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Merged latest updates from the Eigen trunk.

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This commit is contained in:
Benoit Steiner
2014-09-15 09:18:16 -07:00
parent efdff15749 9452eb38f8
commit 10a79ca3a3
66 changed files with 882 additions and 1343 deletions
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26
unsupported/Eigen/BDCSVD Normal file
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@@ -0,0 +1,26 @@
#ifndef EIGEN_BDCSVD_MODULE_H
#define EIGEN_BDCSVD_MODULE_H
#include <Eigen/SVD>
#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
/** \defgroup BDCSVD_Module BDCSVD module
*
*
*
* This module provides Divide & Conquer SVD decomposition for matrices (both real and complex).
* This decomposition is accessible via the following MatrixBase method:
* - MatrixBase::bdcSvd()
*
* \code
* #include <Eigen/BDCSVD>
* \endcode
*/
#include "src/BDCSVD/BDCSVD.h"
#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_BDCSVD_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

21
unsupported/Eigen/MatrixFunctions
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@@ -82,7 +82,9 @@ const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
\param[in] M a square matrix.
\returns expression representing \f$ \cos(M) \f$.
This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos().
This function computes the matrix cosine. Use ArrayBase::cos() for computing the entry-wise cosine.
The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos().
\sa \ref matrixbase_sin "sin()" for an example.
@@ -123,6 +125,9 @@ differential equations: the solution of \f$ y' = My \f$ with the
initial condition \f$ y(0) = y_0 \f$ is given by
\f$ y(t) = \exp(M) y_0 \f$.
The matrix exponential is different from applying the exp function to all the entries in the matrix.
Use ArrayBase::exp() if you want to do the latter.
The cost of the computation is approximately \f$ 20 n^3 \f$ for
matrices of size \f$ n \f$. The number 20 depends weakly on the
norm of the matrix.
@@ -177,6 +182,9 @@ the scalar logarithm, the equation \f$ \exp(X) = M \f$ may have
multiple solutions; this function returns a matrix whose eigenvalues
have imaginary part in the interval \f$ (-\pi,\pi] \f$.
The matrix logarithm is different from applying the log function to all the entries in the matrix.
Use ArrayBase::log() if you want to do the latter.
In the real case, the matrix \f$ M \f$ should be invertible and
it should have no eigenvalues which are real and negative (pairs of
complex conjugate eigenvalues are allowed). In the complex case, it
@@ -232,7 +240,8 @@ const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(RealScalar p) con
The matrix power \f$ M^p \f$ is defined as \f$ \exp(p \log(M)) \f$,
where exp denotes the matrix exponential, and log denotes the matrix
logarithm.
logarithm. This is different from raising all the entries in the matrix
to the p-th power. Use ArrayBase::pow() if you want to do the latter.
If \p p is complex, the scalar type of \p M should be the type of \p
p . \f$ M^p \f$ simply evaluates into \f$ \exp(p \log(M)) \f$.
@@ -391,7 +400,9 @@ const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
\param[in] M a square matrix.
\returns expression representing \f$ \sin(M) \f$.
This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin().
This function computes the matrix sine. Use ArrayBase::sin() for computing the entry-wise sine.
The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin().
Example: \include MatrixSine.cpp
Output: \verbinclude MatrixSine.out
@@ -428,7 +439,9 @@ const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
The matrix square root of \f$ M \f$ is the matrix \f$ M^{1/2} \f$
whose square is the original matrix; so if \f$ S = M^{1/2} \f$ then
\f$ S^2 = M \f$.
\f$ S^2 = M \f$. This is different from taking the square root of all
the entries in the matrix; use ArrayBase::sqrt() if you want to do the
latter.
In the <b>real case</b>, the matrix \f$ M \f$ should be invertible and
it should have no eigenvalues which are real and negative (pairs of

35
unsupported/Eigen/SVD
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@@ -1,35 +0,0 @@
#ifndef EIGEN_SVD_MODULE_H
#define EIGEN_SVD_MODULE_H
#include <Eigen/QR>
#include <Eigen/Householder>
#include <Eigen/Jacobi>
#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
/** \defgroup SVD_Module SVD module
*
*
*
* This module provides SVD decomposition for matrices (both real and complex).
* This decomposition is accessible via the following MatrixBase method:
* - MatrixBase::jacobiSvd()
*
* \code
* #include <Eigen/SVD>
* \endcode
*/
#include "../../Eigen/src/misc/Solve.h"
#include "../../Eigen/src/SVD/UpperBidiagonalization.h"
#include "src/SVD/SVDBase.h"
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/BDCSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#include "../../Eigen/src/SVD/JacobiSVD_MKL.h"
#endif
#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_SVD_MODULE_H
/* vim: set filetype=cpp et sw=2 ts=2 ai: */

48
unsupported/Eigen/src/SVD/BDCSVD.h → unsupported/Eigen/src/BDCSVD/BDCSVD.h
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@@ -24,6 +24,20 @@
#define ALGOSWAP 16
namespace Eigen {
template<typename _MatrixType> class BDCSVD;
namespace internal {
template<typename _MatrixType>
struct traits<BDCSVD<_MatrixType> >
{
typedef _MatrixType MatrixType;
};
} // end namespace internal
/** \ingroup SVD_Module
*
*
@@ -36,9 +50,9 @@ namespace Eigen {
* It should be used to speed up the calcul of SVD for big matrices.
*/
template<typename _MatrixType>
class BDCSVD : public SVDBase<_MatrixType>
class BDCSVD : public SVDBase<BDCSVD<_MatrixType> >
{
typedef SVDBase<_MatrixType> Base;
typedef SVDBase<BDCSVD> Base;
public:
using Base::rows;
@@ -77,9 +91,7 @@ public:
* The default constructor is useful in cases in which the user intends to
* perform decompositions via BDCSVD::compute(const MatrixType&).
*/
BDCSVD()
: SVDBase<_MatrixType>::SVDBase(),
algoswap(ALGOSWAP), m_numIters(0)
BDCSVD() : algoswap(ALGOSWAP), m_numIters(0)
{}
@@ -90,8 +102,7 @@ public:
* \sa BDCSVD()
*/
BDCSVD(Index rows, Index cols, unsigned int computationOptions = 0)
: SVDBase<_MatrixType>::SVDBase(),
algoswap(ALGOSWAP), m_numIters(0)
: algoswap(ALGOSWAP), m_numIters(0)
{
allocate(rows, cols, computationOptions);
}
@@ -107,8 +118,7 @@ public:
* available with the (non - default) FullPivHouseholderQR preconditioner.
*/
BDCSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
: SVDBase<_MatrixType>::SVDBase(),
algoswap(ALGOSWAP), m_numIters(0)
: algoswap(ALGOSWAP), m_numIters(0)
{
compute(matrix, computationOptions);
}
@@ -116,6 +126,7 @@ public:
~BDCSVD()
{
}
/** \brief Method performing the decomposition of given matrix using custom options.
*
* \param matrix the matrix to decompose
@@ -126,7 +137,7 @@ public:
* Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
* available with the (non - default) FullPivHouseholderQR preconditioner.
*/
SVDBase<MatrixType>& compute(const MatrixType& matrix, unsigned int computationOptions);
BDCSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
/** \brief Method performing the decomposition of given matrix using current options.
*
@@ -134,7 +145,7 @@ public:
*
* This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
*/
SVDBase<MatrixType>& compute(const MatrixType& matrix)
BDCSVD& compute(const MatrixType& matrix)
{
return compute(matrix, this->m_computationOptions);
}
@@ -160,8 +171,8 @@ public:
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(this->m_isInitialized && "BDCSVD is not initialized.");
eigen_assert(SVDBase<_MatrixType>::computeU() && SVDBase<_MatrixType>::computeV() &&
"BDCSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
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());
}
@@ -195,6 +206,9 @@ public:
return this->m_matrixV;
}
}
using Base::computeU;
using Base::computeV;
private:
void allocate(Index rows, Index cols, unsigned int computationOptions);
@@ -229,7 +243,7 @@ template<typename MatrixType>
void BDCSVD<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions)
{
isTranspose = (cols > rows);
if (SVDBase<MatrixType>::allocate(rows, cols, computationOptions)) return;
if (Base::allocate(rows, cols, computationOptions)) return;
m_computed = MatrixXr::Zero(this->m_diagSize + 1, this->m_diagSize );
if (isTranspose){
compU = this->computeU();
@@ -262,8 +276,7 @@ void BDCSVD<MatrixType>::allocate(Index rows, Index cols, unsigned int computati
// Methode which compute the BDCSVD for the int
template<>
SVDBase<Matrix<int, Dynamic, Dynamic> >&
BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsigned int computationOptions) {
BDCSVD<Matrix<int, Dynamic, Dynamic> >& BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsigned int computationOptions) {
allocate(matrix.rows(), matrix.cols(), computationOptions);
this->m_nonzeroSingularValues = 0;
m_computed = Matrix<int, Dynamic, Dynamic>::Zero(rows(), cols());
@@ -279,8 +292,7 @@ BDCSVD<Matrix<int, Dynamic, Dynamic> >::compute(const MatrixType& matrix, unsign
// Methode which compute the BDCSVD
template<typename MatrixType>
SVDBase<MatrixType>&
BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions)
BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions)
{
allocate(matrix.rows(), matrix.cols(), computationOptions);
using std::abs;

6
unsupported/Eigen/src/BDCSVD/CMakeLists.txt Normal file
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@@ -0,0 +1,6 @@
FILE(GLOB Eigen_BDCSVD_SRCS "*.h")
INSTALL(FILES
${Eigen_BDCSVD_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}unsupported/Eigen/src/BDCSVD COMPONENT Devel
)

0
unsupported/Eigen/src/SVD/TODOBdcsvd.txt → unsupported/Eigen/src/BDCSVD/TODOBdcsvd.txt
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0
unsupported/Eigen/src/SVD/doneInBDCSVD.txt → unsupported/Eigen/src/BDCSVD/doneInBDCSVD.txt
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1
unsupported/Eigen/src/CMakeLists.txt
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@@ -12,3 +12,4 @@ ADD_SUBDIRECTORY(Skyline)
ADD_SUBDIRECTORY(SparseExtra)
ADD_SUBDIRECTORY(KroneckerProduct)
ADD_SUBDIRECTORY(Splines)
ADD_SUBDIRECTORY(BDCSVD)

156
unsupported/Eigen/src/IterativeSolvers/GMRES.h
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@@ -11,7 +11,7 @@
#ifndef EIGEN_GMRES_H
#define EIGEN_GMRES_H
namespace Eigen {
namespace Eigen {
namespace internal {
@@ -27,11 +27,11 @@ namespace internal {
* \param iters on input: maximum number of iterations to perform
* on output: number of iterations performed
* \param restart number of iterations for a restart
* \param tol_error on input: residual tolerance
* \param tol_error on input: relative residual tolerance
* on output: residuum achieved
*
* \sa IterativeMethods::bicgstab()
*
* \sa IterativeMethods::bicgstab()
*
*
* For references, please see:
*
@@ -70,18 +70,24 @@ bool gmres(const MatrixType & mat, const Rhs & rhs, Dest & x, const Precondition
const int m = mat.rows();
VectorType p0 = rhs - mat*x;
// residual and preconditioned residual
const VectorType p0 = rhs - mat*x;
VectorType r0 = precond.solve(p0);
const RealScalar r0Norm = r0.norm();
// is initial guess already good enough?
if(abs(r0.norm()) < tol) {
return true;
if(r0Norm == 0) {
tol_error=0;
return true;
}
// storage for Hessenberg matrix and Householder data
FMatrixType H = FMatrixType::Zero(m, restart + 1);
VectorType w = VectorType::Zero(restart + 1);
FMatrixType H = FMatrixType::Zero(m, restart + 1); // Hessenberg matrix
VectorType tau = VectorType::Zero(restart + 1);
// storage for Jacobi rotations
std::vector < JacobiRotation < Scalar > > G(restart);
// generate first Householder vector
@@ -112,11 +118,10 @@ bool gmres(const MatrixType & mat, const Rhs & rhs, Dest & x, const Precondition
}
if (v.tail(m - k).norm() != 0.0) {
if (k <= restart) {
// generate new Householder vector
VectorType e(m - k - 1);
VectorType e(m - k - 1);
RealScalar beta;
v.tail(m - k).makeHouseholder(e, tau.coeffRef(k), beta);
H.col(k).tail(m - k - 1) = e;
@@ -125,78 +130,77 @@ bool gmres(const MatrixType & mat, const Rhs & rhs, Dest & x, const Precondition
v.tail(m - k).applyHouseholderOnTheLeft(H.col(k).tail(m - k - 1), tau.coeffRef(k), workspace.data());
}
}
}
if (k > 1) {
for (int i = 0; i < k - 1; ++i) {
// apply old Givens rotations to v
v.applyOnTheLeft(i, i + 1, G[i].adjoint());
}
}
if (k > 1) {
for (int i = 0; i < k - 1; ++i) {
// apply old Givens rotations to v
v.applyOnTheLeft(i, i + 1, G[i].adjoint());
}
}
if (k<m && v(k) != (Scalar) 0) {
// determine next Givens rotation
G[k - 1].makeGivens(v(k - 1), v(k));
if (k<m && v(k) != (Scalar) 0) {
// apply Givens rotation to v and w
v.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
w.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
// determine next Givens rotation
G[k - 1].makeGivens(v(k - 1), v(k));
}
// apply Givens rotation to v and w
v.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
w.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
}
// insert coefficients into upper matrix triangle
H.col(k - 1).head(k) = v.head(k);
// insert coefficients into upper matrix triangle
H.col(k - 1).head(k) = v.head(k);
bool stop=(k==m || abs(w(k)) < tol || iters == maxIters);
bool stop=(k==m || abs(w(k)) < tol * r0Norm || iters == maxIters);
if (stop || k == restart) {
if (stop || k == restart) {
// solve upper triangular system
VectorType y = w.head(k);
H.topLeftCorner(k, k).template triangularView < Eigen::Upper > ().solveInPlace(y);
// solve upper triangular system
VectorType y = w.head(k);
H.topLeftCorner(k, k).template triangularView < Eigen::Upper > ().solveInPlace(y);
// use Horner-like scheme to calculate solution vector
VectorType x_new = y(k - 1) * VectorType::Unit(m, k - 1);
// use Horner-like scheme to calculate solution vector
VectorType x_new = y(k - 1) * VectorType::Unit(m, k - 1);
// apply Householder reflection H_{k} to x_new
x_new.tail(m - k + 1).applyHouseholderOnTheLeft(H.col(k - 1).tail(m - k), tau.coeffRef(k - 1), workspace.data());
// apply Householder reflection H_{k} to x_new
x_new.tail(m - k + 1).applyHouseholderOnTheLeft(H.col(k - 1).tail(m - k), tau.coeffRef(k - 1), workspace.data());
for (int i = k - 2; i >= 0; --i) {
x_new += y(i) * VectorType::Unit(m, i);
// apply Householder reflection H_{i} to x_new
x_new.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
}
for (int i = k - 2; i >= 0; --i) {
x_new += y(i) * VectorType::Unit(m, i);
// apply Householder reflection H_{i} to x_new
x_new.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
}
x += x_new;
x += x_new;
if (stop) {
return true;
} else {
k=0;
if (stop) {
return true;
} else {
k=0;
// reset data for a restart r0 = rhs - mat * x;
VectorType p0=mat*x;
VectorType p1=precond.solve(p0);
r0 = rhs - p1;
// r0_sqnorm = r0.squaredNorm();
w = VectorType::Zero(restart + 1);
H = FMatrixType::Zero(m, restart + 1);
tau = VectorType::Zero(restart + 1);
// reset data for restart
const VectorType p0 = rhs - mat*x;
r0 = precond.solve(p0);
// generate first Householder vector
RealScalar beta;
r0.makeHouseholder(e, tau.coeffRef(0), beta);
w(0)=(Scalar) beta;
H.bottomLeftCorner(m - 1, 1) = e;
// clear Hessenberg matrix and Householder data
H = FMatrixType::Zero(m, restart + 1);
w = VectorType::Zero(restart + 1);
tau = VectorType::Zero(restart + 1);
}
// generate first Householder vector
RealScalar beta;
r0.makeHouseholder(e, tau.coeffRef(0), beta);
w(0)=(Scalar) beta;
H.bottomLeftCorner(m - 1, 1) = e;
}
}
}
}
return false;
}
@@ -230,7 +234,7 @@ struct traits<GMRES<_MatrixType,_Preconditioner> >
* The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
* and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
* and NumTraits<Scalar>::epsilon() for the tolerance.
*
*
* This class can be used as the direct solver classes. Here is a typical usage example:
* \code
* int n = 10000;
@@ -244,7 +248,7 @@ struct traits<GMRES<_MatrixType,_Preconditioner> >
* // update b, and solve again
* x = solver.solve(b);
* \endcode
*
*
* By default the iterations start with x=0 as an initial guess of the solution.
* One can control the start using the solveWithGuess() method. Here is a step by
* step execution example starting with a random guess and printing the evolution
@@ -260,7 +264,7 @@ struct traits<GMRES<_MatrixType,_Preconditioner> >
* } while (solver.info()!=Success && i<100);
* \endcode
* Note that such a step by step excution is slightly slower.
*
*
* \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
*/
template< typename _MatrixType, typename _Preconditioner>
@@ -272,10 +276,10 @@ class GMRES : public IterativeSolverBase<GMRES<_MatrixType,_Preconditioner> >
using Base::m_iterations;
using Base::m_info;
using Base::m_isInitialized;
private:
int m_restart;
public:
typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar;
@@ -289,10 +293,10 @@ public:
GMRES() : Base(), m_restart(30) {}
/** Initialize the solver with matrix \a A for further \c Ax=b solving.
*
*
* This constructor is a shortcut for the default constructor followed
* by a call to compute().
*
*
* \warning this class stores a reference to the matrix A as well as some
* precomputed values that depend on it. Therefore, if \a A is changed
* this class becomes invalid. Call compute() to update it with the new
@@ -301,16 +305,16 @@ public:
GMRES(const MatrixType& A) : Base(A), m_restart(30) {}
~GMRES() {}
/** Get the number of iterations after that a restart is performed.
*/
int get_restart() { return m_restart; }
/** Set the number of iterations after that a restart is performed.
* \param restart number of iterations for a restarti, default is 30.
*/
void set_restart(const int restart) { m_restart=restart; }
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
* \a x0 as an initial solution.
*
@@ -326,17 +330,17 @@ public:
return internal::solve_retval_with_guess
<GMRES, Rhs, Guess>(*this, b.derived(), x0);
}
/** \internal */
template<typename Rhs,typename Dest>
void _solveWithGuess(const Rhs& b, Dest& x) const
{
{
bool failed = false;
for(int j=0; j<b.cols(); ++j)
{
m_iterations = Base::maxIterations();
m_error = Base::m_tolerance;
typename Dest::ColXpr xj(x,j);
if(!internal::gmres(*mp_matrix, b.col(j), xj, Base::m_preconditioner, m_iterations, m_restart, m_error))
failed = true;

8
unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
View File

@@ -144,11 +144,13 @@ class LevenbergMarquardt : internal::no_assignment_operator
/** Sets the default parameters */
void resetParameters()
{
{
using std::sqrt;
m_factor = 100.;
m_maxfev = 400;
m_ftol = std::sqrt(NumTraits<RealScalar>::epsilon());
m_xtol = std::sqrt(NumTraits<RealScalar>::epsilon());
m_ftol = sqrt(NumTraits<RealScalar>::epsilon());
m_xtol = sqrt(NumTraits<RealScalar>::epsilon());
m_gtol = 0. ;
m_epsfcn = 0. ;
}

19
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
View File

@@ -45,18 +45,24 @@ namespace LevenbergMarquardtSpace {
template<typename FunctorType, typename Scalar=double>
class LevenbergMarquardt
{
static Scalar sqrt_epsilon()
{
using std::sqrt;
return sqrt(NumTraits<Scalar>::epsilon());
}
public:
LevenbergMarquardt(FunctorType &_functor)
: functor(_functor) { nfev = njev = iter = 0; fnorm = gnorm = 0.; useExternalScaling=false; }
typedef DenseIndex Index;
struct Parameters {
Parameters()
: factor(Scalar(100.))
, maxfev(400)
, ftol(std::sqrt(NumTraits<Scalar>::epsilon()))
, xtol(std::sqrt(NumTraits<Scalar>::epsilon()))
, ftol(sqrt_epsilon())
, xtol(sqrt_epsilon())
, gtol(Scalar(0.))
, epsfcn(Scalar(0.)) {}
Scalar factor;
@@ -72,7 +78,7 @@ public:
LevenbergMarquardtSpace::Status lmder1(
FVectorType &x,
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = sqrt_epsilon()
);
LevenbergMarquardtSpace::Status minimize(FVectorType &x);
@@ -83,12 +89,12 @@ public:
FunctorType &functor,
FVectorType &x,
Index *nfev,
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = sqrt_epsilon()
);
LevenbergMarquardtSpace::Status lmstr1(
FVectorType &x,
const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = sqrt_epsilon()
);
LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType &x);
@@ -109,6 +115,7 @@ public:
Scalar lm_param(void) { return par; }
private:
FunctorType &functor;
Index n;
Index m;

6
unsupported/Eigen/src/SVD/CMakeLists.txt
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@@ -1,6 +0,0 @@
FILE(GLOB Eigen_SVD_SRCS "*.h")
INSTALL(FILES
${Eigen_SVD_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}unsupported/Eigen/src/SVD COMPONENT Devel
)

782
unsupported/Eigen/src/SVD/JacobiSVD.h
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@@ -1,782 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_JACOBISVD_H
#define EIGEN_JACOBISVD_H
namespace Eigen {
namespace internal {
// forward declaration (needed by ICC)
// the empty body is required by MSVC
template<typename MatrixType, int QRPreconditioner,
bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
struct svd_precondition_2x2_block_to_be_real {};
/*** QR preconditioners (R-SVD)
***
*** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
*** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
*** JacobiSVD which by itself is only able to work on square matrices.
***/
enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols };
template<typename MatrixType, int QRPreconditioner, int Case>
struct qr_preconditioner_should_do_anything
{
enum { a = MatrixType::RowsAtCompileTime != Dynamic &&
MatrixType::ColsAtCompileTime != Dynamic &&
MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime,
b = MatrixType::RowsAtCompileTime != Dynamic &&
MatrixType::ColsAtCompileTime != Dynamic &&
MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime,
ret = !( (QRPreconditioner == NoQRPreconditioner) ||
(Case == PreconditionIfMoreColsThanRows && bool(a)) ||
(Case == PreconditionIfMoreRowsThanCols && bool(b)) )
};
};
template<typename MatrixType, int QRPreconditioner, int Case,
bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret
> struct qr_preconditioner_impl {};
template<typename MatrixType, int QRPreconditioner, int Case>
class qr_preconditioner_impl<MatrixType, QRPreconditioner, Case, false>
{
public:
typedef typename MatrixType::Index Index;
void allocate(const JacobiSVD<MatrixType, QRPreconditioner>&) {}
bool run(JacobiSVD<MatrixType, QRPreconditioner>&, const MatrixType&)
{
return false;
}
};
/*** preconditioner using FullPivHouseholderQR ***/
template<typename MatrixType>
class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
{
public:
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
enum
{
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
};
typedef Matrix<Scalar, 1, RowsAtCompileTime, RowMajor, 1, MaxRowsAtCompileTime> WorkspaceType;
void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
{
if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
{
m_qr.~QRType();
::new (&m_qr) QRType(svd.rows(), svd.cols());
}
if (svd.m_computeFullU) m_workspace.resize(svd.rows());
}
bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
{
if(matrix.rows() > matrix.cols())
{
m_qr.compute(matrix);
svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
if(svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace);
if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
return true;
}
return false;
}
private:
typedef FullPivHouseholderQR<MatrixType> QRType;
QRType m_qr;
WorkspaceType m_workspace;
};
template<typename MatrixType>
class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
{
public:
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
enum
{
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
Options = MatrixType::Options
};
typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime>
TransposeTypeWithSameStorageOrder;
void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
{
if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
{
m_qr.~QRType();
::new (&m_qr) QRType(svd.cols(), svd.rows());
}
m_adjoint.resize(svd.cols(), svd.rows());
if (svd.m_computeFullV) m_workspace.resize(svd.cols());
}
bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
{
if(matrix.cols() > matrix.rows())
{
m_adjoint = matrix.adjoint();
m_qr.compute(m_adjoint);
svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
if(svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace);
if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
return true;
}
else return false;
}
private:
typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
QRType m_qr;
TransposeTypeWithSameStorageOrder m_adjoint;
typename internal::plain_row_type<MatrixType>::type m_workspace;
};
/*** preconditioner using ColPivHouseholderQR ***/
template<typename MatrixType>
class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
{
public:
typedef typename MatrixType::Index Index;
void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
{
if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
{
m_qr.~QRType();
::new (&m_qr) QRType(svd.rows(), svd.cols());
}
if (svd.m_computeFullU) m_workspace.resize(svd.rows());
else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
}
bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
{
if(matrix.rows() > matrix.cols())
{
m_qr.compute(matrix);
svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
else if(svd.m_computeThinU)
{
svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
}
if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
return true;
}
return false;
}
private:
typedef ColPivHouseholderQR<MatrixType> QRType;
QRType m_qr;
typename internal::plain_col_type<MatrixType>::type m_workspace;
};
template<typename MatrixType>
class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
{
public:
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
enum
{
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
Options = MatrixType::Options
};
typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime>
TransposeTypeWithSameStorageOrder;
void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
{
if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
{
m_qr.~QRType();
::new (&m_qr) QRType(svd.cols(), svd.rows());
}
if (svd.m_computeFullV) m_workspace.resize(svd.cols());
else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
m_adjoint.resize(svd.cols(), svd.rows());
}
bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
{
if(matrix.cols() > matrix.rows())
{
m_adjoint = matrix.adjoint();
m_qr.compute(m_adjoint);
svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
else if(svd.m_computeThinV)
{
svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
}
if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
return true;
}
else return false;
}
private:
typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
QRType m_qr;
TransposeTypeWithSameStorageOrder m_adjoint;
typename internal::plain_row_type<MatrixType>::type m_workspace;
};
/*** preconditioner using HouseholderQR ***/
template<typename MatrixType>
class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true>
{
public:
typedef typename MatrixType::Index Index;
void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
{
if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
{
m_qr.~QRType();
::new (&m_qr) QRType(svd.rows(), svd.cols());
}
if (svd.m_computeFullU) m_workspace.resize(svd.rows());
else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
}
bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
{
if(matrix.rows() > matrix.cols())
{
m_qr.compute(matrix);
svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
else if(svd.m_computeThinU)
{
svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
}
if(svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols());
return true;
}
return false;
}
private:
typedef HouseholderQR<MatrixType> QRType;
QRType m_qr;
typename internal::plain_col_type<MatrixType>::type m_workspace;
};
template<typename MatrixType>
class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true>
{
public:
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
enum
{
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
Options = MatrixType::Options
};
typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime>
TransposeTypeWithSameStorageOrder;
void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
{
if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
{
m_qr.~QRType();
::new (&m_qr) QRType(svd.cols(), svd.rows());
}
if (svd.m_computeFullV) m_workspace.resize(svd.cols());
else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
m_adjoint.resize(svd.cols(), svd.rows());
}
bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
{
if(matrix.cols() > matrix.rows())
{
m_adjoint = matrix.adjoint();
m_qr.compute(m_adjoint);
svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
else if(svd.m_computeThinV)
{
svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
}
if(svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows());
return true;
}
else return false;
}
private:
typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
QRType m_qr;
TransposeTypeWithSameStorageOrder m_adjoint;
typename internal::plain_row_type<MatrixType>::type m_workspace;
};
/*** 2x2 SVD implementation
***
*** JacobiSVD consists in performing a series of 2x2 SVD subproblems
***/
template<typename MatrixType, int QRPreconditioner>
struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, false>
{
typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
typedef typename SVD::Index Index;
static void run(typename SVD::WorkMatrixType&, SVD&, Index, Index) {}
};
template<typename MatrixType, int QRPreconditioner>
struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
{
typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename SVD::Index Index;
static void run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q)
{
using std::sqrt;
Scalar z;
JacobiRotation<Scalar> rot;
RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p)));
if(n==0)
{
z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
work_matrix.row(p) *= z;
if(svd.computeU()) svd.m_matrixU.col(p) *= conj(z);
z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
work_matrix.row(q) *= z;
if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
}
else
{
rot.c() = conj(work_matrix.coeff(p,p)) / n;
rot.s() = work_matrix.coeff(q,p) / n;
work_matrix.applyOnTheLeft(p,q,rot);
if(svd.computeU()) svd.m_matrixU.applyOnTheRight(p,q,rot.adjoint());
if(work_matrix.coeff(p,q) != Scalar(0))
{
Scalar z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
work_matrix.col(q) *= z;
if(svd.computeV()) svd.m_matrixV.col(q) *= z;
}
if(work_matrix.coeff(q,q) != Scalar(0))
{
z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
work_matrix.row(q) *= z;
if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
}
}
}
};
template<typename MatrixType, typename RealScalar, typename Index>
void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
JacobiRotation<RealScalar> *j_left,
JacobiRotation<RealScalar> *j_right)
{
using std::sqrt;
Matrix<RealScalar,2,2> m;
m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
JacobiRotation<RealScalar> rot1;
RealScalar t = m.coeff(0,0) + m.coeff(1,1);
RealScalar d = m.coeff(1,0) - m.coeff(0,1);
if(t == RealScalar(0))
{
rot1.c() = RealScalar(0);
rot1.s() = d > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
}
else
{
RealScalar u = d / t;
rot1.c() = RealScalar(1) / sqrt(RealScalar(1) + numext::abs2(u));
rot1.s() = rot1.c() * u;
}
m.applyOnTheLeft(0,1,rot1);
j_right->makeJacobi(m,0,1);
*j_left = rot1 * j_right->transpose();
}
} // end namespace internal
/** \ingroup SVD_Module
*
*
* \class JacobiSVD
*
* \brief Two-sided Jacobi SVD decomposition of a rectangular matrix
*
* \param MatrixType the type of the matrix of which we are computing the SVD decomposition
* \param QRPreconditioner this optional parameter allows to specify the type of QR decomposition that will be used internally
* for the R-SVD step for non-square matrices. See discussion of possible values below.
*
* SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
* \f[ A = U S V^* \f]
* where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
* the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
* and right \em singular \em vectors of \a A respectively.
*
* Singular values are always sorted in decreasing order.
*
* This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask for them explicitly.
*
* You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
* smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
* singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
* and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
*
* Here's an example demonstrating basic usage:
* \include JacobiSVD_basic.cpp
* Output: \verbinclude JacobiSVD_basic.out
*
* This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
* bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \f$ O(n^2p) \f$ where \a n is the smaller dimension and
* \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms.
* In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
*
* If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
* terminate in finite (and reasonable) time.
*
* The possible values for QRPreconditioner are:
* \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
* \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
* Contrary to other QRs, it doesn't allow computing thin unitaries.
* \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses non-pivoting QR.
* This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing SVD algorithms (since bidiagonalization
* is inherently non-pivoting). However the resulting SVD is still more reliable than bidiagonalizing SVDs because the Jacobi-based iterarive
* process is more reliable than the optimized bidiagonal SVD iterations.
* \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that you will only be computing
* JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in
* faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking
* if QR preconditioning is needed before applying it anyway.
*
* \sa MatrixBase::jacobiSvd()
*/
template<typename _MatrixType, int QRPreconditioner>
class JacobiSVD : public SVDBase<_MatrixType>
{
public:
typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
MatrixOptions = MatrixType::Options
};
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime,
MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime>
MatrixUType;
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, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
WorkMatrixType;
/** \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via JacobiSVD::compute(const MatrixType&).
*/
JacobiSVD()
: SVDBase<_MatrixType>::SVDBase()
{}
/** \brief Default Constructor with memory preallocation
*
* Like the default constructor but with preallocation of the internal data
* according to the specified problem size.
* \sa JacobiSVD()
*/
JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0)
: SVDBase<_MatrixType>::SVDBase()
{
allocate(rows, cols, computationOptions);
}
/** \brief Constructor performing the decomposition of given matrix.
*
* \param matrix the matrix to decompose
* \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
* By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
* #ComputeFullV, #ComputeThinV.
*
* Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
* available with the (non-default) FullPivHouseholderQR preconditioner.
*/
JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
: SVDBase<_MatrixType>::SVDBase()
{
compute(matrix, computationOptions);
}
/** \brief Method performing the decomposition of given matrix using custom options.
*
* \param matrix the matrix to decompose
* \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
* By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
* #ComputeFullV, #ComputeThinV.
*
* Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
* available with the (non-default) FullPivHouseholderQR preconditioner.
*/
SVDBase<MatrixType>& compute(const MatrixType& matrix, unsigned int computationOptions);
/** \brief Method performing the decomposition of given matrix using current options.
*
* \param matrix the matrix to decompose
*
* This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
*/
SVDBase<MatrixType>& compute(const MatrixType& matrix)
{
return compute(matrix, this->m_computationOptions);
}
/** \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<JacobiSVD, Rhs>
solve(const MatrixBase<Rhs>& b) const
{
eigen_assert(this->m_isInitialized && "JacobiSVD is not initialized.");
eigen_assert(SVDBase<MatrixType>::computeU() && SVDBase<MatrixType>::computeV() && "JacobiSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
return internal::solve_retval<JacobiSVD, Rhs>(*this, b.derived());
}
private:
void allocate(Index rows, Index cols, unsigned int computationOptions);
protected:
WorkMatrixType m_workMatrix;
template<typename __MatrixType, int _QRPreconditioner, bool _IsComplex>
friend struct internal::svd_precondition_2x2_block_to_be_real;
template<typename __MatrixType, int _QRPreconditioner, int _Case, bool _DoAnything>
friend struct internal::qr_preconditioner_impl;
internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows> m_qr_precond_morecols;
internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols> m_qr_precond_morerows;
};
template<typename MatrixType, int QRPreconditioner>
void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, unsigned int computationOptions)
{
if (SVDBase<MatrixType>::allocate(rows, cols, computationOptions)) return;
if (QRPreconditioner == FullPivHouseholderQRPreconditioner)
{
eigen_assert(!(this->m_computeThinU || this->m_computeThinV) &&
"JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
"Use the ColPivHouseholderQR preconditioner instead.");
}
m_workMatrix.resize(this->m_diagSize, this->m_diagSize);
if(this->m_cols>this->m_rows) m_qr_precond_morecols.allocate(*this);
if(this->m_rows>this->m_cols) m_qr_precond_morerows.allocate(*this);
}
template<typename MatrixType, int QRPreconditioner>
SVDBase<MatrixType>&
JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsigned int computationOptions)
{
using std::abs;
allocate(matrix.rows(), matrix.cols(), computationOptions);
// currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number of iterations,
// only worsening the precision of U and V as we accumulate more rotations
const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
// limit for very small denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
const RealScalar considerAsZero = RealScalar(2) * std::numeric_limits<RealScalar>::denorm_min();
/*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
if(!m_qr_precond_morecols.run(*this, matrix) && !m_qr_precond_morerows.run(*this, matrix))
{
m_workMatrix = matrix.block(0,0,this->m_diagSize,this->m_diagSize);
if(this->m_computeFullU) this->m_matrixU.setIdentity(this->m_rows,this->m_rows);
if(this->m_computeThinU) this->m_matrixU.setIdentity(this->m_rows,this->m_diagSize);
if(this->m_computeFullV) this->m_matrixV.setIdentity(this->m_cols,this->m_cols);
if(this->m_computeThinV) this->m_matrixV.setIdentity(this->m_cols, this->m_diagSize);
}
/*** step 2. The main Jacobi SVD iteration. ***/
bool finished = false;
while(!finished)
{
finished = true;
// do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix
for(Index p = 1; p < this->m_diagSize; ++p)
{
for(Index q = 0; q < p; ++q)
{
// if this 2x2 sub-matrix is not diagonal already...
// notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
// keep us iterating forever. Similarly, small denormal numbers are considered zero.
using std::max;
RealScalar threshold = (max)(considerAsZero, precision * (max)(abs(m_workMatrix.coeff(p,p)),
abs(m_workMatrix.coeff(q,q))));
if((max)(abs(m_workMatrix.coeff(p,q)),abs(m_workMatrix.coeff(q,p))) > threshold)
{
finished = false;
// perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal
internal::svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner>::run(m_workMatrix, *this, p, q);
JacobiRotation<RealScalar> j_left, j_right;
internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right);
// accumulate resulting Jacobi rotations
m_workMatrix.applyOnTheLeft(p,q,j_left);
if(SVDBase<MatrixType>::computeU()) this->m_matrixU.applyOnTheRight(p,q,j_left.transpose());
m_workMatrix.applyOnTheRight(p,q,j_right);
if(SVDBase<MatrixType>::computeV()) this->m_matrixV.applyOnTheRight(p,q,j_right);
}
}
}
}
/*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values ***/
for(Index i = 0; i < this->m_diagSize; ++i)
{
RealScalar a = abs(m_workMatrix.coeff(i,i));
this->m_singularValues.coeffRef(i) = a;
if(SVDBase<MatrixType>::computeU() && (a!=RealScalar(0))) this->m_matrixU.col(i) *= this->m_workMatrix.coeff(i,i)/a;
}
/*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/
this->m_nonzeroSingularValues = this->m_diagSize;
for(Index i = 0; i < this->m_diagSize; i++)
{
Index pos;
RealScalar maxRemainingSingularValue = this->m_singularValues.tail(this->m_diagSize-i).maxCoeff(&pos);
if(maxRemainingSingularValue == RealScalar(0))
{
this->m_nonzeroSingularValues = i;
break;
}
if(pos)
{
pos += i;
std::swap(this->m_singularValues.coeffRef(i), this->m_singularValues.coeffRef(pos));
if(SVDBase<MatrixType>::computeU()) this->m_matrixU.col(pos).swap(this->m_matrixU.col(i));
if(SVDBase<MatrixType>::computeV()) this->m_matrixV.col(pos).swap(this->m_matrixV.col(i));
}
}
this->m_isInitialized = true;
return *this;
}
namespace internal {
template<typename _MatrixType, int QRPreconditioner, typename Rhs>
struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
: solve_retval_base<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
{
typedef JacobiSVD<_MatrixType, QRPreconditioner> JacobiSVDType;
EIGEN_MAKE_SOLVE_HELPERS(JacobiSVDType,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 JacobiSVDType::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();
}
};
} // end namespace internal
/** \svd_module
*
* \return the singular value decomposition of \c *this computed by two-sided
* Jacobi transformations.
*
* \sa class JacobiSVD
*/
template<typename Derived>
JacobiSVD<typename MatrixBase<Derived>::PlainObject>
MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const
{
return JacobiSVD<PlainObject>(*this, computationOptions);
}
} // end namespace Eigen
#endif // EIGEN_JACOBISVD_H

236
unsupported/Eigen/src/SVD/SVDBase.h
View File

@@ -1,236 +0,0 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SVD_H
#define EIGEN_SVD_H
namespace Eigen {
/** \ingroup SVD_Module
*
*
* \class SVDBase
*
* \brief Mother class of SVD classes algorithms
*
* \param MatrixType the type of the matrix of which we are computing the SVD decomposition
* SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
* \f[ A = U S V^* \f]
* where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
* the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
* and right \em singular \em vectors of \a A respectively.
*
* Singular values are always sorted in decreasing order.
*
*
* You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
* smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
* singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
* and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
*
* If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to
* terminate in finite (and reasonable) time.
* \sa MatrixBase::genericSvd()
*/
template<typename _MatrixType>
class SVDBase
{
public:
typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Index Index;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime),
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime),
MatrixOptions = MatrixType::Options
};
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime,
MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime>
MatrixUType;
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, DiagSizeAtCompileTime, DiagSizeAtCompileTime,
MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime>
WorkMatrixType;
/** \brief Method performing the decomposition of given matrix using custom options.
*
* \param matrix the matrix to decompose
* \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed.
* By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU,
* #ComputeFullV, #ComputeThinV.
*
* Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not
* available with the (non-default) FullPivHouseholderQR preconditioner.
*/
SVDBase& compute(const MatrixType& matrix, unsigned int computationOptions);
/** \brief Method performing the decomposition of given matrix using current options.
*
* \param matrix the matrix to decompose
*
* This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
*/
//virtual SVDBase& compute(const MatrixType& matrix) = 0;
SVDBase& compute(const MatrixType& matrix);
/** \returns the \a U matrix.
*
* For the SVDBase decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
* the U matrix is n-by-n if you asked for #ComputeFullU, and is n-by-m if you asked for #ComputeThinU.
*
* The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
*
* This method asserts that you asked for \a U to be computed.
*/
const MatrixUType& matrixU() const
{
eigen_assert(m_isInitialized && "SVD is not initialized.");
eigen_assert(computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
return m_matrixU;
}
/** \returns the \a V matrix.
*
* For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
* the V matrix is p-by-p if you asked for #ComputeFullV, and is p-by-m if you asked for ComputeThinV.
*
* The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
*
* This method asserts that you asked for \a V to be computed.
*/
const MatrixVType& matrixV() const
{
eigen_assert(m_isInitialized && "SVD is not initialized.");
eigen_assert(computeV() && "This SVD decomposition didn't compute V. Did you ask for it?");
return m_matrixV;
}
/** \returns the vector of singular values.
*
* For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
* returned vector has size \a m. Singular values are always sorted in decreasing order.
*/
const SingularValuesType& singularValues() const
{
eigen_assert(m_isInitialized && "SVD is not initialized.");
return m_singularValues;
}
/** \returns the number of singular values that are not exactly 0 */
Index nonzeroSingularValues() const
{
eigen_assert(m_isInitialized && "SVD is not initialized.");
return m_nonzeroSingularValues;
}
/** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
inline bool computeU() const { return m_computeFullU || m_computeThinU; }
/** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
inline bool computeV() const { return m_computeFullV || m_computeThinV; }
inline Index rows() const { return m_rows; }
inline Index cols() const { return m_cols; }
protected:
// return true if already allocated
bool allocate(Index rows, Index cols, unsigned int computationOptions) ;
MatrixUType m_matrixU;
MatrixVType m_matrixV;
SingularValuesType m_singularValues;
bool m_isInitialized, m_isAllocated;
bool m_computeFullU, m_computeThinU;
bool m_computeFullV, m_computeThinV;
unsigned int m_computationOptions;
Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize;
/** \brief Default Constructor.
*
* Default constructor of SVDBase
*/
SVDBase()
: m_isInitialized(false),
m_isAllocated(false),
m_computationOptions(0),
m_rows(-1), m_cols(-1)
{}
};
template<typename MatrixType>
bool SVDBase<MatrixType>::allocate(Index rows, Index cols, unsigned int computationOptions)
{
eigen_assert(rows >= 0 && cols >= 0);
if (m_isAllocated &&
rows == m_rows &&
cols == m_cols &&
computationOptions == m_computationOptions)
{
return true;
}
m_rows = rows;
m_cols = cols;
m_isInitialized = false;
m_isAllocated = true;
m_computationOptions = computationOptions;
m_computeFullU = (computationOptions & ComputeFullU) != 0;
m_computeThinU = (computationOptions & ComputeThinU) != 0;
m_computeFullV = (computationOptions & ComputeFullV) != 0;
m_computeThinV = (computationOptions & ComputeThinV) != 0;
eigen_assert(!(m_computeFullU && m_computeThinU) && "SVDBase: you can't ask for both full and thin U");
eigen_assert(!(m_computeFullV && m_computeThinV) && "SVDBase: you can't ask for both full and thin V");
eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) &&
"SVDBase: thin U and V are only available when your matrix has a dynamic number of columns.");
m_diagSize = (std::min)(m_rows, m_cols);
m_singularValues.resize(m_diagSize);
if(RowsAtCompileTime==Dynamic)
m_matrixU.resize(m_rows, m_computeFullU ? m_rows
: m_computeThinU ? m_diagSize
: 0);
if(ColsAtCompileTime==Dynamic)
m_matrixV.resize(m_cols, m_computeFullV ? m_cols
: m_computeThinV ? m_diagSize
: 0);
return false;
}
}// end namespace
#endif // EIGEN_SVD_H

4
unsupported/doc/examples/CMakeLists.txt
View File

@@ -10,12 +10,10 @@ FOREACH(example_src ${examples_SRCS})
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
target_link_libraries(example_${example} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
endif()
GET_TARGET_PROPERTY(example_executable
example_${example} LOCATION)
ADD_CUSTOM_COMMAND(
TARGET example_${example}
POST_BUILD
COMMAND ${example_executable}
COMMAND example_${example}
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${example}.out
)
ADD_DEPENDENCIES(unsupported_examples example_${example})

4
unsupported/doc/snippets/CMakeLists.txt
View File

@@ -14,12 +14,10 @@ FOREACH(snippet_src ${snippets_SRCS})
if(EIGEN_STANDARD_LIBRARIES_TO_LINK_TO)
target_link_libraries(${compile_snippet_target} ${EIGEN_STANDARD_LIBRARIES_TO_LINK_TO})
endif()
GET_TARGET_PROPERTY(compile_snippet_executable
${compile_snippet_target} LOCATION)
ADD_CUSTOM_COMMAND(
TARGET ${compile_snippet_target}
POST_BUILD
COMMAND ${compile_snippet_executable}
COMMAND ${compile_snippet_target}
ARGS >${CMAKE_CURRENT_BINARY_DIR}/${snippet}.out
)
ADD_DEPENDENCIES(unsupported_snippets ${compile_snippet_target})

22
unsupported/test/NonLinearOptimization.cpp
View File

@@ -1022,7 +1022,8 @@ void testNistLanczos1(void)
VERIFY_IS_EQUAL(lm.nfev, 79);
VERIFY_IS_EQUAL(lm.njev, 72);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.430899764097e-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
std::cout.precision(30);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4290986055242372e-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
// check x
VERIFY_IS_APPROX(x[0], 9.5100000027E-02);
VERIFY_IS_APPROX(x[1], 1.0000000001E+00);
@@ -1043,7 +1044,7 @@ void testNistLanczos1(void)
VERIFY_IS_EQUAL(lm.nfev, 9);
VERIFY_IS_EQUAL(lm.njev, 8);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.428595533845e-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.430571737783119393e-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
// check x
VERIFY_IS_APPROX(x[0], 9.5100000027E-02);
VERIFY_IS_APPROX(x[1], 1.0000000001E+00);
@@ -1262,8 +1263,8 @@ void testNistBoxBOD(void)
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev, 31);
VERIFY_IS_EQUAL(lm.njev, 25);
VERIFY(lm.nfev < 31); // 31
VERIFY(lm.njev < 25); // 25
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
// check x
@@ -1342,10 +1343,6 @@ void testNistMGH17(void)
lm.parameters.maxfev = 1000;
info = lm.minimize(x);
// check return value
VERIFY_IS_EQUAL(info, 2);
VERIFY_IS_EQUAL(lm.nfev, 602 );
VERIFY_IS_EQUAL(lm.njev, 545 );
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
// check x
@@ -1354,6 +1351,11 @@ void testNistMGH17(void)
VERIFY_IS_APPROX(x[2], -1.4646871366E+00);
VERIFY_IS_APPROX(x[3], 1.2867534640E-02);
VERIFY_IS_APPROX(x[4], 2.2122699662E-02);
// check return value
VERIFY_IS_EQUAL(info, 2);
VERIFY(lm.nfev < 650); // 602
VERIFY(lm.njev < 600); // 545
/*
* Second try
@@ -1832,8 +1834,8 @@ void test_NonLinearOptimization()
// NIST tests, level of difficulty = "Average"
CALL_SUBTEST/*_5*/(testNistHahn1());
CALL_SUBTEST/*_6*/(testNistMisra1d());
// CALL_SUBTEST/*_7*/(testNistMGH17());
// CALL_SUBTEST/*_8*/(testNistLanczos1());
CALL_SUBTEST/*_7*/(testNistMGH17());
CALL_SUBTEST/*_8*/(testNistLanczos1());
// // NIST tests, level of difficulty = "Higher"
CALL_SUBTEST/*_9*/(testNistRat42());

44
unsupported/test/levenberg_marquardt.cpp
View File

@@ -787,16 +787,17 @@ void testNistMGH10(void)
LevenbergMarquardt<MGH10_functor> lm(functor);
info = lm.minimize(x);
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev(), 284 );
VERIFY_IS_EQUAL(lm.njev(), 249 );
// check norm^2
VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7945855171E+01);
// check x
VERIFY_IS_APPROX(x[0], 5.6096364710E-03);
VERIFY_IS_APPROX(x[1], 6.1813463463E+03);
VERIFY_IS_APPROX(x[2], 3.4522363462E+02);
// check return value
//VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev(), 284 );
VERIFY_IS_EQUAL(lm.njev(), 249 );
/*
* Second try
@@ -805,16 +806,17 @@ void testNistMGH10(void)
// do the computation
info = lm.minimize(x);
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev(), 126);
VERIFY_IS_EQUAL(lm.njev(), 116);
// check norm^2
VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 8.7945855171E+01);
// check x
VERIFY_IS_APPROX(x[0], 5.6096364710E-03);
VERIFY_IS_APPROX(x[1], 6.1813463463E+03);
VERIFY_IS_APPROX(x[2], 3.4522363462E+02);
// check return value
//VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev(), 126);
VERIFY_IS_EQUAL(lm.njev(), 116);
}
@@ -866,15 +868,16 @@ void testNistBoxBOD(void)
lm.setFactor(10);
info = lm.minimize(x);
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev(), 31);
VERIFY_IS_EQUAL(lm.njev(), 25);
// check norm^2
VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 1.1680088766E+03);
// check x
VERIFY_IS_APPROX(x[0], 2.1380940889E+02);
VERIFY_IS_APPROX(x[1], 5.4723748542E-01);
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY(lm.nfev() < 31); // 31
VERIFY(lm.njev() < 25); // 25
/*
* Second try
@@ -948,10 +951,6 @@ void testNistMGH17(void)
lm.setMaxfev(1000);
info = lm.minimize(x);
// check return value
// VERIFY_IS_EQUAL(info, 2); //FIXME Use (lm.info() == Success)
// VERIFY_IS_EQUAL(lm.nfev(), 602 );
VERIFY_IS_EQUAL(lm.njev(), 545 );
// check norm^2
VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 5.4648946975E-05);
// check x
@@ -960,6 +959,11 @@ void testNistMGH17(void)
VERIFY_IS_APPROX(x[2], -1.4646871366E+00);
VERIFY_IS_APPROX(x[3], 1.2867534640E-02);
VERIFY_IS_APPROX(x[4], 2.2122699662E-02);
// check return value
// VERIFY_IS_EQUAL(info, 2); //FIXME Use (lm.info() == Success)
VERIFY(lm.nfev() < 700 ); // 602
VERIFY(lm.njev() < 600 ); // 545
/*
* Second try
@@ -1035,10 +1039,6 @@ void testNistMGH09(void)
lm.setMaxfev(1000);
info = lm.minimize(x);
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY_IS_EQUAL(lm.nfev(), 490 );
VERIFY_IS_EQUAL(lm.njev(), 376 );
// check norm^2
VERIFY_IS_APPROX(lm.fvec().squaredNorm(), 3.0750560385E-04);
// check x
@@ -1046,6 +1046,10 @@ void testNistMGH09(void)
VERIFY_IS_APPROX(x[1], 0.19126423573); // should be 1.9128232873E-01
VERIFY_IS_APPROX(x[2], 0.12305309914); // should be 1.2305650693E-01
VERIFY_IS_APPROX(x[3], 0.13605395375); // should be 1.3606233068E-01
// check return value
VERIFY_IS_EQUAL(info, 1);
VERIFY(lm.nfev() < 510 ); // 490
VERIFY(lm.njev() < 400 ); // 376
/*
* Second try

2
unsupported/test/svd_common.h
View File

@@ -18,7 +18,7 @@
#define EIGEN_RUNTIME_NO_MALLOC
#include "main.h"
#include <unsupported/Eigen/SVD>
#include <unsupported/Eigen/BDCSVD>
#include <Eigen/LU>