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Dakota
Version 6.2
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Derived approximation class for first- or second-order Taylor series (a local approximation). More...
Public Member Functions | |
TaylorApproximation () | |
default constructor | |
TaylorApproximation (ProblemDescDB &problem_db, const SharedApproxData &shared_data) | |
standard constructor | |
TaylorApproximation (const SharedApproxData &shared_data) | |
alternate constructor | |
~TaylorApproximation () | |
destructor | |
Protected Member Functions | |
int | min_coefficients () const |
return the minimum number of samples (unknowns) required to build the derived class approximation type in numVars dimensions | |
void | build () |
builds the approximation from scratch | |
Real | value (const Variables &vars) |
retrieve the approximate function value for a given parameter vector | |
const RealVector & | gradient (const Variables &vars) |
retrieve the approximate function gradient for a given parameter vector | |
const RealSymMatrix & | hessian (const Variables &vars) |
retrieve the approximate function Hessian for a given parameter vector |
Derived approximation class for first- or second-order Taylor series (a local approximation).
The TaylorApproximation class provides a local approximation based on data from a single point in parameter space. It uses a zeroth-, first- or second-order Taylor series expansion: f(x) = f(x_c) for zeroth-order, plus grad(x_c)' (x - x_c) for first- and second-order, and plus (x - x_c)' Hess(x_c) (x - x_c) / 2 for second-order.
void build | ( | ) | [protected, virtual] |
builds the approximation from scratch
This is the common base class portion of the virtual fn and is insufficient on its own; derived implementations should explicitly invoke (or reimplement) this base class contribution.
Reimplemented from Approximation.
References Dakota::abort_handler(), Approximation::approxData, SharedApproxData::buildDataOrder, SharedApproxData::numVars, and Approximation::sharedDataRep.