Uses of Class
function.VariableDomainScalarFunction

Packages that use VariableDomainScalarFunction

diviner	Provides functionality for measuring the level of promise of a design candidates.
function	Provides classes for scalar and vector-valued functions that operate on multi- dimensional double-valued data.
model	Provides classes that handle various kinds of approximation models, data fits, and so on.

Uses of VariableDomainScalarFunction in diviner

Subclasses of VariableDomainScalarFunction in diviner

class	BasicBoundConstrainedDiviner Implements basic functionality for BoundConstrainedDiviner.
class	BasicInequalityConstrainedDiviner Basic diviner for inequality-constrained problems.
class	ConstrainedEIDiviner Constrained version of the EIDiviner.
class	ConstrainedImprovementDiviner ImprovementDiviner for constrained problems.
class	ConstrainedMultiMetricDiviner Multi-metric diviner for constrained problems.
class	EIDiviner Implements an expected improvement BoundConstrainedDiviner.
class	ImprovementDiviner Focuses on various ways of divining merit using the posterior distribution at a point and some computation of improvement.
class	MultiMetricDiviner Uses multiple figures of merit to compute 'goodness' of a point for sampling.
class	PIDiviner Implements a diviner based on probability of improvement.
class	RiskDiviner Another way of trading performance and risk: quantify the risk as the probability of not achieving a given performance.
class	RiskPerfTradeDiviner BoundConstrainedDiviner that returns mean - k*stdDev.

Uses of VariableDomainScalarFunction in function

Subclasses of VariableDomainScalarFunction in function

class	Rosenbrock Extended Rosenbrock function, defined for an even number of input dimensions.
class	RosenbrockLoFi Implements the Rosenbrock function minus one quadratic term.

Uses of VariableDomainScalarFunction in model

Subclasses of VariableDomainScalarFunction in model

class	BasicApproxModel Basic implementation of the methods in ApproxModel.
class	BasicTwoFidelityModel Basic two-fidelity approximation model, using an additive surrogate correction.
class	GPRegression Creation process: Set tau (compulsory), set prior mean and covariance, set bounds, init().
class	PenaltyFunctionModel Computes mean and variance for an external penalty function augmented Lagrangian.