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Dakota
Version 6.19
Explore and Predict with Confidence
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Public Member Functions | |
| DakotaROLIneqConstraintsGrad (Model &model) | |
| Constructor. More... | |
| virtual | ~DakotaROLIneqConstraintsGrad () |
| Destructor. | |
| void | applyJacobian (std::vector< Real > &jv, const std::vector< Real > &v, const std::vector< Real > &x, Real &tol) override |
| Function to return the result of applying the constraint gradient on an arbitrary vector to ROL. | |
| void | applyAdjointJacobian (std::vector< Real > &ajv, const std::vector< Real > &v, const std::vector< Real > &x, Real &tol) override |
| Function to return the result of applying the constraint adjoint to an arbitrary vector to ROL. | |
Public Member Functions inherited from DakotaROLIneqConstraints | |
| DakotaROLIneqConstraints (Model &model) | |
| Constructor. More... | |
| void | value (std::vector< Real > &c, const std::vector< Real > &x, Real &tol) override |
| Function to return the constraint value to ROL. | |
Additional Inherited Members | |
Protected Attributes inherited from DakotaROLIneqConstraints | |
| Model & | dakotaModel |
| Dakota problem data provided by user. | |
| bool | haveNlnConst |
| Whether or not problem has nonlinear inequality constraints. | |
DakotaROLIneqConstraintsGrad is derived from DakotaROLIneqConstraints. It implements overrides of ROL member functions to provide a Dakota-specific application of the inequality constraint Jacobian to a vector. This separate class is needed to allow for the option of utilizing ROL's finite-differenced gradients
| DakotaROLIneqConstraintsGrad | ( | Model & | model | ) |
Constructor.
Implementation of the DakotaROLIneqConstraintsGrad class.