minimize(method=’newton-exact’)¶
-
torchmin.newton.
_minimize_newton_exact
(fun, x0, lr=1.0, max_iter=None, line_search='strong-wolfe', xtol=1e-05, normp=1, tikhonov=0.0, handle_npd='grad', callback=None, disp=0, return_all=False)[source]¶ Minimize a scalar function of one or more variables using the Newton-Raphson method.
This variant uses an “exact” Newton routine based on Cholesky factorization of the explicit Hessian matrix.
- Parameters
fun (callable) – Scalar objective function to minimize.
x0 (Tensor) – Initialization point.
lr (float) – Step size for parameter updates. If using line search, this will be used as the initial step size for the search.
max_iter (int, optional) – Maximum number of iterations to perform. Defaults to
200 * x0.numel()
.line_search (str) – Line search specifier. Currently the available options are {‘none’, ‘strong_wolfe’}.
xtol (float) – Average relative error in solution xopt acceptable for convergence.
normp (Number or str) – The norm type to use for termination conditions. Can be any value supported by
torch.norm()
.tikhonov (float) – Optional diagonal regularization (Tikhonov) parameter for the Hessian.
handle_npd (str) –
Mode for handling non-positive definite hessian matrices. Can be one of the following:
’grad’ : use steepest descent direction (gradient)
’lu’ : solve the inverse hessian with LU factorization
’eig’ : use symmetric eigendecomposition to determine a diagonal regularization parameter
callback (callable, optional) – Function to call after each iteration with the current parameter state, e.g.
callback(x)
.disp (int or bool) – Display (verbosity) level. Set to >0 to print status messages.
return_all (bool) – Set to True to return a list of the best solution at each of the iterations.
- Returns
result – Result of the optimization routine.
- Return type
OptimizeResult