Class OptimizerObjective

Class Documentation

class OptimizerObjective

Base class for objective functions for Optimizer.

Public Functions

static PTR(Prior)
OptimizerObjective(int dataSize_, int parameterSize_)

Base class constructor; must be called by all subclasses.

void fillObjectiveValueGrid(ndarray::Array<Scalar const, 2, 1> const &parameters, ndarray::Array<Scalar, 1, 1> const &output) const

Evaluate the Objective on a 1-d grid.

This delegates to computeResiduals, and hence need not be reimplemented by derived classes. It is intended mostly for diagnostic purposes; when investigating the behavior of the optimizer, it is frequently valuable to plot its path against the gridded objective function.

Frequently, the arguments to this function will be flattened views into higher dimensional arrays, allowing it to be used to construct N-d grids despite taking only a 1-d sequence of grid points.

Parameters

  • [in] parameters: An array with shape (gridSize, parameterSize), specifying the parameter vector at points on the grid.

  • [out] output: Output array for objective values with shape (gridSize). Must be allocated, but need not be initialized.

virtual void computeResiduals(ndarray::Array<Scalar const, 1, 1> const &parameters, ndarray::Array<Scalar, 1, 1> const &residuals) const = 0

Evaluate the residuals of the model for a given parameter vector.

Parameters

  • [in] parameters: An array of parameters with shape (parameterSize).

  • [out] residuals: Output array that will contain (model - data) on return. Must be allocated to shape (dataSize), but need not be initialized.

virtual bool differentiateResiduals(ndarray::Array<Scalar const, 1, 1> const &parameters, ndarray::Array<Scalar, 2, -2> const &derivatives) const

Evaluate analytic derivatives of the model or signal that they are not available.

Subclasses that provide analytic derivatives should implement this method and return true. Subclasses that do not should return false to indicate to the optimizer that numeric derivatives must be used instead. Subclasses that can only sometimes compute analytic derivatives are not supported.

Parameters

  • [in] parameters: An array of parameters with shape (parameterSize).

  • [out] derivatives: Output array that will contain d(model - data)/d(parameters) on return. Must be allocated to shape (dataSize, parameterSize), but need not be initialized.

virtual bool hasPrior() const

Return true if the Objective has a Bayesian prior as well as a likelihood.

The default implementation returns false.

virtual Scalar computePrior(ndarray::Array<Scalar const, 1, 1> const &parameters) const

Compute the value of the Bayesian prior for the given parameter vector.

The default implementation simply returns 1.0 (appropriate for an unnormalized constant prior, which is mathematically equivalent to no prior).

virtual void differentiatePrior(ndarray::Array<Scalar const, 1, 1> const &parameters, ndarray::Array<Scalar, 1, 1> const &gradient, ndarray::Array<Scalar, 2, 1> const &hessian) const

Compute the first and second derivatives of the Bayesian prior with respect to the parameters.

The default implementation simply sets the output arrays to 0.0 (appropriate for an constant prior, which is mathematically equivalent to no prior).

Parameters

  • [in] parameters: An array of parameters with shape (parameterSize).

  • [out] gradient: First derivative of the prior with respect to the parameters. Must be allocated to shape (parameterSize), but need not be initialized.

  • [out] hessian: Second derivative of the prior with respect to the parameters. Must be allocated to shape (parameterSize, parameterSize), but need not be initialized. This is a symmetric matrix, and only the lower triangle is used.

virtual ~OptimizerObjective()

Public Members

int const dataSize
int const parameterSize

Public Static Functions

static PTR(OptimizerObjective)

Return a concrete Objective object built from a Likelihood and Prior.

Most fitting problems that can be formulated in terms of (multi-shapelet) Models, Likelihoods, and Priors can just use this Objective. The returned Objective relies on numerical derivatives, however, so simple problems where analytic derivatives are easy to implement may merit a custom OptimizerObjective.