pybop.optimisers.pints_optimisers

Classes

AdamW	Implements the AdamW optimisation algorithm in PyBOP.
CMAES	Adapter for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimiser in PINTS.
CuckooSearch	Adapter for the Cuckoo Search optimiser in PyBOP.
GradientDescent	Implements a simple gradient descent optimisation algorithm.
IRPropMin	Implements the iRpropMin optimisation algorithm.
NelderMead	Implements the Nelder-Mead downhill simplex method from PINTS.
PSO	Implements a particle swarm optimisation (PSO) algorithm.
RandomSearch	Adapter for the Random Search optimiser in PyBOP.
SNES	Implements the stochastic natural evolution strategy (SNES) optimisation algorithm.
XNES	Implements the Exponential Natural Evolution Strategy (XNES) optimiser from PINTS.

Module Contents

class pybop.optimisers.pints_optimisers.AdamW(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements the AdamW optimisation algorithm in PyBOP.

This class extends the AdamW optimiser, which is a variant of the Adam optimiser that incorporates weight decay. AdamW is designed to be more robust and stable for training deep neural networks, particularly when using larger learning rates.

Note that this optimiser does not support boundary constraints.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pybop.AdamWImpl

The PyBOP implementation this class is based on.

class pybop.optimisers.pints_optimisers.CMAES(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Adapter for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimiser in PINTS.

CMA-ES is an evolutionary algorithm for difficult non-linear non-convex optimisation problems. It adapts the covariance matrix of a multivariate normal distribution to capture the shape of the cost landscape.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.CMAES

PINTS implementation of CMA-ES algorithm.

class pybop.optimisers.pints_optimisers.CuckooSearch(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Adapter for the Cuckoo Search optimiser in PyBOP.

Cuckoo Search is a population-based optimisation algorithm inspired by the brood parasitism of some cuckoo species. It is designed to be simple, efficient, and robust, and is suitable for global optimisation problems.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pybop.CuckooSearchImpl

PyBOP implementation of Cuckoo Search algorithm.

class pybop.optimisers.pints_optimisers.GradientDescent(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements a simple gradient descent optimisation algorithm.

This class extends the gradient descent optimiser from the PINTS library, designed to minimize a scalar function of one or more variables.

Note that this optimiser does not support boundary constraints.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.GradientDescent

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.IRPropMin(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements the iRpropMin optimisation algorithm.

This class inherits from the PINTS IRPropMin class, which is an optimiser that uses resilient backpropagation with weight-backtracking. It is designed to handle problems with large plateaus, noisy gradients, and local minima.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.IRPropMin

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.NelderMead(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements the Nelder-Mead downhill simplex method from PINTS.

This is a deterministic local optimiser. In most update steps it performs either one evaluation, or two sequential evaluations, so that it will not typically benefit from parallelisation.

Note that this optimiser does not support boundary constraints.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.NelderMead

PINTS implementation of Nelder-Mead algorithm.

class pybop.optimisers.pints_optimisers.PSO(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements a particle swarm optimisation (PSO) algorithm.

This class extends the PSO optimiser from the PINTS library. PSO is a metaheuristic optimisation method inspired by the social behavior of birds flocking or fish schooling, suitable for global optimisation problems.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.PSO

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.RandomSearch(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Adapter for the Random Search optimiser in PyBOP.

Random Search is a simple optimisation algorithm that samples parameter sets randomly within the given boundaries and identifies the best solution based on fitness.

This optimiser has been implemented for benchmarking and comparisons, convergence will be better with one of other optimisers in the majority of cases.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  population_sizeint

  Number of solutions to evaluate per iteration.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pybop.RandomSearchImpl

PyBOP implementation of Random Search algorithm.

class pybop.optimisers.pints_optimisers.SNES(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements the stochastic natural evolution strategy (SNES) optimisation algorithm.

Inheriting from the PINTS SNES class, this optimiser is an evolutionary algorithm that evolves a probability distribution on the parameter space, guiding the search for the optimum based on the natural gradient of expected fitness.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.SNES

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.XNES(cost, max_iterations: int = None, min_iterations: int = 2, max_unchanged_iterations: int = 15, multistart: int = 1, parallel: bool = False, **optimiser_kwargs)

Bases: pybop.BasePintsOptimiser

Implements the Exponential Natural Evolution Strategy (XNES) optimiser from PINTS.

XNES is an evolutionary algorithm that samples from a multivariate normal distribution, which is updated iteratively to fit the distribution of successful solutions.

Parameters:

• cost (callable) – The cost function to be minimized.

• max_iterations (int, optional) – Maximum number of iterations for the optimisation.

• min_iterations (int, optional (default=2)) – Minimum number of iterations before termination.

• max_unchanged_iterations (int, optional (default=15)) – Maximum number of iterations without improvement before termination.

• multistart (int, optional (default=1)) – Number of optimiser restarts from randomly sample position. These positions are sampled from the priors.

• parallel (bool, optional (default=False)) – Whether to run the optimisation in parallel.

• **optimiser_kwargs (optional) –

  Valid PINTS option keys and their values, for example: x0 : array_like

  Initial position from which optimisation will start.

  sigma0float

  Initial step size or standard deviation depending on the optimiser.

  boundsdict

  A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters.

  use_f_guessedbool

  Whether to return the guessed function values.

  absolute_tolerancefloat

  Absolute tolerance for convergence checking.

  relative_tolerancefloat

  Relative tolerance for convergence checking.

  max_evaluationsint

  Maximum number of function evaluations.

  thresholdfloat

  Threshold value for early termination.

See also

pints.XNES

PINTS implementation of XNES algorithm.