/
_minimize.py
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/
_minimize.py
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"""
Unified interfaces to minimization algorithms.
Functions
---------
- minimize : minimization of a function of several variables.
- minimize_scalar : minimization of a function of one variable.
"""
from __future__ import division, print_function, absolute_import
__all__ = ['minimize', 'minimize_scalar']
from warnings import warn
import numpy as np
from scipy._lib.six import callable
# unconstrained minimization
from .optimize import (_minimize_neldermead, _minimize_powell, _minimize_cg,
_minimize_bfgs, _minimize_newtoncg,
_minimize_scalar_brent, _minimize_scalar_bounded,
_minimize_scalar_golden, MemoizeJac)
from ._trustregion_dogleg import _minimize_dogleg
from ._trustregion_ncg import _minimize_trust_ncg
# constrained minimization
from .lbfgsb import _minimize_lbfgsb
from .tnc import _minimize_tnc
from .cobyla import _minimize_cobyla
from .slsqp import _minimize_slsqp
def minimize(fun, x0, args=(), method=None, jac=None, hess=None,
hessp=None, bounds=None, constraints=(), tol=None,
callback=None, options=None):
"""Minimization of scalar function of one or more variables.
In general, the optimization problems are of the form::
minimize f(x) subject to
g_i(x) >= 0, i = 1,...,m
h_j(x) = 0, j = 1,...,p
where x is a vector of one or more variables.
``g_i(x)`` are the inequality constraints.
``h_j(x)`` are the equality constrains.
Optionally, the lower and upper bounds for each element in x can also be
specified using the `bounds` argument.
Parameters
----------
fun : callable
Objective function.
x0 : ndarray
Initial guess.
args : tuple, optional
Extra arguments passed to the objective function and its
derivatives (Jacobian, Hessian).
method : str or callable, optional
Type of solver. Should be one of
- 'Nelder-Mead' :ref:`(see here) <optimize.minimize-neldermead>`
- 'Powell' :ref:`(see here) <optimize.minimize-powell>`
- 'CG' :ref:`(see here) <optimize.minimize-cg>`
- 'BFGS' :ref:`(see here) <optimize.minimize-bfgs>`
- 'Newton-CG' :ref:`(see here) <optimize.minimize-newtoncg>`
- 'L-BFGS-B' :ref:`(see here) <optimize.minimize-lbfgsb>`
- 'TNC' :ref:`(see here) <optimize.minimize-tnc>`
- 'COBYLA' :ref:`(see here) <optimize.minimize-cobyla>`
- 'SLSQP' :ref:`(see here) <optimize.minimize-slsqp>`
- 'dogleg' :ref:`(see here) <optimize.minimize-dogleg>`
- 'trust-ncg' :ref:`(see here) <optimize.minimize-trustncg>`
- custom - a callable object (added in version 0.14.0),
see below for description.
If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,
depending if the problem has constraints or bounds.
jac : bool or callable, optional
Jacobian (gradient) of objective function. Only for CG, BFGS,
Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.
If `jac` is a Boolean and is True, `fun` is assumed to return the
gradient along with the objective function. If False, the
gradient will be estimated numerically.
`jac` can also be a callable returning the gradient of the
objective. In this case, it must accept the same arguments as `fun`.
hess, hessp : callable, optional
Hessian (matrix of second-order derivatives) of objective function or
Hessian of objective function times an arbitrary vector p. Only for
Newton-CG, dogleg, trust-ncg.
Only one of `hessp` or `hess` needs to be given. If `hess` is
provided, then `hessp` will be ignored. If neither `hess` nor
`hessp` is provided, then the Hessian product will be approximated
using finite differences on `jac`. `hessp` must compute the Hessian
times an arbitrary vector.
bounds : sequence, optional
Bounds for variables (only for L-BFGS-B, TNC and SLSQP).
``(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.
constraints : dict or sequence of dict, optional
Constraints definition (only for COBYLA and SLSQP).
Each constraint is defined in a dictionary with fields:
type : str
Constraint type: 'eq' for equality, 'ineq' for inequality.
fun : callable
The function defining the constraint.
jac : callable, optional
The Jacobian of `fun` (only for SLSQP).
args : sequence, optional
Extra arguments to be passed to the function and Jacobian.
Equality constraint means that the constraint function result is to
be zero whereas inequality means that it is to be non-negative.
Note that COBYLA only supports inequality constraints.
tol : float, optional
Tolerance for termination. For detailed control, use solver-specific
options.
options : dict, optional
A dictionary of solver options. All methods accept the following
generic options:
maxiter : int
Maximum number of iterations to perform.
disp : bool
Set to True to print convergence messages.
For method-specific options, see :func:`show_options()`.
callback : callable, optional
Called after each iteration, as ``callback(xk)``, where ``xk`` is the
current parameter vector.
Returns
-------
res : OptimizeResult
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array, ``success`` a
Boolean flag indicating if the optimizer exited successfully and
``message`` which describes the cause of the termination. See
`OptimizeResult` for a description of other attributes.
See also
--------
minimize_scalar : Interface to minimization algorithms for scalar
univariate functions
show_options : Additional options accepted by the solvers
Notes
-----
This section describes the available solvers that can be selected by the
'method' parameter. The default method is *BFGS*.
**Unconstrained minimization**
Method :ref:`Nelder-Mead <optimize.minimize-neldermead>` uses the
Simplex algorithm [1]_, [2]_. This algorithm is robust in many
applications. However, if numerical computation of derivative can be
trusted, other algorithms using the first and/or second derivatives
information might be preferred for their better performance in
general.
Method :ref:`Powell <optimize.minimize-powell>` is a modification
of Powell's method [3]_, [4]_ which is a conjugate direction
method. It performs sequential one-dimensional minimizations along
each vector of the directions set (`direc` field in `options` and
`info`), which is updated at each iteration of the main
minimization loop. The function need not be differentiable, and no
derivatives are taken.
Method :ref:`CG <optimize.minimize-cg>` uses a nonlinear conjugate
gradient algorithm by Polak and Ribiere, a variant of the
Fletcher-Reeves method described in [5]_ pp. 120-122. Only the
first derivatives are used.
Method :ref:`BFGS <optimize.minimize-bfgs>` uses the quasi-Newton
method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) [5]_
pp. 136. It uses the first derivatives only. BFGS has proven good
performance even for non-smooth optimizations. This method also
returns an approximation of the Hessian inverse, stored as
`hess_inv` in the OptimizeResult object.
Method :ref:`Newton-CG <optimize.minimize-newtoncg>` uses a
Newton-CG algorithm [5]_ pp. 168 (also known as the truncated
Newton method). It uses a CG method to the compute the search
direction. See also *TNC* method for a box-constrained
minimization with a similar algorithm.
Method :ref:`dogleg <optimize.minimize-dogleg>` uses the dog-leg
trust-region algorithm [5]_ for unconstrained minimization. This
algorithm requires the gradient and Hessian; furthermore the
Hessian is required to be positive definite.
Method :ref:`trust-ncg <optimize.minimize-trustncg>` uses the
Newton conjugate gradient trust-region algorithm [5]_ for
unconstrained minimization. This algorithm requires the gradient
and either the Hessian or a function that computes the product of
the Hessian with a given vector.
**Constrained minimization**
Method :ref:`L-BFGS-B <optimize.minimize-lbfgsb>` uses the L-BFGS-B
algorithm [6]_, [7]_ for bound constrained minimization.
Method :ref:`TNC <optimize.minimize-tnc>` uses a truncated Newton
algorithm [5]_, [8]_ to minimize a function with variables subject
to bounds. This algorithm uses gradient information; it is also
called Newton Conjugate-Gradient. It differs from the *Newton-CG*
method described above as it wraps a C implementation and allows
each variable to be given upper and lower bounds.
Method :ref:`COBYLA <optimize.minimize-cobyla>` uses the
Constrained Optimization BY Linear Approximation (COBYLA) method
[9]_, [10]_, [11]_. The algorithm is based on linear
approximations to the objective function and each constraint. The
method wraps a FORTRAN implementation of the algorithm. The
constraints functions 'fun' may return either a single number
or an array or list of numbers.
Method :ref:`SLSQP <optimize.minimize-slsqp>` uses Sequential
Least SQuares Programming to minimize a function of several
variables with any combination of bounds, equality and inequality
constraints. The method wraps the SLSQP Optimization subroutine
originally implemented by Dieter Kraft [12]_. Note that the
wrapper handles infinite values in bounds by converting them into
large floating values.
**Custom minimizers**
It may be useful to pass a custom minimization method, for example
when using a frontend to this method such as `scipy.optimize.basinhopping`
or a different library. You can simply pass a callable as the ``method``
parameter.
The callable is called as ``method(fun, x0, args, **kwargs, **options)``
where ``kwargs`` corresponds to any other parameters passed to `minimize`
(such as `callback`, `hess`, etc.), except the `options` dict, which has
its contents also passed as `method` parameters pair by pair. Also, if
`jac` has been passed as a bool type, `jac` and `fun` are mangled so that
`fun` returns just the function values and `jac` is converted to a function
returning the Jacobian. The method shall return an ``OptimizeResult``
object.
The provided `method` callable must be able to accept (and possibly ignore)
arbitrary parameters; the set of parameters accepted by `minimize` may
expand in future versions and then these parameters will be passed to
the method. You can find an example in the scipy.optimize tutorial.
.. versionadded:: 0.11.0
References
----------
.. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function
Minimization. The Computer Journal 7: 308-13.
.. [2] Wright M H. 1996. Direct search methods: Once scorned, now
respectable, in Numerical Analysis 1995: Proceedings of the 1995
Dundee Biennial Conference in Numerical Analysis (Eds. D F
Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.
191-208.
.. [3] Powell, M J D. 1964. An efficient method for finding the minimum of
a function of several variables without calculating derivatives. The
Computer Journal 7: 155-162.
.. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.
Numerical Recipes (any edition), Cambridge University Press.
.. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.
Springer New York.
.. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory
Algorithm for Bound Constrained Optimization. SIAM Journal on
Scientific and Statistical Computing 16 (5): 1190-1208.
.. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm
778: L-BFGS-B, FORTRAN routines for large scale bound constrained
optimization. ACM Transactions on Mathematical Software 23 (4):
550-560.
.. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.
1984. SIAM Journal of Numerical Analysis 21: 770-778.
.. [9] Powell, M J D. A direct search optimization method that models
the objective and constraint functions by linear interpolation.
1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez
and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.
.. [10] Powell M J D. Direct search algorithms for optimization
calculations. 1998. Acta Numerica 7: 287-336.
.. [11] Powell M J D. A view of algorithms for optimization without
derivatives. 2007.Cambridge University Technical Report DAMTP
2007/NA03
.. [12] Kraft, D. A software package for sequential quadratic
programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace
Center -- Institute for Flight Mechanics, Koln, Germany.
Examples
--------
Let us consider the problem of minimizing the Rosenbrock function. This
function (and its respective derivatives) is implemented in `rosen`
(resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.
>>> from scipy.optimize import minimize, rosen, rosen_der
A simple application of the *Nelder-Mead* method is:
>>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
>>> res = minimize(rosen, x0, method='Nelder-Mead', tol=1e-6)
>>> res.x
array([ 1., 1., 1., 1., 1.])
Now using the *BFGS* algorithm, using the first derivative and a few
options:
>>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
... options={'gtol': 1e-6, 'disp': True})
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 26
Function evaluations: 31
Gradient evaluations: 31
>>> res.x
array([ 1., 1., 1., 1., 1.])
>>> print(res.message)
Optimization terminated successfully.
>>> res.hess_inv
array([[ 0.00749589, 0.01255155, 0.02396251, 0.04750988, 0.09495377], # may vary
[ 0.01255155, 0.02510441, 0.04794055, 0.09502834, 0.18996269],
[ 0.02396251, 0.04794055, 0.09631614, 0.19092151, 0.38165151],
[ 0.04750988, 0.09502834, 0.19092151, 0.38341252, 0.7664427 ],
[ 0.09495377, 0.18996269, 0.38165151, 0.7664427, 1.53713523]])
Next, consider a minimization problem with several constraints (namely
Example 16.4 from [5]_). The objective function is:
>>> fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
There are three constraints defined as:
>>> cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
... {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
... {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
And variables must be positive, hence the following bounds:
>>> bnds = ((0, None), (0, None))
The optimization problem is solved using the SLSQP method as:
>>> res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,
... constraints=cons)
It should converge to the theoretical solution (1.4 ,1.7).
"""
x0 = np.asarray(x0)
if x0.dtype.kind in np.typecodes["AllInteger"]:
x0 = np.asarray(x0, dtype=float)
if not isinstance(args, tuple):
args = (args,)
if method is None:
# Select automatically
if constraints:
method = 'SLSQP'
elif bounds is not None:
method = 'L-BFGS-B'
else:
method = 'BFGS'
if callable(method):
meth = "_custom"
else:
meth = method.lower()
if options is None:
options = {}
# check if optional parameters are supported by the selected method
# - jac
if meth in ['nelder-mead', 'powell', 'cobyla'] and bool(jac):
warn('Method %s does not use gradient information (jac).' % method,
RuntimeWarning)
# - hess
if meth not in ('newton-cg', 'dogleg', 'trust-ncg', '_custom') and hess is not None:
warn('Method %s does not use Hessian information (hess).' % method,
RuntimeWarning)
# - hessp
if meth not in ('newton-cg', 'dogleg', 'trust-ncg', '_custom') and hessp is not None:
warn('Method %s does not use Hessian-vector product '
'information (hessp).' % method, RuntimeWarning)
# - constraints or bounds
if (meth in ['nelder-mead', 'powell', 'cg', 'bfgs', 'newton-cg', 'dogleg',
'trust-ncg'] and (bounds is not None or np.any(constraints))):
warn('Method %s cannot handle constraints nor bounds.' % method,
RuntimeWarning)
if meth in ['l-bfgs-b', 'tnc'] and np.any(constraints):
warn('Method %s cannot handle constraints.' % method,
RuntimeWarning)
if meth == 'cobyla' and bounds is not None:
warn('Method %s cannot handle bounds.' % method,
RuntimeWarning)
# - callback
if (meth in ['cobyla'] and callback is not None):
warn('Method %s does not support callback.' % method, RuntimeWarning)
# - return_all
if (meth in ['l-bfgs-b', 'tnc', 'cobyla', 'slsqp'] and
options.get('return_all', False)):
warn('Method %s does not support the return_all option.' % method,
RuntimeWarning)
# fun also returns the jacobian
if not callable(jac):
if bool(jac):
fun = MemoizeJac(fun)
jac = fun.derivative
else:
jac = None
# set default tolerances
if tol is not None:
options = dict(options)
if meth == 'nelder-mead':
options.setdefault('xatol', tol)
options.setdefault('fatol', tol)
if meth in ['newton-cg', 'powell', 'tnc']:
options.setdefault('xtol', tol)
if meth in ['powell', 'l-bfgs-b', 'tnc', 'slsqp']:
options.setdefault('ftol', tol)
if meth in ['bfgs', 'cg', 'l-bfgs-b', 'tnc', 'dogleg', 'trust-ncg']:
options.setdefault('gtol', tol)
if meth in ['cobyla', '_custom']:
options.setdefault('tol', tol)
if meth == '_custom':
return method(fun, x0, args=args, jac=jac, hess=hess, hessp=hessp,
bounds=bounds, constraints=constraints,
callback=callback, **options)
elif meth == 'nelder-mead':
return _minimize_neldermead(fun, x0, args, callback, **options)
elif meth == 'powell':
return _minimize_powell(fun, x0, args, callback, **options)
elif meth == 'cg':
return _minimize_cg(fun, x0, args, jac, callback, **options)
elif meth == 'bfgs':
return _minimize_bfgs(fun, x0, args, jac, callback, **options)
elif meth == 'newton-cg':
return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,
**options)
elif meth == 'l-bfgs-b':
return _minimize_lbfgsb(fun, x0, args, jac, bounds,
callback=callback, **options)
elif meth == 'tnc':
return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,
**options)
elif meth == 'cobyla':
return _minimize_cobyla(fun, x0, args, constraints, **options)
elif meth == 'slsqp':
return _minimize_slsqp(fun, x0, args, jac, bounds,
constraints, callback=callback, **options)
elif meth == 'dogleg':
return _minimize_dogleg(fun, x0, args, jac, hess,
callback=callback, **options)
elif meth == 'trust-ncg':
return _minimize_trust_ncg(fun, x0, args, jac, hess, hessp,
callback=callback, **options)
else:
raise ValueError('Unknown solver %s' % method)
def minimize_scalar(fun, bracket=None, bounds=None, args=(),
method='brent', tol=None, options=None):
"""Minimization of scalar function of one variable.
Parameters
----------
fun : callable
Objective function.
Scalar function, must return a scalar.
bracket : sequence, optional
For methods 'brent' and 'golden', `bracket` defines the bracketing
interval and can either have three items `(a, b, c)` so that `a < b
< c` and `fun(b) < fun(a), fun(c)` or two items `a` and `c` which
are assumed to be a starting interval for a downhill bracket search
(see `bracket`); it doesn't always mean that the obtained solution
will satisfy `a <= x <= c`.
bounds : sequence, optional
For method 'bounded', `bounds` is mandatory and must have two items
corresponding to the optimization bounds.
args : tuple, optional
Extra arguments passed to the objective function.
method : str or callable, optional
Type of solver. Should be one of
- 'Brent' :ref:`(see here) <optimize.minimize_scalar-brent>`
- 'Bounded' :ref:`(see here) <optimize.minimize_scalar-bounded>`
- 'Golden' :ref:`(see here) <optimize.minimize_scalar-golden>`
- custom - a callable object (added in version 0.14.0),
see below
tol : float, optional
Tolerance for termination. For detailed control, use solver-specific
options.
options : dict, optional
A dictionary of solver options.
maxiter : int
Maximum number of iterations to perform.
disp : bool
Set to True to print convergence messages.
See :func:`show_options()` for solver-specific options.
Returns
-------
res : OptimizeResult
The optimization result represented as a ``OptimizeResult`` object.
Important attributes are: ``x`` the solution array, ``success`` a
Boolean flag indicating if the optimizer exited successfully and
``message`` which describes the cause of the termination. See
`OptimizeResult` for a description of other attributes.
See also
--------
minimize : Interface to minimization algorithms for scalar multivariate
functions
show_options : Additional options accepted by the solvers
Notes
-----
This section describes the available solvers that can be selected by the
'method' parameter. The default method is *Brent*.
Method :ref:`Brent <optimize.minimize_scalar-brent>` uses Brent's
algorithm to find a local minimum. The algorithm uses inverse
parabolic interpolation when possible to speed up convergence of
the golden section method.
Method :ref:`Golden <optimize.minimize_scalar-golden>` uses the
golden section search technique. It uses analog of the bisection
method to decrease the bracketed interval. It is usually
preferable to use the *Brent* method.
Method :ref:`Bounded <optimize.minimize_scalar-bounded>` can
perform bounded minimization. It uses the Brent method to find a
local minimum in the interval x1 < xopt < x2.
**Custom minimizers**
It may be useful to pass a custom minimization method, for example
when using some library frontend to minimize_scalar. You can simply
pass a callable as the ``method`` parameter.
The callable is called as ``method(fun, args, **kwargs, **options)``
where ``kwargs`` corresponds to any other parameters passed to `minimize`
(such as `bracket`, `tol`, etc.), except the `options` dict, which has
its contents also passed as `method` parameters pair by pair. The method
shall return an ``OptimizeResult`` object.
The provided `method` callable must be able to accept (and possibly ignore)
arbitrary parameters; the set of parameters accepted by `minimize` may
expand in future versions and then these parameters will be passed to
the method. You can find an example in the scipy.optimize tutorial.
.. versionadded:: 0.11.0
Examples
--------
Consider the problem of minimizing the following function.
>>> def f(x):
... return (x - 2) * x * (x + 2)**2
Using the *Brent* method, we find the local minimum as:
>>> from scipy.optimize import minimize_scalar
>>> res = minimize_scalar(f)
>>> res.x
1.28077640403
Using the *Bounded* method, we find a local minimum with specified
bounds as:
>>> res = minimize_scalar(f, bounds=(-3, -1), method='bounded')
>>> res.x
-2.0000002026
"""
if not isinstance(args, tuple):
args = (args,)
if callable(method):
meth = "_custom"
else:
meth = method.lower()
if options is None:
options = {}
if tol is not None:
options = dict(options)
if meth == 'bounded' and 'xatol' not in options:
warn("Method 'bounded' does not support relative tolerance in x; "
"defaulting to absolute tolerance.", RuntimeWarning)
options['xatol'] = tol
elif meth == '_custom':
options.setdefault('tol', tol)
else:
options.setdefault('xtol', tol)
if meth == '_custom':
return method(fun, args=args, bracket=bracket, bounds=bounds, **options)
elif meth == 'brent':
return _minimize_scalar_brent(fun, bracket, args, **options)
elif meth == 'bounded':
if bounds is None:
raise ValueError('The `bounds` parameter is mandatory for '
'method `bounded`.')
return _minimize_scalar_bounded(fun, bounds, args, **options)
elif meth == 'golden':
return _minimize_scalar_golden(fun, bracket, args, **options)
else:
raise ValueError('Unknown solver %s' % method)