/usr/include/trilinos/ROL_MeanVarianceQuadrangle.hpp is in libtrilinos-rol-dev 12.12.1-5.
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// Rapid Optimization Library (ROL) Package
// Copyright (2014) Sandia Corporation
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#ifndef ROL_MEANVARIANCEQUAD_HPP
#define ROL_MEANVARIANCEQUAD_HPP
#include "ROL_ExpectationQuad.hpp"
/** @ingroup risk_group
\class ROL::MeanVarianceQuadrangle
\brief Provides an interface for the mean plus variance risk measure
using the expectation risk quadrangle.
The mean plus variances risk measure is
\f[
\mathcal{R}(X) = \mathbb{E}[X]
+ c \mathbb{E}[|X-\mathbb{E}[X]|^2]
\f]
where \f$c \ge 0\f$.
\f$\mathcal{R}\f$ is law-invariant, but not coherent since it
violates positive homogeneity. The associated scalar regret
function is
\f[
v(x) = c x^2 + x
\f]
and the mean-plus-variance risk measure is computed as
\f[
\mathcal{R}(X) = \inf_{t\in\mathbb{R}}\left\{
t + \mathbb{E}[v(X-t)] \right\}.
\f]
ROL implements this by augmenting the optimization vector \f$x_0\f$ with
the parameter \f$t\f$, then minimizes jointly for \f$(x_0,t)\f$.
*/
namespace ROL {
template<class Real>
class MeanVarianceQuadrangle : public ExpectationQuad<Real> {
private:
Real coeff_;
void checkInputs(void) const {
Real zero(0);
TEUCHOS_TEST_FOR_EXCEPTION((coeff_ <= zero), std::invalid_argument,
">>> ERROR (ROL::MeanVarianceQuadrangle): Coefficient must be positive!");
}
public:
/** \brief Constructor.
@param[in] coeff is the weight for variance term
*/
MeanVarianceQuadrangle(const Real coeff = 1)
: ExpectationQuad<Real>(), coeff_(coeff) {
checkInputs();
}
/** \brief Constructor.
@param[in] parlist is a parameter list specifying inputs
parlist should contain sublists "SOL"->"Risk Measure"->"Mean-Variance Quadrangle" and
within the "Mean-Variance Quadrangle" sublist should have the following parameters
\li "Coefficient" (array of positive scalars).
*/
MeanVarianceQuadrangle(Teuchos::ParameterList &parlist)
: ExpectationQuad<Real>() {
Teuchos::ParameterList &list
= parlist.sublist("SOL").sublist("Risk Measure").sublist("Mean-Variance Quadrangle");
coeff_ = list.get<Real>("Coefficient");
checkInputs();
}
Real error(Real x, int deriv = 0) {
Real err(0), two(2);
if (deriv==0) {
err = coeff_*x*x;
}
else if (deriv==1) {
err = two*coeff_*x;
}
else {
err = two*coeff_;
}
return err;
}
Real regret(Real x, int deriv = 0) {
Real zero(0), one(1);
Real X = ((deriv==0) ? x : ((deriv==1) ? one : zero));
Real reg = error(x,deriv) + X;
return reg;
}
};
}
#endif
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