Show documentation for additional options of optimization solvers.
These are methodspecific options that can be supplied through the options dict.
Parameters :  solver : str method : str, optional


Notes
** minimize options
Gradient norm must be less than gtol before successful termination.
Order of norm (Inf is max, Inf is min).
If jac is approximated, use this value for the step size.
Relative error in solution xopt acceptable for convergence.
Relative error in fun(xopt) acceptable for convergence.
Maximum number of function evaluations to make.
Average relative error in solution xopt acceptable for convergence.
If jac is approximated, use this value for the step size.
Gradient norm must be less than gtol before successful termination.
Order of norm (Inf is max, Inf is min).
If jac is approximated, use this value for the step size.
Relative error in solution xopt acceptable for convergence.
Relative error in fun(xopt) acceptable for convergence.
Maximum number of function evaluations to make.
Initial set of direction vectors for the Powell method.
Relative error in fun(x) acceptable for convergence.
Annealing schedule to use. One of: ‘fast’, ‘cauchy’ or ‘boltzmann’.
Initial Temperature (estimated as 1.2 times the largest costfunction deviation over random points in the range).
Final goal temperature.
Maximum number of function evaluations to make.
Maximum changes to accept.
Boltzmann constant in acceptance test (increase for less stringent test at each temperature).
Scale constant for adjusting guesses.
Parameters to alter fast_sa schedule.
Lower and upper bounds on x.
The number of times to search the space at each temperature.
The iteration stops when (f^k  f^{k+1})/max{f^k,f^{k+1},1} <= ftol.
The iteration will stop when max{proj g_i  i = 1, ..., n} <= gtol where pg_i is the ith component of the projected gradient.
The maximum number of variable metric corrections used to define the limited memory matrix. (The limited memory BFGS method does not store the full hessian but uses this many terms in an approximation to it.)
Maximum number of function evaluations.
Precision goal for the value of f in the stoping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to 1.
Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to 1.
Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to 1.
Scaling factors to apply to each variable. If None, the factors are uplow for interval bounded variables and 1+x] fo the others. Defaults to None
Value to substract from each variable. If None, the offsets are (up+low)/2 for interval bounded variables and x for the others.
Maximum number of hessian*vector evaluations per main iteration. If maxCGit == 0, the direction chosen is gradient if maxCGit < 0, maxCGit is set to max(1,min(50,n/2)). Defaults to 1.
Maximum number of function evaluation. if None, maxiter is set to max(100, 10*len(x0)). Defaults to None.
Severity of the line search. if < 0 or > 1, set to 0.25. Defaults to 1.
Maximum step for the line search. May be increased during call. If too small, it will be set to 10.0. Defaults to 0.
Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.
Minimum function value estimate. Defaults to 0.
Scaling factor (in log10) used to trigger f value rescaling. If 0, rescale at each iteration. If a large value, never rescale. If < 0, rescale is set to 1.3.
Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region.
Reasonable initial changes to the variables.
Maximum number of function evaluations.
Precision goal for the value of f in the stopping criterion.
Step size used for numerical approximation of the jacobian.
Maximum number of iterations.
** root options
Specify whether the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).
The calculation will terminate if the relative error between two consecutive iterates is at most xtol.
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.
If set to a twosequence containing the number of sub and superdiagonals within the band of the Jacobi matrix, the Jacobi matrix is considered banded (only for fprime=None).
A suitable step length for the forwarddifference approximation of the Jacobian (for fprime=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.
A parameter determining the initial step bound (factor *  diag * x). Should be in the interval (0.1, 100).
N positive entries that serve as a scale factors for the variables.
nonzero to specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation).
Relative error desired in the sum of squares.
Relative error desired in the approximate solution.
Orthogonality desired between the function vector and the columns of the Jacobian.
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.
A suitable step length for the forwarddifference 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.
A parameter determining the initial step bound (factor *  diag * x). Should be in interval (0.1, 100).
N positive entries that serve as a scale factors for the variables.
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
Initial guess for the Jacobian is (1/alpha).
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.
extra parameters.
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.
Maximum rank for the Broyden matrix. Default is infinity (ie., no rank reduction).
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
Initial guess for the Jacobian is (1/alpha).
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.
extra parameters.
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.
Maximum rank for the Broyden matrix. Default is infinity (ie., no rank reduction).
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
Initial guess for the Jacobian is (1/alpha).
Number of previous vectors to retain. Defaults to 5.
Regularization parameter for numerical stability. Compared to unity, good values of the order of 0.01.
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
initial guess for the jacobian is (1/alpha).
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
initial guess for the jacobian is (1/alpha).
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
Initial Jacobian approximation is (1/alpha).
The entries of the diagonal Jacobian are kept in the range [alpha, alphamax].
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
Print status to stdout on every iteration.
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
Relative tolerance for the residual. If omitted, not used.
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
Relative minimum step size. If omitted, not used.
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.
Norm to use in convergence check. Default is the maximum norm.
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
Relative step size to use in numerical differentiation.
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.
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.
Parameters to pass on to the “inner” Krylov solver. See scipy.sparse.linalg.gmres for details.
Size of the subspace kept across LGMRES nonlinear iterations. See scipy.sparse.linalg.lgmres for details.