SLSQP¶

GPyOpt does this and that…

For details please refer to http://sheffieldml.github.io/GPyOpt/

Not sure why it is not showing the docstring and Args here below…?

class olympus.planners.Slsqp(goal='minimize', disp=False, eps=1.4901161193847656e-08, ftol=1e-06, maxiter=15000, init_guess=None, init_guess_method='random', init_guess_seed=None)[source]

Sequential Least SQuares Programming (SLSQP) optimizers. SciPy implementation.

Parameters

goal (str) – The optimization goal, either ‘minimize’ or ‘maximize’. Default is ‘minimize’.
disp (bool) – Set to True to print convergence messages. If False, verbosity is ignored and set to 0.
eps (float) – Step size used for numerical approximation of the Jacobian.
ftol (float) – Precision goal for the value of f in the stopping criterion.
maxiter (int) – Maximum number of iterations.
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.
`set_param_space`(param_space)	Defines the parameter space over which the planner will search.

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

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)

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

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.

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.