+ /i"pRtR.t^RIt^RIHt^RIHtHtH t ^RI H t H t ^RI Ht^RIHt.ROtRRltR tRR ltRR ltR tR tRtRtRtRtRtR#)zp Unified interfaces to root finding algorithms. Functions --------- - root : find a root of a vector function. rootN)warn) MemoizeJacOptimizeResult_check_unknown_options) _root_hybrleastsq) _root_df_sane)_nonlinc aa V V3Rlo ^S n\V\4'gV3pVP4pVf/pVeVR9d\ RV R2\ ^R7\ V4'g3VR9d,\V4'd\S4oSPpMRpVe\V4pVR9dVPRV4MVR9dVPR V4MyVR9dsVPRV4VPR \P4VPR \P4VPR \P4VR8Xd\S V3R VRV/VBp MsVR8Xd\S V3R VRV/VBp MZVR 8Xd\!WC4\#S V3R VRV/VBp M6VR9d"\!WC4\%S V3R VRVRVRV/VBp M\'RV 24hS PV nV #)a Find a root of a vector function. Parameters ---------- fun : callable A vector function to find a root of. Suppose the callable has signature ``f0(x, *my_args, **my_kwargs)``, where ``my_args`` and ``my_kwargs`` are required positional and keyword arguments. Rather than passing ``f0`` as the callable, wrap it to accept only ``x``; e.g., pass ``fun=lambda x: f0(x, *my_args, **my_kwargs)`` as the callable, where ``my_args`` (tuple) and ``my_kwargs`` (dict) have been gathered before invoking this function. 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) ` - 'lm' :ref:`(see here) ` - 'broyden1' :ref:`(see here) ` - 'broyden2' :ref:`(see here) ` - 'anderson' :ref:`(see here) ` - 'linearmixing' :ref:`(see here) ` - 'diagbroyden' :ref:`(see here) ` - 'excitingmixing' :ref:`(see here) ` - 'krylov' :ref:`(see here) ` - 'df-sane' :ref:`(see here) ` 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. - 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. :doi:`10.1137/1.9781611970944` .. [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. >>> import numpy as np >>> 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]) **Large problem** Suppose that we needed to solve the following integrodifferential equation on the square :math:`[0,1]\times[0,1]`: .. math:: \nabla^2 P = 10 \left(\int_0^1\int_0^1\cosh(P)\,dx\,dy\right)^2 with :math:`P(x,1) = 1` and :math:`P=0` elsewhere on the boundary of the square. The solution can be found using the ``method='krylov'`` solver: >>> from scipy import optimize >>> # parameters >>> nx, ny = 75, 75 >>> hx, hy = 1./(nx-1), 1./(ny-1) >>> P_left, P_right = 0, 0 >>> P_top, P_bottom = 1, 0 >>> def residual(P): ... d2x = np.zeros_like(P) ... d2y = np.zeros_like(P) ... ... d2x[1:-1] = (P[2:] - 2*P[1:-1] + P[:-2]) / hx/hx ... d2x[0] = (P[1] - 2*P[0] + P_left)/hx/hx ... d2x[-1] = (P_right - 2*P[-1] + P[-2])/hx/hx ... ... d2y[:,1:-1] = (P[:,2:] - 2*P[:,1:-1] + P[:,:-2])/hy/hy ... d2y[:,0] = (P[:,1] - 2*P[:,0] + P_bottom)/hy/hy ... d2y[:,-1] = (P_top - 2*P[:,-1] + P[:,-2])/hy/hy ... ... return d2x + d2y - 10*np.cosh(P).mean()**2 >>> guess = np.zeros((nx, ny), float) >>> sol = optimize.root(residual, guess, method='krylov') >>> print('Residual: %g' % abs(residual(sol.x)).max()) Residual: 5.7972e-06 # may vary >>> import matplotlib.pyplot as plt >>> x, y = np.mgrid[0:1:(nx*1j), 0:1:(ny*1j)] >>> plt.pcolormesh(x, y, sol.x, shading='gouraud') >>> plt.colorbar() >>> plt.show() cB<S;P^, unS!V!#zK Wrapped `func` to track the number of times the function has been called. )nfev)fargs _wrapped_funfuns*R/var/www/html/photoedit/myenv/lib/python3.14/site-packages/scipy/optimize/_root.pyrroot.._wrapped_funs QE{NhybrlmMethod z does not accept callback. stacklevelxtoldf-saneftolxatolfatolargsjaccallback_method _callbackzUnknown solver )rr)r)broyden1broyden2anderson linearmixing diagbroydenexcitingmixingkrylov)r isinstancetuplelowerrRuntimeWarningcallableboolr derivativedict setdefaultnpinfr _root_leastsq_warn_jac_unusedr _root_nonlin_solve ValueError) rx0rmethodr tolr!optionsmethsolrs f&&&&&&& @rrrsrL dE " "w <<>D 6 wvh8 9  + C==T^3 99S/C..CC w- > !   vs + \ !   vs + AA   vs +   w /   vrvv .   w / v~rII#II L"L4LSLGL  %L"'4'('%' = =% r,,#,)-,9A,#*,?6(344  CH Jrc>Ve\RV R2\^R7R#R#)Nrz! does not use the jacobian (jac).r)rr.)r r;s&&rr7r7s%  wvh? @  +rc  aa^oVV3Rlp \V 4\WVVRWEWgWWR7 wrppp\VVVVR9VVPR4RR7pVP V4SVnV#)aR 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. eps : float A suitable step length for the forward-difference approximation of the Jacobian (for Dfun=None). If `eps` 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. c"<S^, oS!V!#r )rrrs*rr#_root_leastsq.._wrapped_fun:s  E{rT) rDfun full_output col_derivrrgtolmaxfevepsfcnfactordiagfvecr)xmessagestatussuccesscov_xrr;))rrrpopupdater)rr:rr rGrrrHmaxiterepsrKrLunknown_optionsrrNrRinfomsgierr?rsf&&&&&&&&&&&, @rr6r6sB D?+&|d,/T1:,0.5.4  AAdC 1c#!$ !4E!XXf-d .fms1}t}Q''rc<S!V.SO5!#)NrCrbs&rrcrdps1}t}$rF) jacobianiterverboserYf_tolf_rtolx_tolx_rtoltol_norm line_searchr!rFraise_exception)rNr;rC) rr2nonlin BroydenFirst BroydenSecondAnderson LinearMixing DiagBroydenExcitingMixingKrylovJacobian nonlin_solverrX)rr:rr r#r"nitdisprYrrrrrmrn jac_optionsr[rirjrkrlrhrfrcrNr\r?sf&f&&&&&&&&&&&&&, rr8r8Rs  ?+ E F E FGf F//F00FOO 3 3v11 &"7"7&// H $; ( % !!!R(2I[2I'*G*1)/u)/(.9+4$279GAt 1W -CJJt JrcR#)a 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. 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. Takes an extra parameter, ``to_retain``, which determines the 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 (i.e., no rank reduction). Examples -------- >>> def func(x): ... return np.cos(x) + x[::-1] - [1, 2, 3, 4] ... >>> from scipy import optimize >>> res = optimize.root(func, [1, 1, 1, 1], method='broyden1', tol=1e-14) >>> x = res.x >>> x array([4.04674914, 3.91158389, 2.71791677, 1.61756251]) >>> np.cos(x) + x[::-1] array([1., 2., 3., 4.]) NrCrCrr_root_broyden1_docr}sF rcR#)ak 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. 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. Takes an extra parameter, ``to_retain``, which determines the 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 (i.e., no rank reduction). NrCrCrr_root_broyden2_docrsj rcR#)a 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. 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. NrCrCrr_root_anderson_docrsN rcR#a 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. 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). NrCrCrr_root_linearmixing_docr(D rcR#rrCrCrr_root_diagbroyden_docrLrrcR#)ak 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. 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]``. NrCrCrr_root_excitingmixing_docrpsJ rcR#)a` 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. 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 : str or callable, optional 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`. If a string, needs to be one of: ``'lgmres'``, ``'gmres'``, ``'bicgstab'``, ``'cgs'``, ``'minres'``, ``'tfqmr'``. 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_rtol, inner_atol, inner_callback, ... Parameters to pass on to the "inner" Krylov solver. For a full list of options, see the documentation for the solver you are using. By default this is `scipy.sparse.linalg.lgmres`. If the solver has been overridden through `method`, see the documentation for that solver instead. To use an option for that solver, prepend ``inner_`` to it. For example, to control the ``rtol`` argument to the solver, set the `inner_rtol` option here. outer_k : int, optional Size of the subspace kept across LGMRES nonlinear iterations. See `scipy.sparse.linalg.lgmres` for details. NrCrCrr_root_krylov_docrsL r) rrr$r%r&r'r(r)r*r)rCrNNNN) rCNraJP>rrardN)rCNNNNFNNNNNNarmijoN)__doc____all__numpyr4warningsr _optimizerrr _minpack_pyrr _spectralr r rp ROOT_METHODSrr7r6r8r}rrrrrrrCrrrsr (II,$ vr+ 7t-^C L5 p' R" H" H% NF r