Conjugate Gradient¶
does this and that…
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class
olympus.planners.ConjugateGradient(goal='minimize', disp=False, maxiter=None, gtol=1e-05, norm=inf, eps=1.4901161193847656e-08, init_guess=None, init_guess_method='random', init_guess_seed=None)[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.
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ask(return_as=None) suggest new set of parameters
- Parameters
return_as (string) – choose data type for returned parameters allowed options (dict, array)
- Returns
newly generated parameters
- Return type
ParameterVector
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optimize(emulator, num_iter=1, verbose=False) Optimizes a surface for a fixed number of iterations.
- Parameters
emulator (object) – Emulator or a Surface instance to optimize over.
num_iter (int) – Maximum number of iterations allowed.
verbose (bool) – Whether to print information to screen.
- Returns
- Campaign object with information about the optimization, including all parameters
tested and measurements obtained.
- Return type
campaign (Campaign)
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recommend(observations=None, return_as=None) Consecutively executes tell and ask: tell the planner about all previous observations, and ask about the next query point.
- Parameters
observations (list of ???) –
return_as (string) – choose data type for returned parameters allowed options (dict, array)
- Returns
newly generated parameters
- Return type
list
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set_param_space(param_space) Defines the parameter space over which the planner will search.
- Parameters
param_space (ParameterSpace) – a ParameterSpace object defining the space over which to search.
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tell(observations=<olympus.campaigns.observations.Observations object>) Provide the planner with all previous observations.
- Parameters
observations (Observations) – an Observation object containing all previous observations. This defines the history of the campaign seen by the planner. The default is None, i.e. there are no previous observations.