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840 lines
37 KiB
Python
840 lines
37 KiB
Python
"""
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Unified interfaces to minimization algorithms.
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Functions
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---------
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- minimize : minimization of a function of several variables.
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- minimize_scalar : minimization of a function of one variable.
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"""
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__all__ = ['minimize', 'minimize_scalar']
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from warnings import warn
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import numpy as np
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# unconstrained minimization
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from .optimize import (_minimize_neldermead, _minimize_powell, _minimize_cg,
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_minimize_bfgs, _minimize_newtoncg,
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_minimize_scalar_brent, _minimize_scalar_bounded,
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_minimize_scalar_golden, MemoizeJac)
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from ._trustregion_dogleg import _minimize_dogleg
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from ._trustregion_ncg import _minimize_trust_ncg
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from ._trustregion_krylov import _minimize_trust_krylov
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from ._trustregion_exact import _minimize_trustregion_exact
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from ._trustregion_constr import _minimize_trustregion_constr
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# constrained minimization
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from .lbfgsb import _minimize_lbfgsb
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from .tnc import _minimize_tnc
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from .cobyla import _minimize_cobyla
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from .slsqp import _minimize_slsqp
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from ._constraints import (old_bound_to_new, new_bounds_to_old,
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old_constraint_to_new, new_constraint_to_old,
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NonlinearConstraint, LinearConstraint, Bounds)
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from ._differentiable_functions import FD_METHODS
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MINIMIZE_METHODS = ['nelder-mead', 'powell', 'cg', 'bfgs', 'newton-cg',
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'l-bfgs-b', 'tnc', 'cobyla', 'slsqp', 'trust-constr',
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'dogleg', 'trust-ncg', 'trust-exact', 'trust-krylov']
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MINIMIZE_SCALAR_METHODS = ['brent', 'bounded', 'golden']
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def minimize(fun, x0, args=(), method=None, jac=None, hess=None,
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hessp=None, bounds=None, constraints=(), tol=None,
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callback=None, options=None):
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"""Minimization of scalar function of one or more variables.
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Parameters
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----------
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fun : callable
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The objective function to be minimized.
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``fun(x, *args) -> float``
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where ``x`` is an 1-D array with shape (n,) and ``args``
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is a tuple of the fixed parameters needed to completely
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specify the function.
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x0 : ndarray, shape (n,)
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Initial guess. Array of real elements of size (n,),
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where 'n' is the number of independent variables.
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args : tuple, optional
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Extra arguments passed to the objective function and its
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derivatives (`fun`, `jac` and `hess` functions).
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method : str or callable, optional
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Type of solver. Should be one of
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- 'Nelder-Mead' :ref:`(see here) <optimize.minimize-neldermead>`
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- 'Powell' :ref:`(see here) <optimize.minimize-powell>`
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- 'CG' :ref:`(see here) <optimize.minimize-cg>`
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- 'BFGS' :ref:`(see here) <optimize.minimize-bfgs>`
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- 'Newton-CG' :ref:`(see here) <optimize.minimize-newtoncg>`
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- 'L-BFGS-B' :ref:`(see here) <optimize.minimize-lbfgsb>`
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- 'TNC' :ref:`(see here) <optimize.minimize-tnc>`
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- 'COBYLA' :ref:`(see here) <optimize.minimize-cobyla>`
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- 'SLSQP' :ref:`(see here) <optimize.minimize-slsqp>`
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- 'trust-constr':ref:`(see here) <optimize.minimize-trustconstr>`
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- 'dogleg' :ref:`(see here) <optimize.minimize-dogleg>`
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- 'trust-ncg' :ref:`(see here) <optimize.minimize-trustncg>`
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- 'trust-exact' :ref:`(see here) <optimize.minimize-trustexact>`
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- 'trust-krylov' :ref:`(see here) <optimize.minimize-trustkrylov>`
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- custom - a callable object (added in version 0.14.0),
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see below for description.
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If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,
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depending if the problem has constraints or bounds.
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jac : {callable, '2-point', '3-point', 'cs', bool}, optional
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Method for computing the gradient vector. Only for CG, BFGS,
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Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg, trust-krylov,
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trust-exact and trust-constr.
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If it is a callable, it should be a function that returns the gradient
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vector:
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``jac(x, *args) -> array_like, shape (n,)``
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where ``x`` is an array with shape (n,) and ``args`` is a tuple with
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the fixed parameters. If `jac` is a Boolean and is True, `fun` is
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assumed to return and objective and gradient as an ``(f, g)`` tuple.
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Methods 'Newton-CG', 'trust-ncg', 'dogleg', 'trust-exact', and
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'trust-krylov' require that either a callable be supplied, or that
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`fun` return the objective and gradient.
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If None or False, the gradient will be estimated using 2-point finite
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difference estimation with an absolute step size.
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Alternatively, the keywords {'2-point', '3-point', 'cs'} can be used
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to select a finite difference scheme for numerical estimation of the
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gradient with a relative step size. These finite difference schemes
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obey any specified `bounds`.
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hess : {callable, '2-point', '3-point', 'cs', HessianUpdateStrategy}, optional
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Method for computing the Hessian matrix. Only for Newton-CG, dogleg,
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trust-ncg, trust-krylov, trust-exact and trust-constr. If it is
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callable, it should return the Hessian matrix:
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``hess(x, *args) -> {LinearOperator, spmatrix, array}, (n, n)``
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where x is a (n,) ndarray and `args` is a tuple with the fixed
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parameters. LinearOperator and sparse matrix returns are
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allowed only for 'trust-constr' method. Alternatively, the keywords
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{'2-point', '3-point', 'cs'} select a finite difference scheme
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for numerical estimation. Or, objects implementing
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`HessianUpdateStrategy` interface can be used to approximate
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the Hessian. Available quasi-Newton methods implementing
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this interface are:
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- `BFGS`;
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- `SR1`.
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Whenever the gradient is estimated via finite-differences,
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the Hessian cannot be estimated with options
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{'2-point', '3-point', 'cs'} and needs to be
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estimated using one of the quasi-Newton strategies.
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Finite-difference options {'2-point', '3-point', 'cs'} and
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`HessianUpdateStrategy` are available only for 'trust-constr' method.
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hessp : callable, optional
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Hessian of objective function times an arbitrary vector p. Only for
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Newton-CG, trust-ncg, trust-krylov, trust-constr.
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Only one of `hessp` or `hess` needs to be given. If `hess` is
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provided, then `hessp` will be ignored. `hessp` must compute the
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Hessian times an arbitrary vector:
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``hessp(x, p, *args) -> ndarray shape (n,)``
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where x is a (n,) ndarray, p is an arbitrary vector with
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dimension (n,) and `args` is a tuple with the fixed
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parameters.
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bounds : sequence or `Bounds`, optional
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Bounds on variables for L-BFGS-B, TNC, SLSQP, Powell, and
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trust-constr methods. There are two ways to specify the bounds:
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1. Instance of `Bounds` class.
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2. Sequence of ``(min, max)`` pairs for each element in `x`. None
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is used to specify no bound.
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constraints : {Constraint, dict} or List of {Constraint, dict}, optional
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Constraints definition (only for COBYLA, SLSQP and trust-constr).
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Constraints for 'trust-constr' are defined as a single object or a
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list of objects specifying constraints to the optimization problem.
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Available constraints are:
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- `LinearConstraint`
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- `NonlinearConstraint`
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Constraints for COBYLA, SLSQP are defined as a list of dictionaries.
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Each dictionary with fields:
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type : str
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Constraint type: 'eq' for equality, 'ineq' for inequality.
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fun : callable
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The function defining the constraint.
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jac : callable, optional
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The Jacobian of `fun` (only for SLSQP).
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args : sequence, optional
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Extra arguments to be passed to the function and Jacobian.
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Equality constraint means that the constraint function result is to
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be zero whereas inequality means that it is to be non-negative.
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Note that COBYLA only supports inequality constraints.
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tol : float, optional
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Tolerance for termination. For detailed control, use solver-specific
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options.
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options : dict, optional
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A dictionary of solver options. All methods accept the following
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generic options:
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maxiter : int
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Maximum number of iterations to perform. Depending on the
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method each iteration may use several function evaluations.
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disp : bool
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Set to True to print convergence messages.
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For method-specific options, see :func:`show_options()`.
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callback : callable, optional
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Called after each iteration. For 'trust-constr' it is a callable with
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the signature:
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``callback(xk, OptimizeResult state) -> bool``
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where ``xk`` is the current parameter vector. and ``state``
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is an `OptimizeResult` object, with the same fields
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as the ones from the return. If callback returns True
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the algorithm execution is terminated.
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For all the other methods, the signature is:
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``callback(xk)``
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where ``xk`` is the current parameter vector.
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Returns
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-------
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res : OptimizeResult
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The optimization result represented as a ``OptimizeResult`` object.
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Important attributes are: ``x`` the solution array, ``success`` a
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Boolean flag indicating if the optimizer exited successfully and
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``message`` which describes the cause of the termination. See
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`OptimizeResult` for a description of other attributes.
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See also
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--------
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minimize_scalar : Interface to minimization algorithms for scalar
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univariate functions
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show_options : Additional options accepted by the solvers
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Notes
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-----
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This section describes the available solvers that can be selected by the
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'method' parameter. The default method is *BFGS*.
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**Unconstrained minimization**
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Method :ref:`Nelder-Mead <optimize.minimize-neldermead>` uses the
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Simplex algorithm [1]_, [2]_. This algorithm is robust in many
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applications. However, if numerical computation of derivative can be
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trusted, other algorithms using the first and/or second derivatives
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information might be preferred for their better performance in
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general.
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Method :ref:`CG <optimize.minimize-cg>` uses a nonlinear conjugate
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gradient algorithm by Polak and Ribiere, a variant of the
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Fletcher-Reeves method described in [5]_ pp.120-122. Only the
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first derivatives are used.
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Method :ref:`BFGS <optimize.minimize-bfgs>` uses the quasi-Newton
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method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) [5]_
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pp. 136. It uses the first derivatives only. BFGS has proven good
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performance even for non-smooth optimizations. This method also
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returns an approximation of the Hessian inverse, stored as
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`hess_inv` in the OptimizeResult object.
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Method :ref:`Newton-CG <optimize.minimize-newtoncg>` uses a
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Newton-CG algorithm [5]_ pp. 168 (also known as the truncated
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Newton method). It uses a CG method to the compute the search
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direction. See also *TNC* method for a box-constrained
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minimization with a similar algorithm. Suitable for large-scale
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problems.
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Method :ref:`dogleg <optimize.minimize-dogleg>` uses the dog-leg
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trust-region algorithm [5]_ for unconstrained minimization. This
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algorithm requires the gradient and Hessian; furthermore the
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Hessian is required to be positive definite.
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Method :ref:`trust-ncg <optimize.minimize-trustncg>` uses the
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Newton conjugate gradient trust-region algorithm [5]_ for
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unconstrained minimization. This algorithm requires the gradient
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and either the Hessian or a function that computes the product of
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the Hessian with a given vector. Suitable for large-scale problems.
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Method :ref:`trust-krylov <optimize.minimize-trustkrylov>` uses
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the Newton GLTR trust-region algorithm [14]_, [15]_ for unconstrained
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minimization. This algorithm requires the gradient
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and either the Hessian or a function that computes the product of
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the Hessian with a given vector. Suitable for large-scale problems.
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On indefinite problems it requires usually less iterations than the
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`trust-ncg` method and is recommended for medium and large-scale problems.
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Method :ref:`trust-exact <optimize.minimize-trustexact>`
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is a trust-region method for unconstrained minimization in which
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quadratic subproblems are solved almost exactly [13]_. This
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algorithm requires the gradient and the Hessian (which is
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*not* required to be positive definite). It is, in many
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situations, the Newton method to converge in fewer iteraction
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and the most recommended for small and medium-size problems.
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**Bound-Constrained minimization**
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Method :ref:`L-BFGS-B <optimize.minimize-lbfgsb>` uses the L-BFGS-B
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algorithm [6]_, [7]_ for bound constrained minimization.
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Method :ref:`Powell <optimize.minimize-powell>` is a modification
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of Powell's method [3]_, [4]_ which is a conjugate direction
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method. It performs sequential one-dimensional minimizations along
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each vector of the directions set (`direc` field in `options` and
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`info`), which is updated at each iteration of the main
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minimization loop. The function need not be differentiable, and no
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derivatives are taken. If bounds are not provided, then an
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unbounded line search will be used. If bounds are provided and
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the initial guess is within the bounds, then every function
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evaluation throughout the minimization procedure will be within
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the bounds. If bounds are provided, the initial guess is outside
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the bounds, and `direc` is full rank (default has full rank), then
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some function evaluations during the first iteration may be
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outside the bounds, but every function evaluation after the first
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iteration will be within the bounds. If `direc` is not full rank,
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then some parameters may not be optimized and the solution is not
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guaranteed to be within the bounds.
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Method :ref:`TNC <optimize.minimize-tnc>` uses a truncated Newton
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algorithm [5]_, [8]_ to minimize a function with variables subject
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to bounds. This algorithm uses gradient information; it is also
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called Newton Conjugate-Gradient. It differs from the *Newton-CG*
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method described above as it wraps a C implementation and allows
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each variable to be given upper and lower bounds.
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**Constrained Minimization**
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Method :ref:`COBYLA <optimize.minimize-cobyla>` uses the
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Constrained Optimization BY Linear Approximation (COBYLA) method
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[9]_, [10]_, [11]_. The algorithm is based on linear
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approximations to the objective function and each constraint. The
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method wraps a FORTRAN implementation of the algorithm. The
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constraints functions 'fun' may return either a single number
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or an array or list of numbers.
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Method :ref:`SLSQP <optimize.minimize-slsqp>` uses Sequential
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Least SQuares Programming to minimize a function of several
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variables with any combination of bounds, equality and inequality
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constraints. The method wraps the SLSQP Optimization subroutine
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originally implemented by Dieter Kraft [12]_. Note that the
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wrapper handles infinite values in bounds by converting them into
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large floating values.
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Method :ref:`trust-constr <optimize.minimize-trustconstr>` is a
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trust-region algorithm for constrained optimization. It swiches
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between two implementations depending on the problem definition.
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It is the most versatile constrained minimization algorithm
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implemented in SciPy and the most appropriate for large-scale problems.
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For equality constrained problems it is an implementation of Byrd-Omojokun
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Trust-Region SQP method described in [17]_ and in [5]_, p. 549. When
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inequality constraints are imposed as well, it swiches to the trust-region
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interior point method described in [16]_. This interior point algorithm,
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in turn, solves inequality constraints by introducing slack variables
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and solving a sequence of equality-constrained barrier problems
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for progressively smaller values of the barrier parameter.
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The previously described equality constrained SQP method is
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used to solve the subproblems with increasing levels of accuracy
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as the iterate gets closer to a solution.
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**Finite-Difference Options**
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For Method :ref:`trust-constr <optimize.minimize-trustconstr>`
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the gradient and the Hessian may be approximated using
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three finite-difference schemes: {'2-point', '3-point', 'cs'}.
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The scheme 'cs' is, potentially, the most accurate but it
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requires the function to correctly handles complex inputs and to
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be differentiable in the complex plane. The scheme '3-point' is more
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accurate than '2-point' but requires twice as many operations.
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**Custom minimizers**
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It may be useful to pass a custom minimization method, for example
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when using a frontend to this method such as `scipy.optimize.basinhopping`
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or a different library. You can simply pass a callable as the ``method``
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parameter.
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The callable is called as ``method(fun, x0, args, **kwargs, **options)``
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where ``kwargs`` corresponds to any other parameters passed to `minimize`
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(such as `callback`, `hess`, etc.), except the `options` dict, which has
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its contents also passed as `method` parameters pair by pair. Also, if
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`jac` has been passed as a bool type, `jac` and `fun` are mangled so that
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`fun` returns just the function values and `jac` is converted to a function
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returning the Jacobian. The method shall return an `OptimizeResult`
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object.
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The provided `method` callable must be able to accept (and possibly ignore)
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arbitrary parameters; the set of parameters accepted by `minimize` may
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expand in future versions and then these parameters will be passed to
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the method. You can find an example in the scipy.optimize tutorial.
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.. versionadded:: 0.11.0
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References
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----------
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.. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function
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Minimization. The Computer Journal 7: 308-13.
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.. [2] Wright M H. 1996. Direct search methods: Once scorned, now
|
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respectable, in Numerical Analysis 1995: Proceedings of the 1995
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Dundee Biennial Conference in Numerical Analysis (Eds. D F
|
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Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.
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191-208.
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.. [3] Powell, M J D. 1964. An efficient method for finding the minimum of
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a function of several variables without calculating derivatives. The
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Computer Journal 7: 155-162.
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.. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.
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Numerical Recipes (any edition), Cambridge University Press.
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.. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.
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Springer New York.
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.. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory
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Algorithm for Bound Constrained Optimization. SIAM Journal on
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Scientific and Statistical Computing 16 (5): 1190-1208.
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.. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm
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778: L-BFGS-B, FORTRAN routines for large scale bound constrained
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optimization. ACM Transactions on Mathematical Software 23 (4):
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550-560.
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.. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.
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1984. SIAM Journal of Numerical Analysis 21: 770-778.
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.. [9] Powell, M J D. A direct search optimization method that models
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the objective and constraint functions by linear interpolation.
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1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez
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and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.
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.. [10] Powell M J D. Direct search algorithms for optimization
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calculations. 1998. Acta Numerica 7: 287-336.
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.. [11] Powell M J D. A view of algorithms for optimization without
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derivatives. 2007.Cambridge University Technical Report DAMTP
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2007/NA03
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.. [12] Kraft, D. A software package for sequential quadratic
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programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace
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Center -- Institute for Flight Mechanics, Koln, Germany.
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.. [13] Conn, A. R., Gould, N. I., and Toint, P. L.
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Trust region methods. 2000. Siam. pp. 169-200.
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.. [14] F. Lenders, C. Kirches, A. Potschka: "trlib: A vector-free
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implementation of the GLTR method for iterative solution of
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the trust region problem", :arxiv:`1611.04718`
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.. [15] N. Gould, S. Lucidi, M. Roma, P. Toint: "Solving the
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Trust-Region Subproblem using the Lanczos Method",
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SIAM J. Optim., 9(2), 504--525, (1999).
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.. [16] Byrd, Richard H., Mary E. Hribar, and Jorge Nocedal. 1999.
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An interior point algorithm for large-scale nonlinear programming.
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SIAM Journal on Optimization 9.4: 877-900.
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.. [17] Lalee, Marucha, Jorge Nocedal, and Todd Plantega. 1998. On the
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implementation of an algorithm for large-scale equality constrained
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optimization. SIAM Journal on Optimization 8.3: 682-706.
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Examples
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--------
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Let us consider the problem of minimizing the Rosenbrock function. This
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function (and its respective derivatives) is implemented in `rosen`
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(resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.
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>>> from scipy.optimize import minimize, rosen, rosen_der
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A simple application of the *Nelder-Mead* method is:
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>>> x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
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>>> res = minimize(rosen, x0, method='Nelder-Mead', tol=1e-6)
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>>> res.x
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array([ 1., 1., 1., 1., 1.])
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Now using the *BFGS* algorithm, using the first derivative and a few
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options:
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>>> res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
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... options={'gtol': 1e-6, 'disp': True})
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Optimization terminated successfully.
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Current function value: 0.000000
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Iterations: 26
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Function evaluations: 31
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Gradient evaluations: 31
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>>> res.x
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array([ 1., 1., 1., 1., 1.])
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>>> print(res.message)
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Optimization terminated successfully.
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>>> res.hess_inv
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array([[ 0.00749589, 0.01255155, 0.02396251, 0.04750988, 0.09495377], # may vary
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[ 0.01255155, 0.02510441, 0.04794055, 0.09502834, 0.18996269],
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[ 0.02396251, 0.04794055, 0.09631614, 0.19092151, 0.38165151],
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[ 0.04750988, 0.09502834, 0.19092151, 0.38341252, 0.7664427 ],
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[ 0.09495377, 0.18996269, 0.38165151, 0.7664427, 1.53713523]])
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Next, consider a minimization problem with several constraints (namely
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Example 16.4 from [5]_). The objective function is:
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>>> fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
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There are three constraints defined as:
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>>> cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2 * x[1] + 2},
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... {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
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... {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
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And variables must be positive, hence the following bounds:
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>>> bnds = ((0, None), (0, None))
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The optimization problem is solved using the SLSQP method as:
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>>> res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,
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... constraints=cons)
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It should converge to the theoretical solution (1.4 ,1.7).
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"""
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x0 = np.asarray(x0)
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if x0.dtype.kind in np.typecodes["AllInteger"]:
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x0 = np.asarray(x0, dtype=float)
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if not isinstance(args, tuple):
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args = (args,)
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if method is None:
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# Select automatically
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if constraints:
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method = 'SLSQP'
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elif bounds is not None:
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method = 'L-BFGS-B'
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else:
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method = 'BFGS'
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if callable(method):
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meth = "_custom"
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else:
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meth = method.lower()
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if options is None:
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options = {}
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# check if optional parameters are supported by the selected method
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# - jac
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if meth in ('nelder-mead', 'powell', 'cobyla') and bool(jac):
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warn('Method %s does not use gradient information (jac).' % method,
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RuntimeWarning)
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# - hess
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if meth not in ('newton-cg', 'dogleg', 'trust-ncg', 'trust-constr',
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'trust-krylov', 'trust-exact', '_custom') and hess is not None:
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warn('Method %s does not use Hessian information (hess).' % method,
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RuntimeWarning)
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# - hessp
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if meth not in ('newton-cg', 'dogleg', 'trust-ncg', 'trust-constr',
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'trust-krylov', '_custom') \
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and hessp is not None:
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warn('Method %s does not use Hessian-vector product '
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'information (hessp).' % method, RuntimeWarning)
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# - constraints or bounds
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if (meth in ('nelder-mead', 'cg', 'bfgs', 'newton-cg', 'dogleg',
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'trust-ncg') and (bounds is not None or np.any(constraints))):
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warn('Method %s cannot handle constraints nor bounds.' % method,
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RuntimeWarning)
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if meth in ('l-bfgs-b', 'tnc', 'powell') and np.any(constraints):
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warn('Method %s cannot handle constraints.' % method,
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RuntimeWarning)
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if meth == 'cobyla' and bounds is not None:
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warn('Method %s cannot handle bounds.' % method,
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RuntimeWarning)
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# - callback
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if (meth in ('cobyla',) and callback is not None):
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warn('Method %s does not support callback.' % method, RuntimeWarning)
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# - return_all
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if (meth in ('l-bfgs-b', 'tnc', 'cobyla', 'slsqp') and
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options.get('return_all', False)):
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warn('Method %s does not support the return_all option.' % method,
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RuntimeWarning)
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# check gradient vector
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if callable(jac):
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pass
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elif jac is True:
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# fun returns func and grad
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fun = MemoizeJac(fun)
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jac = fun.derivative
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elif (jac in FD_METHODS and
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meth in ['trust-constr', 'bfgs', 'cg', 'l-bfgs-b', 'tnc', 'slsqp']):
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# finite differences with relative step
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pass
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elif meth in ['trust-constr']:
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# default jac calculation for this method
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jac = '2-point'
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elif jac is None or bool(jac) is False:
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# this will cause e.g. LBFGS to use forward difference, absolute step
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jac = None
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else:
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# default if jac option is not understood
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jac = None
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# set default tolerances
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if tol is not None:
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options = dict(options)
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if meth == 'nelder-mead':
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options.setdefault('xatol', tol)
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options.setdefault('fatol', tol)
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if meth in ('newton-cg', 'powell', 'tnc'):
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options.setdefault('xtol', tol)
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if meth in ('powell', 'l-bfgs-b', 'tnc', 'slsqp'):
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options.setdefault('ftol', tol)
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if meth in ('bfgs', 'cg', 'l-bfgs-b', 'tnc', 'dogleg',
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'trust-ncg', 'trust-exact', 'trust-krylov'):
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options.setdefault('gtol', tol)
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if meth in ('cobyla', '_custom'):
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options.setdefault('tol', tol)
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if meth == 'trust-constr':
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options.setdefault('xtol', tol)
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options.setdefault('gtol', tol)
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options.setdefault('barrier_tol', tol)
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if meth == '_custom':
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# custom method called before bounds and constraints are 'standardised'
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# custom method should be able to accept whatever bounds/constraints
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# are provided to it.
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return method(fun, x0, args=args, jac=jac, hess=hess, hessp=hessp,
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bounds=bounds, constraints=constraints,
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callback=callback, **options)
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if bounds is not None:
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bounds = standardize_bounds(bounds, x0, meth)
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if constraints is not None:
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constraints = standardize_constraints(constraints, x0, meth)
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if meth == 'nelder-mead':
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return _minimize_neldermead(fun, x0, args, callback, **options)
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elif meth == 'powell':
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return _minimize_powell(fun, x0, args, callback, bounds, **options)
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elif meth == 'cg':
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return _minimize_cg(fun, x0, args, jac, callback, **options)
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elif meth == 'bfgs':
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return _minimize_bfgs(fun, x0, args, jac, callback, **options)
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elif meth == 'newton-cg':
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return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,
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**options)
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elif meth == 'l-bfgs-b':
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return _minimize_lbfgsb(fun, x0, args, jac, bounds,
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callback=callback, **options)
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elif meth == 'tnc':
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return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,
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**options)
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elif meth == 'cobyla':
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return _minimize_cobyla(fun, x0, args, constraints, **options)
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elif meth == 'slsqp':
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return _minimize_slsqp(fun, x0, args, jac, bounds,
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constraints, callback=callback, **options)
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elif meth == 'trust-constr':
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return _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,
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bounds, constraints,
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callback=callback, **options)
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elif meth == 'dogleg':
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return _minimize_dogleg(fun, x0, args, jac, hess,
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callback=callback, **options)
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elif meth == 'trust-ncg':
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return _minimize_trust_ncg(fun, x0, args, jac, hess, hessp,
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callback=callback, **options)
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elif meth == 'trust-krylov':
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return _minimize_trust_krylov(fun, x0, args, jac, hess, hessp,
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callback=callback, **options)
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elif meth == 'trust-exact':
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return _minimize_trustregion_exact(fun, x0, args, jac, hess,
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callback=callback, **options)
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else:
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raise ValueError('Unknown solver %s' % method)
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def minimize_scalar(fun, bracket=None, bounds=None, args=(),
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method='brent', tol=None, options=None):
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"""Minimization of scalar function of one variable.
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Parameters
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----------
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fun : callable
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Objective function.
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Scalar function, must return a scalar.
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bracket : sequence, optional
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For methods 'brent' and 'golden', `bracket` defines the bracketing
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interval and can either have three items ``(a, b, c)`` so that
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``a < b < c`` and ``fun(b) < fun(a), fun(c)`` or two items ``a`` and
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``c`` which are assumed to be a starting interval for a downhill
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bracket search (see `bracket`); it doesn't always mean that the
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obtained solution will satisfy ``a <= x <= c``.
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bounds : sequence, optional
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For method 'bounded', `bounds` is mandatory and must have two items
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corresponding to the optimization bounds.
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args : tuple, optional
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Extra arguments passed to the objective function.
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method : str or callable, optional
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Type of solver. Should be one of:
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- 'Brent' :ref:`(see here) <optimize.minimize_scalar-brent>`
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- 'Bounded' :ref:`(see here) <optimize.minimize_scalar-bounded>`
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- 'Golden' :ref:`(see here) <optimize.minimize_scalar-golden>`
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- custom - a callable object (added in version 0.14.0), see below
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tol : float, optional
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Tolerance for termination. For detailed control, use solver-specific
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options.
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options : dict, optional
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A dictionary of solver options.
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maxiter : int
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Maximum number of iterations to perform.
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disp : bool
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Set to True to print convergence messages.
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See :func:`show_options()` for solver-specific options.
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Returns
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-------
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res : OptimizeResult
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The optimization result represented as a ``OptimizeResult`` object.
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Important attributes are: ``x`` the solution array, ``success`` a
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Boolean flag indicating if the optimizer exited successfully and
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``message`` which describes the cause of the termination. See
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`OptimizeResult` for a description of other attributes.
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See also
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--------
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minimize : Interface to minimization algorithms for scalar multivariate
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functions
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show_options : Additional options accepted by the solvers
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Notes
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-----
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This section describes the available solvers that can be selected by the
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'method' parameter. The default method is *Brent*.
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Method :ref:`Brent <optimize.minimize_scalar-brent>` uses Brent's
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algorithm to find a local minimum. The algorithm uses inverse
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parabolic interpolation when possible to speed up convergence of
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the golden section method.
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Method :ref:`Golden <optimize.minimize_scalar-golden>` uses the
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golden section search technique. It uses analog of the bisection
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method to decrease the bracketed interval. It is usually
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preferable to use the *Brent* method.
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Method :ref:`Bounded <optimize.minimize_scalar-bounded>` can
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perform bounded minimization. It uses the Brent method to find a
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local minimum in the interval x1 < xopt < x2.
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**Custom minimizers**
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It may be useful to pass a custom minimization method, for example
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when using some library frontend to minimize_scalar. You can simply
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pass a callable as the ``method`` parameter.
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The callable is called as ``method(fun, args, **kwargs, **options)``
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where ``kwargs`` corresponds to any other parameters passed to `minimize`
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(such as `bracket`, `tol`, etc.), except the `options` dict, which has
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its contents also passed as `method` parameters pair by pair. The method
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shall return an `OptimizeResult` object.
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The provided `method` callable must be able to accept (and possibly ignore)
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arbitrary parameters; the set of parameters accepted by `minimize` may
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expand in future versions and then these parameters will be passed to
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the method. You can find an example in the scipy.optimize tutorial.
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.. versionadded:: 0.11.0
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Examples
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--------
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Consider the problem of minimizing the following function.
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>>> def f(x):
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... return (x - 2) * x * (x + 2)**2
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Using the *Brent* method, we find the local minimum as:
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>>> from scipy.optimize import minimize_scalar
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>>> res = minimize_scalar(f)
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>>> res.x
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1.28077640403
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Using the *Bounded* method, we find a local minimum with specified
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bounds as:
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>>> res = minimize_scalar(f, bounds=(-3, -1), method='bounded')
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>>> res.x
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-2.0000002026
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"""
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if not isinstance(args, tuple):
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args = (args,)
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if callable(method):
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meth = "_custom"
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else:
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meth = method.lower()
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if options is None:
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options = {}
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if tol is not None:
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options = dict(options)
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if meth == 'bounded' and 'xatol' not in options:
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warn("Method 'bounded' does not support relative tolerance in x; "
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"defaulting to absolute tolerance.", RuntimeWarning)
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options['xatol'] = tol
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elif meth == '_custom':
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options.setdefault('tol', tol)
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else:
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options.setdefault('xtol', tol)
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if meth == '_custom':
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return method(fun, args=args, bracket=bracket, bounds=bounds, **options)
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elif meth == 'brent':
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return _minimize_scalar_brent(fun, bracket, args, **options)
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elif meth == 'bounded':
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if bounds is None:
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raise ValueError('The `bounds` parameter is mandatory for '
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'method `bounded`.')
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# replace boolean "disp" option, if specified, by an integer value, as
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# expected by _minimize_scalar_bounded()
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disp = options.get('disp')
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if isinstance(disp, bool):
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options['disp'] = 2 * int(disp)
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return _minimize_scalar_bounded(fun, bounds, args, **options)
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elif meth == 'golden':
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return _minimize_scalar_golden(fun, bracket, args, **options)
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else:
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raise ValueError('Unknown solver %s' % method)
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def standardize_bounds(bounds, x0, meth):
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"""Converts bounds to the form required by the solver."""
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if meth in {'trust-constr', 'powell'}:
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if not isinstance(bounds, Bounds):
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lb, ub = old_bound_to_new(bounds)
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bounds = Bounds(lb, ub)
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elif meth in ('l-bfgs-b', 'tnc', 'slsqp'):
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if isinstance(bounds, Bounds):
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bounds = new_bounds_to_old(bounds.lb, bounds.ub, x0.shape[0])
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return bounds
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def standardize_constraints(constraints, x0, meth):
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"""Converts constraints to the form required by the solver."""
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all_constraint_types = (NonlinearConstraint, LinearConstraint, dict)
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new_constraint_types = all_constraint_types[:-1]
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if isinstance(constraints, all_constraint_types):
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constraints = [constraints]
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constraints = list(constraints) # ensure it's a mutable sequence
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if meth == 'trust-constr':
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for i, con in enumerate(constraints):
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if not isinstance(con, new_constraint_types):
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constraints[i] = old_constraint_to_new(i, con)
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else:
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# iterate over copy, changing original
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for i, con in enumerate(list(constraints)):
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if isinstance(con, new_constraint_types):
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old_constraints = new_constraint_to_old(con, x0)
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constraints[i] = old_constraints[0]
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constraints.extend(old_constraints[1:]) # appends 1 if present
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return constraints
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