`TransportMaps.Optimization.Optimizers`¶

Module Contents¶

Functions¶

 `minimize_newtonlu`(fun, x0[, args, jac, hess, hessp, ...]) Minimization of scalar function of one or more variables using the Newton-LU algorithm. `minimize_newtoncglu`(fun, x0[, args, jac, hess, hessp, ...]) Minimization of scalar function of one or more variables using the Newton-LU algorithm.
TransportMaps.Optimization.Optimizers.minimize_newtonlu(fun, x0, args=(), jac=None, hess=None, hessp=None, callback=None, xtol=1e-05, eps=_epsilon, maxiter=None, disp=False, return_all=False, **unknown_options)[source]

Minimization of scalar function of one or more variables using the Newton-LU algorithm.

Note

the jac parameter (Jacobian) is required.

Parameters:
• disp (bool) – Set to True to print convergence messages.

• xtol (float) – Average relative error in solution xopt acceptable for convergence.

• maxiter (int) – Maximum number of iterations to perform.

• eps (float or ndarray) – If jac is approximated, use this value for the step size.

TransportMaps.Optimization.Optimizers.minimize_newtoncglu(fun, x0, args=(), jac=None, hess=None, hessp=None, callback=None, xtol=1e-05, eps=_epsilon, maxiter=None, disp=False, return_all=False, **unknown_options)[source]

Minimization of scalar function of one or more variables using the Newton-LU algorithm.

Note

the jac parameter (Jacobian) is required.

Parameters:
• disp (bool) – Set to True to print convergence messages.

• xtol (float) – Average relative error in solution xopt acceptable for convergence.

• maxiter (int) – Maximum number of iterations to perform.

• eps (float or ndarray) – If jac is approximated, use this value for the step size.