Method for selecting points that can be used as start points for multistart optimization. All methods have the form
method(**kwargs) -> startpoints
where the kwargs can/should include the following parameters, which are passed by pypesto:
- n_starts: int
- Number of points to generate.
- lb, ub: ndarray
- Lower and upper bound, may for most methods not contain nan or inf values.
- x_guesses: ndarray, shape=(g, dim), optional
- Parameter guesses by the user, where g denotes the number of guesses. Note that these are only possibly taken as reference points to generate new start points (e.g. to maximize some distance) depending on the method, but regardless of g, there are always n_starts points generated and returned.
- objective: pypesto.Objective, optional
- The objective can be used to evaluate the goodness of start points.
- max_n_fval: int, optional
- The maximum number of evaluations of the objective function allowed.
Generate uniform points.
Generate latin hypercube points.
assign_startpoints(n_starts, startpoint_method, problem, options)¶