minimize(method=’cg’)

torchmin.cg._minimize_cg(fun, x0, max_iter=None, gtol=1e-05, normp=inf, callback=None, disp=0, return_all=False)[source]

Minimize a scalar function of one or more variables using nonlinear conjugate gradient.

The algorithm is described in Nocedal & Wright (2006) chapter 5.2.

Parameters

  • fun (callable) – Scalar objective function to minimize.

  • x0 (Tensor) – Initialization point.

  • max_iter (int) – Maximum number of iterations to perform. Defaults to 200 * x0.numel().

  • gtol (float) – Termination tolerance on 1st-order optimality (gradient norm).

  • normp (float) – The norm type to use for termination conditions. Can be any value supported by torch.norm().

  • 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, optional) – Set to True to return a list of the best solution at each of the iterations.