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Clean bdcsvd

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This commit is contained in:
Gael Guennebaud 2014-09-02 22:30:23 +02:00
parent 47829e2d16
commit a96f3d629c
1 changed files with 7 additions and 66 deletions
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73
unsupported/Eigen/src/BDCSVD/BDCSVD.h
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@ -57,6 +57,8 @@ class BDCSVD : public SVDBase<BDCSVD<_MatrixType> >
public: public:
using Base::rows; using Base::rows;
using Base::cols; using Base::cols;
using Base::computeU;
using Base::computeV;
typedef _MatrixType MatrixType; typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::Scalar Scalar;
@ -72,15 +74,10 @@ public:
MatrixOptions = MatrixType::Options MatrixOptions = MatrixType::Options
}; };
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, typedef typename Base::MatrixUType MatrixUType;
MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> typedef typename Base::MatrixVType MatrixVType;
MatrixUType; typedef typename Base::SingularValuesType SingularValuesType;
typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime,
MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime>
MatrixVType;
typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
typedef typename internal::plain_row_type<MatrixType>::type RowType;
typedef typename internal::plain_col_type<MatrixType>::type ColType;
typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX; typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX;
typedef Matrix<RealScalar, Dynamic, Dynamic> MatrixXr; typedef Matrix<RealScalar, Dynamic, Dynamic> MatrixXr;
typedef Matrix<RealScalar, Dynamic, 1> VectorType; typedef Matrix<RealScalar, Dynamic, 1> VectorType;
@ -155,27 +152,6 @@ public:
eigen_assert(s>3 && "BDCSVD the size of the algo switch has to be greater than 3"); eigen_assert(s>3 && "BDCSVD the size of the algo switch has to be greater than 3");
algoswap = s; algoswap = s;
} }
/** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
*
* \param b the right - hand - side of the equation to solve.
*
* \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V.
*
* \note SVD solving is implicitly least - squares. Thus, this method serves both purposes of exact solving and least - squares solving.
* In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$.
*/
template<typename Rhs>
inline const internal::solve_retval<BDCSVD, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(this->m_isInitialized && "BDCSVD is not initialized.");
eigen_assert(computeU() && computeV() &&
"BDCSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
return internal::solve_retval<BDCSVD, Rhs>(*this, b.derived());
}
const MatrixUType& matrixU() const const MatrixUType& matrixU() const
{ {
@ -188,11 +164,9 @@ public:
{ {
eigen_assert(this->computeU() && "This SVD decomposition didn't compute U. Did you ask for it?"); eigen_assert(this->computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
return this->m_matrixU; return this->m_matrixU;
} }
} }
const MatrixVType& matrixV() const const MatrixVType& matrixV() const
{ {
eigen_assert(this->m_isInitialized && "SVD is not initialized."); eigen_assert(this->m_isInitialized && "SVD is not initialized.");
@ -206,9 +180,6 @@ public:
return this->m_matrixV; return this->m_matrixV;
} }
} }
using Base::computeU;
using Base::computeV;
private: private:
void allocate(Index rows, Index cols, unsigned int computationOptions); void allocate(Index rows, Index cols, unsigned int computationOptions);
@ -898,36 +869,6 @@ void BDCSVD<MatrixType>::deflation(Index firstCol, Index lastCol, Index k, Index
}//end deflation }//end deflation
namespace internal{
template<typename _MatrixType, typename Rhs>
struct solve_retval<BDCSVD<_MatrixType>, Rhs>
: solve_retval_base<BDCSVD<_MatrixType>, Rhs>
{
typedef BDCSVD<_MatrixType> BDCSVDType;
EIGEN_MAKE_SOLVE_HELPERS(BDCSVDType, Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
eigen_assert(rhs().rows() == dec().rows());
// A = U S V^*
// So A^{ - 1} = V S^{ - 1} U^*
Index diagSize = (std::min)(dec().rows(), dec().cols());
typename BDCSVDType::SingularValuesType invertedSingVals(diagSize);
Index nonzeroSingVals = dec().nonzeroSingularValues();
invertedSingVals.head(nonzeroSingVals) = dec().singularValues().head(nonzeroSingVals).array().inverse();
invertedSingVals.tail(diagSize - nonzeroSingVals).setZero();
dst = dec().matrixV().leftCols(diagSize)
* invertedSingVals.asDiagonal()
* dec().matrixU().leftCols(diagSize).adjoint()
* rhs();
return;
}
};
} //end namespace internal
/** \svd_module /** \svd_module
* *
* \return the singular value decomposition of \c *this computed by * \return the singular value decomposition of \c *this computed by