Conjugate Gradient

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class olympus.planners.ConjugateGradient(*args, **kwargs)[source]

Conjugate Gradient optimizer based on the SciPy implementation.

Parameters

  • goal (str) – The optimization goal, either ‘minimize’ or ‘maximize’. Default is ‘minimize’.

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

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

  • gtol (float) – Gradient norm must be less than gtol before successful termination.

  • norm (float) – Order of norm (Inf is max, -Inf is min).

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

  • init_guess (array, optional) – initial guess for the optimization

  • init_guess_method (str) – method to construct initial guesses if init_guess is not provided. Choose from: random

  • init_guess_seed (str) – random seed for init_guess_method

Methods

tell([observations])

Provide the planner with all previous observations.

ask([return_as])

suggest new set of parameters

recommend([observations, return_as])

Consecutively executes tell and ask: tell the planner about all previous observations, and ask about the next query point.

optimize(emulator[, num_iter, verbose])

Optimizes a surface for a fixed number of iterations.