Line data    Source code

       1             : #pragma once
       2             : 
       3             : #include "Functional.h"
       4             : 
       5             : namespace elsa
       6             : {
       7             :     /**
       8             :      * @brief Class representing the l0 pseudo-norm functional.
       9             :      *
      10             :      * @author Andi Braimllari - initial code
      11             :      *
      12             :      * @tparam data_t data type for the domain of the residual of the functional, defaulting to
      13             :      * real_t
      14             :      *
      15             :      * The l0 pseudo-norm functional evaluates to @f$ \sum_{i=1}^n 1_{x_{i} \neq 0} @f$ for @f$
      16             :      * x=(x_i)_{i=1}^n @f$. Please note that it is not differentiable, hence getGradient and
      17             :      * getHessian will throw exceptions.
      18             :      *
      19             :      * References:
      20             :      * https://www.sciencedirect.com/bookseries/north-holland-mathematical-library/vol/38/suppl/C
      21             :      * https://en.wikipedia.org/wiki/Lp_space#When_p_=_0
      22             :      */
      23             :     template <typename data_t = real_t>
      24             :     class L0PseudoNorm : public Functional<data_t>
      25             :     {
      26             :     public:
      27             :         /**
      28             :          * @brief Constructor for the l0 pseudo-norm functional, mapping domain vector to a scalar
      29             :          * (without a residual)
      30             :          *
      31             :          * @param[in] domainDescriptor describing the domain of the functional
      32             :          */
      33             :         explicit L0PseudoNorm(const DataDescriptor& domainDescriptor);
      34             : 
      35             :         /**
      36             :          * @brief Constructor for the l0 psuedo-norm functional, using a residual as input to map to
      37             :          * a scalar
      38             :          *
      39             :          * @param[in] residual to be used when evaluating the functional (or its derivatives)
      40             :          */
      41             :         explicit L0PseudoNorm(const Residual<data_t>& residual);
      42             : 
      43             :         /// make copy constructor deletion explicit
      44             :         L0PseudoNorm(const L0PseudoNorm<data_t>&) = delete;
      45             : 
      46             :         /// default destructor
      47           0 :         ~L0PseudoNorm() override = default;
      48             : 
      49             :     protected:
      50             :         /// the evaluation of the l0 pseudo-norm
      51             :         auto evaluateImpl(const DataContainer<data_t>& Rx) -> data_t override;
      52             : 
      53             :         /// the computation of the gradient (in place)
      54             :         void getGradientInPlaceImpl(DataContainer<data_t>& Rx) override;
      55             : 
      56             :         /// the computation of the Hessian
      57             :         auto getHessianImpl(const DataContainer<data_t>& Rx) -> LinearOperator<data_t> override;
      58             : 
      59             :         /// implement the polymorphic clone operation
      60             :         auto cloneImpl() const -> L0PseudoNorm<data_t>* override;
      61             : 
      62             :         /// implement the polymorphic comparison operation
      63             :         auto isEqual(const Functional<data_t>& other) const -> bool override;
      64             :     };
      65             : } // namespace elsa