Optimize

Multistart optimization with support for various optimizers.

class pypesto.optimize.CmaesOptimizer(par_sigma0: float = 0.25, options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Global optimization using cma-es.

Package homepage: https://pypi.org/project/cma-es/

__init__(par_sigma0: float = 0.25, options: Optional[Dict] = None)

Initialize.

Parameters

• par_sigma0 – scalar, initial standard deviation in each coordinate. par_sigma0 should be about 1/4th of the search domain width (where the optimum is to be expected)

• options – Optimizer options that are directly passed on to cma.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.DlibOptimizer(options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Use the Dlib toolbox for optimization.

__init__(options: Optional[Dict] = None)

Initialize base class.

get_default_options()

Create default options specific for the optimizer.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.FidesOptimizer(hessian_update: Optional[None] = 'default', options: Optional[Dict] = None, verbose: Optional[int] = 20)

Bases: pypesto.optimize.optimizer.Optimizer

Global/Local optimization using the trust region optimizer fides.

Package Homepage: https://fides-optimizer.readthedocs.io/en/latest

__init__(hessian_update: Optional[None] = 'default', options: Optional[Dict] = None, verbose: Optional[int] = 20)

Initialize.

Parameters

• options – Optimizer options.

• hessian_update – Hessian update strategy. If this is None, a hybrid approximation that switches from the problem.objective provided Hessian ( approximation) to a BFGS approximation will be used.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.IpoptOptimizer(options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Use IpOpt (https://pypi.org/project/ipopt/) for optimization.

__init__(options: Optional[Dict] = None)

Initialize.

Parameters

options – Options are directly passed on to cyipopt.minimize_ipopt.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.NLoptOptimizer(method=None, local_method=None, options: Optional[Dict] = None, local_options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Global/Local optimization using NLopt.

Package homepage: https://nlopt.readthedocs.io/en/latest/

__init__(method=None, local_method=None, options: Optional[Dict] = None, local_options: Optional[Dict] = None)

Initialize.

Parameters

• method – Local or global Optimizer to use for minimization.

• local_method – Local method to use in combination with the global optimizer ( for the MLSL family of solvers) or to solve a subproblem (for the AUGLAG family of solvers)

• options – Optimizer options. scipy option maxiter is automatically transformed into maxeval and takes precedence.

• local_options – Optimizer options for the local method

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.OptimizeOptions(allow_failed_starts: bool = True, report_sres: bool = True, report_hess: bool = True, history_beats_optimizer: bool = True)

Bases: dict

Options for the multistart optimization.

Parameters

• allow_failed_starts – Flag indicating whether we tolerate that exceptions are thrown during the minimization process.

• report_sres – Flag indicating whether sres will be stored in the results object. Deactivating this option will improve memory consumption for large scale problems.

• report_hess – Flag indicating whether hess will be stored in the results object. Deactivating this option will improve memory consumption for large scale problems.

• history_beats_optimizer – Whether the optimal value recorded by pyPESTO in the history has priority over the optimal value reported by the optimizer (True) or not (False).

__init__(allow_failed_starts: bool = True, report_sres: bool = True, report_hess: bool = True, history_beats_optimizer: bool = True)

Initialize self. See help(type(self)) for accurate signature.

static assert_instance(maybe_options: Union[pypesto.optimize.options.OptimizeOptions, Dict]) → pypesto.optimize.options.OptimizeOptions

Return a valid options object.

Parameters

maybe_options (OptimizeOptions or dict) –

class pypesto.optimize.Optimizer

Bases: abc.ABC

Optimizer base class, not functional on its own.

An optimizer takes a problem, and possibly a start point, and then performs an optimization. It returns an OptimizerResult.

__init__()

Initialize base class.

get_default_options()

Create default options specific for the optimizer.

abstract is_least_squares()

Check whether optimizer is a least squares optimizer.

abstract minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.PyswarmOptimizer(options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Global optimization using pyswarm.

__init__(options: Optional[Dict] = None)

Initialize base class.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.PyswarmsOptimizer(par_popsize: float = 10, options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Global optimization using pyswarms.

Package homepage: https://pyswarms.readthedocs.io/en/latest/index.html

Parameters

• par_popsize – number of particles in the swarm, default value 10

• options – Optimizer options that are directly passed on to pyswarms. c1: cognitive parameter c2: social parameter w: inertia parameter Default values are (c1,c2,w) = (0.5, 0.3, 0.9)

Examples

Arguments that can be passed to options:

maxiter:

used to calculate the maximal number of funcion evaluations. Default: 1000

__init__(par_popsize: float = 10, options: Optional[Dict] = None)

Initialize base class.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.ScipyDifferentialEvolutionOptimizer(options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Global optimization using scipy’s differential evolution optimizer.

Package homepage: https://docs.scipy.org/doc/scipy/reference/generated /scipy.optimize.differential_evolution.html

Parameters

options – Optimizer options that are directly passed on to scipy’s optimizer.

Examples

Arguments that can be passed to options:

maxiter:

used to calculate the maximal number of funcion evaluations by maxfevals = (maxiter + 1) * popsize * len(x) Default: 100

popsize:

population size, default value 15

__init__(options: Optional[Dict] = None)

Initialize base class.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

class pypesto.optimize.ScipyOptimizer(method: str = 'L-BFGS-B', tol: Optional[float] = None, options: Optional[Dict] = None)

Bases: pypesto.optimize.optimizer.Optimizer

Use the SciPy optimizers.

Find details on the optimizer and configuration options at: https://docs.scipy.org/doc/scipy/reference/generated/scipy. optimize.minimize.html#scipy.optimize.minimize

__init__(method: str = 'L-BFGS-B', tol: Optional[float] = None, options: Optional[Dict] = None)

Initialize base class.

get_default_options()

Create default options specific for the optimizer.

is_least_squares()

Check whether optimizer is a least squares optimizer.

minimize(problem: pypesto.problem.Problem, x0: numpy.ndarray, id: str, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None)

pypesto.optimize.fill_result_from_history(result: pypesto.result.optimize.OptimizerResult, optimizer_history: pypesto.objective.history.OptimizerHistory, optimize_options: Optional[pypesto.optimize.options.OptimizeOptions] = None) → pypesto.result.optimize.OptimizerResult

Overwrite some values in the result object with values in the history.

Parameters

• result (Result as reported from the used optimizer.) –

• optimizer_history (History of function values recorded by the objective.) –

• optimize_options (Options on e.g. how to override.) –

Returns

result

Return type

The in-place modified result.

pypesto.optimize.minimize(problem: pypesto.problem.Problem, optimizer: Optional[pypesto.optimize.optimizer.Optimizer] = None, n_starts: int = 100, ids: Optional[Iterable[str]] = None, startpoint_method: Optional[Union[pypesto.startpoint.base.StartpointMethod, Callable, bool]] = None, result: Optional[pypesto.result.result.Result] = None, engine: Optional[pypesto.engine.base.Engine] = None, progress_bar: bool = True, options: Optional[pypesto.optimize.options.OptimizeOptions] = None, history_options: Optional[pypesto.objective.history.HistoryOptions] = None, filename: Optional[Union[str, Callable]] = 'Auto', overwrite: bool = False) → pypesto.result.result.Result

Do multistart optimization.

Parameters

• problem – The problem to be solved.

• optimizer – The optimizer to be used n_starts times.

• n_starts – Number of starts of the optimizer.

• ids – Ids assigned to the startpoints.

• startpoint_method – Method for how to choose start points. False means the optimizer does not require start points, e.g. for the ‘PyswarmOptimizer’.

• result – A result object to append the optimization results to. For example, one might append more runs to a previous optimization. If None, a new object is created.

• engine – Parallelization engine. Defaults to sequential execution on a SingleCoreEngine.

• progress_bar – Whether to display a progress bar.

• options – Various options applied to the multistart optimization.

• history_options – Optimizer history options.

• filename – Name of the hdf5 file, where the result will be saved. Default is “Auto”, in which case it will automatically generate a file named year_month_day_optimization_result.hdf5. Deactivate saving by setting filename to None. Optionally a method, see docs for pypesto.store.auto.autosave.

• overwrite – Whether to overwrite result/optimization in the autosave file if it already exists.

Returns

Result object containing the results of all multistarts in result.optimize_result.

Return type

result

pypesto.optimize.optimization_result_from_history(filename: str, problem: pypesto.problem.Problem) → pypesto.result.result.Result

Convert a saved hdf5 History to an optimization result.

Used for interrupted optimization runs.

Parameters

• filename – The name of the file in which the information are stored.

• problem – Problem, needed to identify what parameters to accept.

Returns

• A result object in which the optimization result is constructed from

• history. But missing “Time, Message and Exitflag” keys.

pypesto.optimize.read_result_from_file(problem: pypesto.problem.Problem, history_options: pypesto.objective.history.HistoryOptions, identifier: str) → pypesto.result.optimize.OptimizerResult

Fill an OptimizerResult from history.

Parameters

• problem – The problem to find optimal parameters for.

• identifier – Multistart id.

• history_options – Optimizer history options.

pypesto.optimize.read_results_from_file(problem: pypesto.problem.Problem, history_options: pypesto.objective.history.HistoryOptions, n_starts: int) → pypesto.result.result.Result

Fill a Result from a set of histories.

Parameters

• problem – The problem to find optimal parameters for.

• n_starts – Number of performed multistarts.

• history_options – Optimizer history options.