File Content: _root.py """ Unified interfaces to root finding algorithms. Functions --------- - root : find a root of a vector function. """ from __future__ import division, print_function, absolute_import __all__ = ['root'] import numpy as np from scipy._lib.six import callable from warnings import warn from .optimize import MemoizeJac, OptimizeResult, _check_unknown_options from .minpack import _root_hybr, leastsq from ._spectral import _root_df_sane from . import nonlin def root(fun, x0, args=(), method='hybr', jac=None, tol=None, callback=None, options=None): """ Find a root of a vector function. Parameters ---------- fun : callable A vector function to find a root of. x0 : ndarray Initial guess. args : tuple, optional Extra arguments passed to the objective function and its Jacobian. method : str, optional Type of solver. Should be one of - 'hybr' :ref:`(see here) <optimize.root-hybr>` - 'lm' :ref:`(see here) <optimize.root-lm>` - 'broyden1' :ref:`(see here) <optimize.root-broyden1>` - 'broyden2' :ref:`(see here) <optimize.root-broyden2>` - 'anderson' :ref:`(see here) <optimize.root-anderson>` - 'linearmixing' :ref:`(see here) <optimize.root-linearmixing>` - 'diagbroyden' :ref:`(see here) <optimize.root-diagbroyden>` - 'excitingmixing' :ref:`(see here) <optimize.root-excitingmixing>` - 'krylov' :ref:`(see here) <optimize.root-krylov>` - 'df-sane' :ref:`(see here) <optimize.root-dfsane>` jac : bool or callable, optional If `jac` is a Boolean and is True, `fun` is assumed to return the value of Jacobian along with the objective function. If False, the Jacobian will be estimated numerically. `jac` can also be a callable returning the Jacobian of `fun`. In this case, it must accept the same arguments as `fun`. tol : float, optional Tolerance for termination. For detailed control, use solver-specific options. callback : function, optional Optional callback function. It is called on every iteration as ``callback(x, f)`` where `x` is the current solution and `f` the corresponding residual. For all methods but 'hybr' and 'lm'. options : dict, optional A dictionary of solver options. E.g. `xtol` or `maxiter`, see :obj:`show_options()` for details. Returns ------- sol : OptimizeResult The solution represented as a ``OptimizeResult`` object. Important attributes are: ``x`` the solution array, ``success`` a Boolean flag indicating if the algorithm exited successfully and ``message`` which describes the cause of the termination. See `OptimizeResult` for a description of other attributes. See also -------- 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 *hybr*. Method *hybr* uses a modification of the Powell hybrid method as implemented in MINPACK [1]_. Method *lm* solves the system of nonlinear equations in a least squares sense using a modification of the Levenberg-Marquardt algorithm as implemented in MINPACK [1]_. Method *df-sane* is a derivative-free spectral method. [3]_ Methods *broyden1*, *broyden2*, *anderson*, *linearmixing*, *diagbroyden*, *excitingmixing*, *krylov* are inexact Newton methods, with backtracking or full line searches [2]_. Each method corresponds to a particular Jacobian approximations. See `nonlin` for details. - Method *broyden1* uses Broyden's first Jacobian approximation, it is known as Broyden's good method. - Method *broyden2* uses Broyden's second Jacobian approximation, it is known as Broyden's bad method. - Method *anderson* uses (extended) Anderson mixing. - Method *Krylov* uses Krylov approximation for inverse Jacobian. It is suitable for large-scale problem. - Method *diagbroyden* uses diagonal Broyden Jacobian approximation. - Method *linearmixing* uses a scalar Jacobian approximation. - Method *excitingmixing* uses a tuned diagonal Jacobian approximation. .. warning:: The algorithms implemented for methods *diagbroyden*, *linearmixing* and *excitingmixing* may be useful for specific problems, but whether they will work may depend strongly on the problem. .. versionadded:: 0.11.0 References ---------- .. [1] More, Jorge J., Burton S. Garbow, and Kenneth E. Hillstrom. 1980. User Guide for MINPACK-1. .. [2] C. T. Kelley. 1995. Iterative Methods for Linear and Nonlinear Equations. Society for Industrial and Applied Mathematics. <http://www.siam.org/books/kelley/> .. [3] W. La Cruz, J.M. Martinez, M. Raydan. Math. Comp. 75, 1429 (2006). Examples -------- The following functions define a system of nonlinear equations and its jacobian. >>> def fun(x): ... return [x[0] + 0.5 * (x[0] - x[1])**3 - 1.0, ... 0.5 * (x[1] - x[0])**3 + x[1]] >>> def jac(x): ... return np.array([[1 + 1.5 * (x[0] - x[1])**2, ... -1.5 * (x[0] - x[1])**2], ... [-1.5 * (x[1] - x[0])**2, ... 1 + 1.5 * (x[1] - x[0])**2]]) A solution can be obtained as follows. >>> from scipy import optimize >>> sol = optimize.root(fun, [0, 0], jac=jac, method='hybr') >>> sol.x array([ 0.8411639, 0.1588361]) """ if not isinstance(args, tuple): args = (args,) meth = method.lower() if options is None: options = {} if callback is not None and meth in ('hybr', 'lm'): warn('Method %s does not accept callback.' % method, RuntimeWarning) # fun also returns the jacobian if not callable(jac) and meth in ('hybr', 'lm'): 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 in ('hybr', 'lm'): options.setdefault('xtol', tol) elif meth in ('df-sane',): options.setdefault('ftol', tol) elif meth in ('broyden1', 'broyden2', 'anderson', 'linearmixing', 'diagbroyden', 'excitingmixing', 'krylov'): options.setdefault('xtol', tol) options.setdefault('xatol', np.inf) options.setdefault('ftol', np.inf) options.setdefault('fatol', np.inf) if meth == 'hybr': sol = _root_hybr(fun, x0, args=args, jac=jac, **options) elif meth == 'lm': sol = _root_leastsq(fun, x0, args=args, jac=jac, **options) elif meth == 'df-sane': _warn_jac_unused(jac, method) sol = _root_df_sane(fun, x0, args=args, callback=callback, **options) elif meth in ('broyden1', 'broyden2', 'anderson', 'linearmixing', 'diagbroyden', 'excitingmixing', 'krylov'): _warn_jac_unused(jac, method) sol = _root_nonlin_solve(fun, x0, args=args, jac=jac, _method=meth, _callback=callback, **options) else: raise ValueError('Unknown solver %s' % method) return sol def _warn_jac_unused(jac, method): if jac is not None: warn('Method %s does not use the jacobian (jac).' % (method,), RuntimeWarning) def _root_leastsq(func, x0, args=(), jac=None, col_deriv=0, xtol=1.49012e-08, ftol=1.49012e-08, gtol=0.0, maxiter=0, eps=0.0, factor=100, diag=None, **unknown_options): """ Solve for least squares with Levenberg-Marquardt Options ------- col_deriv : bool non-zero to specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation). ftol : float Relative error desired in the sum of squares. xtol : float Relative error desired in the approximate solution. gtol : float Orthogonality desired between the function vector and the columns of the Jacobian. maxiter : int The maximum number of calls to the function. If zero, then 100*(N+1) is the maximum where N is the number of elements in x0. epsfcn : float A suitable step length for the forward-difference approximation of the Jacobian (for Dfun=None). If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision. factor : float A parameter determining the initial step bound (``factor * || diag * x||``). Should be in interval ``(0.1, 100)``. diag : sequence N positive entries that serve as a scale factors for the variables. """ _check_unknown_options(unknown_options) x, cov_x, info, msg, ier = leastsq(func, x0, args=args, Dfun=jac, full_output=True, col_deriv=col_deriv, xtol=xtol, ftol=ftol, gtol=gtol, maxfev=maxiter, epsfcn=eps, factor=factor, diag=diag) sol = OptimizeResult(x=x, message=msg, status=ier, success=ier in (1, 2, 3, 4), cov_x=cov_x, fun=info.pop('fvec')) sol.update(info) return sol def _root_nonlin_solve(func, x0, args=(), jac=None, _callback=None, _method=None, nit=None, disp=False, maxiter=None, ftol=None, fatol=None, xtol=None, xatol=None, tol_norm=None, line_search='armijo', jac_options=None, **unknown_options): _check_unknown_options(unknown_options) f_tol = fatol f_rtol = ftol x_tol = xatol x_rtol = xtol verbose = disp if jac_options is None: jac_options = dict() jacobian = {'broyden1': nonlin.BroydenFirst, 'broyden2': nonlin.BroydenSecond, 'anderson': nonlin.Anderson, 'linearmixing': nonlin.LinearMixing, 'diagbroyden': nonlin.DiagBroyden, 'excitingmixing': nonlin.ExcitingMixing, 'krylov': nonlin.KrylovJacobian }[_method] if args: if jac: def f(x): return func(x, *args)[0] else: def f(x): return func(x, *args) else: f = func x, info = nonlin.nonlin_solve(f, x0, jacobian=jacobian(**jac_options), iter=nit, verbose=verbose, maxiter=maxiter, f_tol=f_tol, f_rtol=f_rtol, x_tol=x_tol, x_rtol=x_rtol, tol_norm=tol_norm, line_search=line_search, callback=_callback, full_output=True, raise_exception=False) sol = OptimizeResult(x=x) sol.update(info) return sol def _root_broyden1_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. alpha : float, optional Initial guess for the Jacobian is (-1/alpha). reduction_method : str or tuple, optional Method used in ensuring that the rank of the Broyden matrix stays low. Can either be a string giving the name of the method, or a tuple of the form ``(method, param1, param2, ...)`` that gives the name of the method and values for additional parameters. Methods available: - ``restart``: drop all matrix columns. Has no extra parameters. - ``simple``: drop oldest matrix column. Has no extra parameters. - ``svd``: keep only the most significant SVD components. Extra parameters: - ``to_retain``: number of SVD components to retain when rank reduction is done. Default is ``max_rank - 2``. max_rank : int, optional Maximum rank for the Broyden matrix. Default is infinity (ie., no rank reduction). """ pass def _root_broyden2_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. alpha : float, optional Initial guess for the Jacobian is (-1/alpha). reduction_method : str or tuple, optional Method used in ensuring that the rank of the Broyden matrix stays low. Can either be a string giving the name of the method, or a tuple of the form ``(method, param1, param2, ...)`` that gives the name of the method and values for additional parameters. Methods available: - ``restart``: drop all matrix columns. Has no extra parameters. - ``simple``: drop oldest matrix column. Has no extra parameters. - ``svd``: keep only the most significant SVD components. Extra parameters: - ``to_retain``: number of SVD components to retain when rank reduction is done. Default is ``max_rank - 2``. max_rank : int, optional Maximum rank for the Broyden matrix. Default is infinity (ie., no rank reduction). """ pass def _root_anderson_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. alpha : float, optional Initial guess for the Jacobian is (-1/alpha). M : float, optional Number of previous vectors to retain. Defaults to 5. w0 : float, optional Regularization parameter for numerical stability. Compared to unity, good values of the order of 0.01. """ pass def _root_linearmixing_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, ``NoConvergence`` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. alpha : float, optional initial guess for the jacobian is (-1/alpha). """ pass def _root_diagbroyden_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. alpha : float, optional initial guess for the jacobian is (-1/alpha). """ pass def _root_excitingmixing_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. alpha : float, optional Initial Jacobian approximation is (-1/alpha). alphamax : float, optional The entries of the diagonal Jacobian are kept in the range ``[alpha, alphamax]``. """ pass def _root_krylov_doc(): """ Options ------- nit : int, optional Number of iterations to make. If omitted (default), make as many as required to meet tolerances. disp : bool, optional Print status to stdout on every iteration. maxiter : int, optional Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised. ftol : float, optional Relative tolerance for the residual. If omitted, not used. fatol : float, optional Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6. xtol : float, optional Relative minimum step size. If omitted, not used. xatol : float, optional Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used. tol_norm : function(vector) -> scalar, optional Norm to use in convergence check. Default is the maximum norm. line_search : {None, 'armijo' (default), 'wolfe'}, optional Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to 'armijo'. jac_options : dict, optional Options for the respective Jacobian approximation. rdiff : float, optional Relative step size to use in numerical differentiation. method : {'lgmres', 'gmres', 'bicgstab', 'cgs', 'minres'} or function Krylov method to use to approximate the Jacobian. Can be a string, or a function implementing the same interface as the iterative solvers in `scipy.sparse.linalg`. The default is `scipy.sparse.linalg.lgmres`. inner_M : LinearOperator or InverseJacobian Preconditioner for the inner Krylov iteration. Note that you can use also inverse Jacobians as (adaptive) preconditioners. For example, >>> jac = BroydenFirst() >>> kjac = KrylovJacobian(inner_M=jac.inverse). If the preconditioner has a method named 'update', it will be called as ``update(x, f)`` after each nonlinear step, with ``x`` giving the current point, and ``f`` the current function value. inner_tol, inner_maxiter, ... Parameters to pass on to the "inner" Krylov solver. See `scipy.sparse.linalg.gmres` for details. outer_k : int, optional Size of the subspace kept across LGMRES nonlinear iterations. See `scipy.sparse.linalg.lgmres` for details. """ pass Submit