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may violate the limit because of evaluating gradients by numerical
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differentiation.
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eta : float, optional
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Severity of the line search. If < 0 or > 1, set to 0.25.
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Defaults to -1.
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stepmx : float, optional
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Maximum step for the line search. May be increased during
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call. If too small, it will be set to 10.0. Defaults to 0.
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accuracy : float, optional
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Relative precision for finite difference calculations. If
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<= machine_precision, set to sqrt(machine_precision).
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Defaults to 0.
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fmin : float, optional
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Minimum function value estimate. Defaults to 0.
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ftol : float, optional
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Precision goal for the value of f in the stopping criterion.
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If ftol < 0.0, ftol is set to 0.0 defaults to -1.
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xtol : float, optional
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Precision goal for the value of x in the stopping
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criterion (after applying x scaling factors). If xtol <
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0.0, xtol is set to sqrt(machine_precision). Defaults to
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-1.
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pgtol : float, optional
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Precision goal for the value of the projected gradient in
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the stopping criterion (after applying x scaling factors).
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If pgtol < 0.0, pgtol is set to 1e-2 * sqrt(accuracy).
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Setting it to 0.0 is not recommended. Defaults to -1.
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rescale : float, optional
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Scaling factor (in log10) used to trigger f value
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rescaling. If 0, rescale at each iteration. If a large
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value, never rescale. If < 0, rescale is set to 1.3.
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callback : callable, optional
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Called after each iteration, as callback(xk), where xk is the
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current parameter vector.
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Returns
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-------
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x : ndarray
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The solution.
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nfeval : int
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The number of function evaluations.
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rc : int
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Return code, see below
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See also
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--------
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minimize: Interface to minimization algorithms for multivariate
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functions. See the 'TNC' `method` in particular.
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Notes
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-----
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The underlying algorithm is truncated Newton, also called
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Newton Conjugate-Gradient. This method differs from
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scipy.optimize.fmin_ncg in that
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1. it wraps a C implementation of the algorithm
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2. it allows each variable to be given an upper and lower bound.
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The algorithm incorporates the bound constraints by determining
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the descent direction as in an unconstrained truncated Newton,
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but never taking a step-size large enough to leave the space
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of feasible x's. The algorithm keeps track of a set of
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currently active constraints, and ignores them when computing
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the minimum allowable step size. (The x's associated with the
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active constraint are kept fixed.) If the maximum allowable
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step size is zero then a new constraint is added. At the end
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of each iteration one of the constraints may be deemed no
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longer active and removed. A constraint is considered
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no longer active is if it is currently active
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but the gradient for that variable points inward from the
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constraint. The specific constraint removed is the one
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associated with the variable of largest index whose
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constraint is no longer active.
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Return codes are defined as follows::
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-1 : Infeasible (lower bound > upper bound)
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0 : Local minimum reached (|pg| ~= 0)
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1 : Converged (|f_n-f_(n-1)| ~= 0)
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2 : Converged (|x_n-x_(n-1)| ~= 0)
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3 : Max. number of function evaluations reached
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4 : Linear search failed
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5 : All lower bounds are equal to the upper bounds
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6 : Unable to progress
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7 : User requested end of minimization
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References
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----------
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Wright S., Nocedal J. (2006), 'Numerical Optimization'
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Nash S.G. (1984), "Newton-Type Minimization Via the Lanczos Method",
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SIAM Journal of Numerical Analysis 21, pp. 770-778
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"""
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# handle fprime/approx_grad
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if approx_grad:
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fun = func
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jac = None
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elif fprime is None:
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fun = MemoizeJac(func)
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jac = fun.derivative
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else:
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fun = func
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jac = fprime
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if disp is not None: # disp takes precedence over messages
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mesg_num = disp
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else:
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mesg_num = {0:MSG_NONE, 1:MSG_ITER, 2:MSG_INFO, 3:MSG_VERS,
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4:MSG_EXIT, 5:MSG_ALL}.get(messages, MSG_ALL)
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# build options
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opts = {'eps': epsilon,
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'scale': scale,
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'offset': offset,
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'mesg_num': mesg_num,
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'maxCGit': maxCGit,
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'maxfun': maxfun,
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'eta': eta,
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'stepmx': stepmx,
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'accuracy': accuracy,
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'minfev': fmin,
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'ftol': ftol,
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'xtol': xtol,
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'gtol': pgtol,
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'rescale': rescale,
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'disp': False}
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res = _minimize_tnc(fun, x0, args, jac, bounds, callback=callback, **opts)
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return res['x'], res['nfev'], res['status']
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def _minimize_tnc(fun, x0, args=(), jac=None, bounds=None,
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eps=1e-8, scale=None, offset=None, mesg_num=None,
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maxCGit=-1, eta=-1, stepmx=0, accuracy=0,
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minfev=0, ftol=-1, xtol=-1, gtol=-1, rescale=-1, disp=False,
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callback=None, finite_diff_rel_step=None, maxfun=None,
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**unknown_options):
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"""
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Minimize a scalar function of one or more variables using a truncated
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Newton (TNC) algorithm.
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Options
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-------
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eps : float or ndarray
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If `jac is None` the absolute step size used for numerical
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approximation of the jacobian via forward differences.
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scale : list of floats
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Scaling factors to apply to each variable. If None, the
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factors are up-low for interval bounded variables and
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1+|x] for the others. Defaults to None.
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offset : float
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Value to subtract from each variable. If None, the
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offsets are (up+low)/2 for interval bounded variables
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and x for the others.
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disp : bool
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Set to True to print convergence messages.
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maxCGit : int
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Maximum number of hessian*vector evaluations per main
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iteration. If maxCGit == 0, the direction chosen is
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-gradient if maxCGit < 0, maxCGit is set to
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max(1,min(50,n/2)). Defaults to -1.
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eta : float
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Severity of the line search. If < 0 or > 1, set to 0.25.
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Defaults to -1.
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stepmx : float
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Maximum step for the line search. May be increased during
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call. If too small, it will be set to 10.0. Defaults to 0.
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accuracy : float
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Relative precision for finite difference calculations. If
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<= machine_precision, set to sqrt(machine_precision).
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Defaults to 0.
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minfev : float
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Minimum function value estimate. Defaults to 0.
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ftol : float
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Precision goal for the value of f in the stopping criterion.
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If ftol < 0.0, ftol is set to 0.0 defaults to -1.
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xtol : float
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Precision goal for the value of x in the stopping
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criterion (after applying x scaling factors). If xtol <
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0.0, xtol is set to sqrt(machine_precision). Defaults to
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-1.
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gtol : float
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Precision goal for the value of the projected gradient in
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the stopping criterion (after applying x scaling factors).
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If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy).
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Setting it to 0.0 is not recommended. Defaults to -1.
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rescale : float
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Scaling factor (in log10) used to trigger f value
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rescaling. If 0, rescale at each iteration. If a large
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value, never rescale. If < 0, rescale is set to 1.3.
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finite_diff_rel_step : None or array_like, optional
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If `jac in ['2-point', '3-point', 'cs']` the relative step size to
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use for numerical approximation of the jacobian. The absolute step
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size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``,
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possibly adjusted to fit into the bounds. For ``method='3-point'``
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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
|
||
|
|
|
||
|
|
xp = array_namespace(x0)
|
||
|
|
x0 = atleast_nd(x0, ndim=1, xp=xp)
|
||
|
|
dtype = xp.float64
|
||
|
|
if xp.isdtype(x0.dtype, "real floating"):
|
||
|
|
dtype = x0.dtype
|
||
|
|
x0 = xp.reshape(xp.astype(x0, dtype), -1)
|
||
|
|
|
||
|
|
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:
|
||
|
|
maxfun = max(100, 10*len(x0))
|
||
|
|
|
||
|
|
rc, nf, nit, x, funv, jacv = moduleTNC.tnc_minimize(
|
||
|
|
func_and_grad, x0, low, up, scale,
|
||
|
|
offset, messages, maxCGit, maxfun,
|
||
|
|
eta, stepmx, accuracy, fmin, ftol,
|
||
|
|
xtol, pgtol, rescale, callback
|
||
|
|
)
|
||
|
|
# the TNC documentation states: "On output, x, f and g may be very
|
||
|
|
# slightly out of sync because of scaling". Therefore re-evaluate
|
||
|
|
# func_and_grad so they are synced.
|
||
|
|
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))
|