ROL
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Provides an interface for a smooth approximation of the conditional value-at-risk. More...
#include <ROL_MoreauYosidaCVaR.hpp>
Public Member Functions | |
MoreauYosidaCVaR (Real prob, Real eps) | |
Constructor. More... | |
MoreauYosidaCVaR (ROL::ParameterList &parlist) | |
Constructor. More... | |
Real | error (Real x, int deriv=0) |
Evaluate the scalar error function at x. More... | |
Real | regret (Real x, int deriv=0) |
Evaluate the scalar regret function at x. More... | |
void | check (void) |
Run default derivative tests for the scalar regret function. More... | |
Public Member Functions inherited from ROL::ExpectationQuad< Real > | |
virtual | ~ExpectationQuad (void) |
ExpectationQuad (void) | |
Private Member Functions | |
void | parseParameterList (ROL::ParameterList &parlist) |
void | checkInputs (void) const |
void | setParameters (void) |
Private Attributes | |
Real | prob_ |
Real | eps_ |
Real | omp_ |
Real | ub_ |
Provides an interface for a smooth approximation of the conditional value-at-risk.
The conditional value-at-risk (also called the average value-at-risk or the expected shortfall) with confidence level \(0\le \beta < 1\) is
\[ \mathcal{R}(X) = \inf_{t\in\mathbb{R}} \left\{ t + \frac{1}{1-\beta} \mathbb{E}\left[(X-t)_+\right] \right\} \]
where \((x)_+ = \max\{0,x\}\). If the distribution of \(X\) is continuous, then \(\mathcal{R}\) is the conditional expectation of \(X\) exceeding the \(\beta\)-quantile of \(X\) and the optimal \(t\) is the \(\beta\)-quantile. Additionally, \(\mathcal{R}\) is a law-invariant coherent risk measure.
The conditional value-at-risk is in general not smooth due to \((\cdot)_+\). One approach to smoothing the conditional-value-at-risk is to regularize its biconjugate form. That is, since \(\mathcal{R}\) is coherent, we have that
\[ \mathcal{R}(X) = \sup_{\vartheta\in\mathfrak{A}}\mathbb{E}[\vartheta X] \]
where \(\mathfrak{A}\) is the effective domain of the conjugate of \(\mathcal{R}\), i.e.,
\[ \mathfrak{A} = \mathrm{dom}\,\mathcal{R}^* = \{\vartheta\in\mathcal{X}^*\,:\, \mathcal{R}^*(\vartheta) < \infty\} \]
where \(\mathcal{R}^*\) denotes the Legendre-Fenchel transformation of \(\mathcal{R}\). This risk measure implements the penalized conditional value-at-risk
\[ \mathcal{R}(X) = \sup_{\vartheta\in\mathfrak{A}} \left\{\mathbb{E}[\vartheta X] - \frac{\gamma}{2}\mathbb{E}[(\vartheta-1)^2]\right\} \]
for \(\gamma > 0\). This is implemented using the expectation risk quadrangle interface. Thus, we represent \(\mathcal{R}\) as
\[ \mathcal{R}(X) = \inf_{t\in\mathbb{R}} \left\{ t + \mathbb{E}\left[v(X-t)\right] \right\} \]
for an appropriately defined scalar regret function \(v\). ROL implements this by augmenting the optimization vector \(x_0\) with the parameter \(t\), then minimizes jointly for \((x_0,t)\).
The conditional value-at-risk (also called the average value-at-risk or the expected shortfall) with confidence level \(0\le \beta < 1\) is
\[ \mathcal{R}(X) = \inf_{t\in\mathbb{R}} \left\{ t + \frac{1}{1-\beta} \mathbb{E}\left[(X-t)_+\right] \right\} \]
where \((x)_+ = \max\{0,x\}\). If the distribution of \(X\) is continuous, then \(\mathcal{R}\) is the conditional expectation of \(X\) exceeding the \(\beta\)-quantile of \(X\) and the optimal \(t\) is the \(\beta\)-quantile. Additionally, \(\mathcal{R}\) is a law-invariant coherent risk measure.
The conditional value-at-risk is in general not smooth due to \((\cdot)_+\). One approach to smoothing the conditional-value-at-risk is to regularize its biconjugate form. That is, since \(\mathcal{R}\) is coherent, we have that
\[ \mathcal{R}(X) = \sup_{\vartheta\in\mathfrak{A}}\mathbb{E}[\vartheta X] \]
where \(\mathfrak{A}\) is the effective domain of the conjugate of \(\mathcal{R}\), i.e.,
\[ \mathfrak{A} = \mathrm{dom}\,\mathcal{R}^* = \{\vartheta\in\mathcal{X}^*\,:\, \mathcal{R}^*(\vartheta) < \infty\} \]
where \(\mathcal{R}^*\) denotes the Legendre-Fenchel transformation of \(\mathcal{R}\). This risk measure implements the penalized conditional value-at-risk
\[ \mathcal{R}(X) = \sup_{\vartheta\in\mathfrak{A}} \left\{\mathbb{E}[\vartheta X] - \frac{\gamma}{2}\mathbb{E}[\vartheta^2]\right\} \]
for \(\gamma > 0\). This is implemented using the expectation risk quadrangle interface. Thus, we represent \(\mathcal{R}\) as
\[ \mathcal{R}(X) = \inf_{t\in\mathbb{R}} \left\{ t + \mathbb{E}\left[v(X-t)\right] \right\} \]
for an appropriately defined scalar regret function \(v\). ROL implements this by augmenting the optimization vector \(x_0\) with the parameter \(t\), then minimizes jointly for \((x_0,t)\).
Definition at line 71 of file ROL_MoreauYosidaCVaR.hpp.
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inline |
Constructor.
[in] | prob | is the confidence level |
[in] | eps | is the regularization parameter |
Definition at line 119 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::checkInputs(), and ROL::MoreauYosidaCVaR< Real >::setParameters().
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inline |
Constructor.
[in] | parlist | is a parameter list specifying inputs |
parlist should contain sublists "SOL"->"Risk Measure"->"Moreau-Yosida CVaR" and within the "Moreau-Yosida CVaR" sublist should have the following parameters
Definition at line 134 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::checkInputs(), ROL::MoreauYosidaCVaR< Real >::parseParameterList(), and ROL::MoreauYosidaCVaR< Real >::setParameters().
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inlineprivate |
Definition at line 80 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::eps_, and ROL::MoreauYosidaCVaR< Real >::prob_.
Referenced by ROL::MoreauYosidaCVaR< Real >::MoreauYosidaCVaR().
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inlineprivate |
Definition at line 99 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::eps_, ROL::MoreauYosidaCVaR< Real >::prob_, and zero.
Referenced by ROL::MoreauYosidaCVaR< Real >::MoreauYosidaCVaR().
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inlineprivate |
Definition at line 107 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::eps_, ROL::MoreauYosidaCVaR< Real >::omp_, ROL::MoreauYosidaCVaR< Real >::prob_, and ROL::MoreauYosidaCVaR< Real >::ub_.
Referenced by ROL::MoreauYosidaCVaR< Real >::MoreauYosidaCVaR().
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inlinevirtual |
Evaluate the scalar error function at x.
[in] | x | is the scalar input |
[in] | deriv | is the derivative order |
This function returns \(e(x)\) or a derivative of \(e(x)\).
Reimplemented from ROL::ExpectationQuad< Real >.
Definition at line 141 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::regret(), and zero.
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inlinevirtual |
Evaluate the scalar regret function at x.
[in] | x | is the scalar input |
[in] | deriv | is the derivative order |
This function returns \(v(x)\) or a derivative of \(v(x)\).
Implements ROL::ExpectationQuad< Real >.
Definition at line 147 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::MoreauYosidaCVaR< Real >::eps_, ROL::MoreauYosidaCVaR< Real >::omp_, ROL::MoreauYosidaCVaR< Real >::ub_, and zero.
Referenced by ROL::MoreauYosidaCVaR< Real >::check(), and ROL::MoreauYosidaCVaR< Real >::error().
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inlinevirtual |
Run default derivative tests for the scalar regret function.
Reimplemented from ROL::ExpectationQuad< Real >.
Definition at line 163 of file ROL_MoreauYosidaCVaR.hpp.
References ROL::ExpectationQuad< Real >::check(), ROL::MoreauYosidaCVaR< Real >::eps_, ROL::MoreauYosidaCVaR< Real >::regret(), and zero.
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private |
Definition at line 74 of file ROL_MoreauYosidaCVaR.hpp.
Referenced by ROL::MoreauYosidaCVaR< Real >::checkInputs(), ROL::MoreauYosidaCVaR< Real >::parseParameterList(), and ROL::MoreauYosidaCVaR< Real >::setParameters().
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private |
Definition at line 75 of file ROL_MoreauYosidaCVaR.hpp.
Referenced by ROL::MoreauYosidaCVaR< Real >::check(), ROL::MoreauYosidaCVaR< Real >::checkInputs(), ROL::MoreauYosidaCVaR< Real >::parseParameterList(), ROL::MoreauYosidaCVaR< Real >::regret(), and ROL::MoreauYosidaCVaR< Real >::setParameters().
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private |
Definition at line 77 of file ROL_MoreauYosidaCVaR.hpp.
Referenced by ROL::MoreauYosidaCVaR< Real >::regret(), and ROL::MoreauYosidaCVaR< Real >::setParameters().
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private |
Definition at line 78 of file ROL_MoreauYosidaCVaR.hpp.
Referenced by ROL::MoreauYosidaCVaR< Real >::regret(), and ROL::MoreauYosidaCVaR< Real >::setParameters().