pybop.optimisers.pints_optimisers#

Classes#

Adam

Implements the Adam optimization algorithm.

CMAES

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

GradientDescent

Implements a simple gradient descent optimization algorithm.

IRPropMin

Implements the iRpropMin optimization algorithm.

PSO

Implements a particle swarm optimization (PSO) algorithm.

SNES

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

XNES

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

Module Contents#

class pybop.optimisers.pints_optimisers.Adam(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.Adam

Implements the Adam optimization algorithm.

This class extends the Adam optimiser from the PINTS library, which combines ideas from RMSProp and Stochastic Gradient Descent with momentum. Note that this optimiser does not support boundary constraints.

Parameters:

  • x0 (array_like) – Initial position from which optimization will start.

  • sigma0 (float, optional) – Initial step size (default is 0.1).

  • bounds (sequence or Bounds, optional) – Ignored by this optimiser, provided for API consistency.

See also

pints.Adam

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.CMAES(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.CMAES

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 optimization problems. It adapts the covariance matrix of a multivariate normal distribution to capture the shape of the cost landscape.

Parameters:

  • x0 (array_like) – The initial parameter vector to optimize.

  • sigma0 (float, optional) – Initial standard deviation of the sampling distribution, defaults to 0.1.

  • bounds (dict, optional) – A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters. If None, no bounds are enforced.

See also

pints.CMAES

PINTS implementation of CMA-ES algorithm.

class pybop.optimisers.pints_optimisers.GradientDescent(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.GradientDescent

Implements a simple gradient descent optimization 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:

  • x0 (array_like) – Initial position from which optimization will start.

  • sigma0 (float, optional) – Initial step size (default is 0.1).

  • bounds (sequence or Bounds, optional) – Ignored by this optimiser, provided for API consistency.

See also

pints.GradientDescent

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.IRPropMin(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.IRPropMin

Implements the iRpropMin optimization 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:

  • x0 (array_like) – Initial position from which optimization will start.

  • sigma0 (float, optional) – Initial step size (default is 0.1).

  • bounds (dict, optional) – Lower and upper bounds for each optimization parameter.

See also

pints.IRPropMin

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.PSO(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.PSO

Implements a particle swarm optimization (PSO) algorithm.

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

Parameters:

  • x0 (array_like) – Initial positions of particles, which the optimization will use.

  • sigma0 (float, optional) – Spread of the initial particle positions (default is 0.1).

  • bounds (dict, optional) – Lower and upper bounds for each optimization parameter.

See also

pints.PSO

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.SNES(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.SNES

Implements the stochastic natural evolution strategy (SNES) optimization 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:

  • x0 (array_like) – Initial position from which optimization will start.

  • sigma0 (float, optional) – Initial standard deviation of the sampling distribution, defaults to 0.1.

  • bounds (dict, optional) – Lower and upper bounds for each optimization parameter.

See also

pints.SNES

The PINTS implementation this class is based on.

class pybop.optimisers.pints_optimisers.XNES(x0, sigma0=0.1, bounds=None)[source]#

Bases: pints.XNES

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:

  • x0 (array_like) – The initial parameter vector to optimize.

  • sigma0 (float, optional) – Initial standard deviation of the sampling distribution, defaults to 0.1.

  • bounds (dict, optional) – A dictionary with ‘lower’ and ‘upper’ keys containing arrays for lower and upper bounds on the parameters. If None, no bounds are enforced.

See also

pints.XNES

PINTS implementation of XNES algorithm.

On this page