paraqeet.optimizers.cmaes_optimizer.CMAEsOptimizer#

class paraqeet.optimizers.cmaes_optimizer.CMAEsOptimizer(measure, optimization_map, logger=None, callback=None)#

Bases: Optimizer

Wrapper for the pycma implementation of CMA-Es.

The pycmi implementation has the following custom options for optimization:

Parameters:

noise (float) – Artificial noise added to a function evaluation.
init_point (boolean) – Force the use of the initial point in the first generation.
spread (float) – Adjust the parameter spread of the first generation cloud.
stop_at_convergence (int) – Custom stopping condition. Stop if the cloud shrunk for this number of generations.
stop_at_sigma (float) – Custom stopping condition. Stop if the cloud shrunk to this standard deviation.
measure (NormalizableMeasurement)
optimization_map (OptimizationMap)
logger (Logger | None)

Parameters:

measure (Measurement) – Represents any observable and the process of measurement itself.
optimization_map (OptimizationMap) – Optimizable interface for all parameters considered in optimization.
logger (FileLogger | None, default=None) – The file logger object.
callback – Callback function for optimization.

__init__(measure, optimization_map, logger=None, callback=None)#

Parameters:

measure (NormalizableMeasurement)
optimization_map (OptimizationMap)
logger (Logger | None)

Methods

`__init__`(measure, optimization_map[, ...])
`optimize`(times)	Optimize the system via the CMA-Es optimizer.

Attributes

`callback`	Returns the current callback function.
`logger`	Returns the current logger that is being used by this optimizer, or None if no logger was set yet.
`optimization_map`	Return the optimization map that this optimizer uses.
`options`	Get options from the system.

property callback: Callable | None#: Returns the current callback function.

property logger: Logger | None#: Returns the current logger that is being used by this optimizer, or None if no logger was set yet.

property optimization_map: OptimizationMap#

Return the optimization map that this optimizer uses.

Parameters that can be optimized need to be added to this map.

Returns:: Returns the optimization map that this optimizer uses.
Return type:: paraqeet.optimization_map

optimize(times)#

Optimize the system via the CMA-Es optimizer.

Performs the actual optimization via the following custom options:

noise: float: Artificial noise added to a function evaluation.
init_point: boolean: Force the use of the initial point in the first generation.
spread: float: Adjust the parameter spread of the first generation cloud.
stop_at_convergenceint: Custom stopping condition. Stop if the cloud shrunk for this number of generations.
stop_at_sigma: float: Custom stopping condition. Stop if the cloud shrunk to this standard deviation.

Note - If input `times` is a float, then the start time of propagation is implicitly assumed to be zero. For an array of times, the first time point is the start time.

Returns:: Result of optimization via the OptimizationResult object. (status, value, iterations and the raw result)
Return type:: OptimizationResult
Parameters:: times (Array | float)

property options: dict#: Get options from the system.