mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2025-02-23 18:20:47 +08:00
Code Issues Packages Projects Releases Wiki Activity

port unsupported modules to new API

Browse Source
This commit is contained in:
Gael Guennebaud 2010-01-05 15:38:20 +01:00
parent cab85218db
commit 39209edd71
9 changed files with 189 additions and 189 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

72
unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
View File

@ -31,16 +31,16 @@
/** \ingroup MatrixFunctions_Module
*
* \brief Compute the matrix exponential.
* \brief Compute the matrix exponential.
*
* \param M matrix whose exponential is to be computed.
* \param M matrix whose exponential is to be computed.
* \param result pointer to the matrix in which to store the result.
*
* The matrix exponential of \f$ M \f$ is defined by
* \f[ \exp(M) = \sum_{k=0}^\infty \frac{M^k}{k!}. \f]
* The matrix exponential can be used to solve linear ordinary
* differential equations: the solution of \f$ y' = My \f$ with the
* initial condition \f$ y(0) = y_0 \f$ is given by
* initial condition \f$ y(0) = y_0 \f$ is given by
* \f$ y(t) = \exp(M) y_0 \f$.
*
* The cost of the computation is approximately \f$ 20 n^3 \f$ for
@ -54,17 +54,17 @@
* squaring. The degree of the Padé approximant is chosen such
* that the approximation error is less than the round-off
* error. However, errors may accumulate during the squaring phase.
*
*
* Details of the algorithm can be found in: Nicholas J. Higham, "The
* scaling and squaring method for the matrix exponential revisited,"
* <em>SIAM J. %Matrix Anal. Applic.</em>, <b>26</b>:1179&ndash;1193,
* 2005.
* 2005.
*
* Example: The following program checks that
* \f[ \exp \left[ \begin{array}{ccc}
* 0 & \frac14\pi & 0 \\
* \f[ \exp \left[ \begin{array}{ccc}
* 0 & \frac14\pi & 0 \\
* -\frac14\pi & 0 & 0 \\
* 0 & 0 & 0
* 0 & 0 & 0
* \end{array} \right] = \left[ \begin{array}{ccc}
* \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
* \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
@ -76,11 +76,11 @@
* \include MatrixExponential.cpp
* Output: \verbinclude MatrixExponential.out
*
* \note \p M has to be a matrix of \c float, \c double,
* \note \p M has to be a matrix of \c float, \c double,
* \c complex<float> or \c complex<double> .
*/
template <typename Derived>
EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M,
EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M,
typename MatrixBase<Derived>::PlainMatrixType* result);
/** \ingroup MatrixFunctions_Module
@ -90,13 +90,13 @@ template <typename MatrixType>
class MatrixExponential {
public:
/** \brief Compute the matrix exponential.
/** \brief Compute the matrix exponential.
*
* \param M matrix whose exponential is to be computed.
* \param M matrix whose exponential is to be computed.
* \param result pointer to the matrix in which to store the result.
*/
MatrixExponential(const MatrixType &M, MatrixType *result);
MatrixExponential(const MatrixType &M, MatrixType *result);
private:
@ -105,7 +105,7 @@ class MatrixExponential {
MatrixExponential& operator=(const MatrixExponential&);
/** \brief Compute the (3,3)-Pad&eacute; approximant to the exponential.
*
*
* After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
* approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
*
@ -114,7 +114,7 @@ class MatrixExponential {
void pade3(const MatrixType &A);
/** \brief Compute the (5,5)-Pad&eacute; approximant to the exponential.
*
*
* After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
* approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
*
@ -123,7 +123,7 @@ class MatrixExponential {
void pade5(const MatrixType &A);
/** \brief Compute the (7,7)-Pad&eacute; approximant to the exponential.
*
*
* After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
* approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
*
@ -132,7 +132,7 @@ class MatrixExponential {
void pade7(const MatrixType &A);
/** \brief Compute the (9,9)-Pad&eacute; approximant to the exponential.
*
*
* After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
* approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
*
@ -141,7 +141,7 @@ class MatrixExponential {
void pade9(const MatrixType &A);
/** \brief Compute the (13,13)-Pad&eacute; approximant to the exponential.
*
*
* After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
* approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
*
@ -149,10 +149,10 @@ class MatrixExponential {
*/
void pade13(const MatrixType &A);
/** \brief Compute Pad&eacute; approximant to the exponential.
*
* Computes \c m_U, \c m_V and \c m_squarings such that
* \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute; of
/** \brief Compute Pad&eacute; approximant to the exponential.
*
* Computes \c m_U, \c m_V and \c m_squarings such that
* \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute; of
* \f$ \exp(2^{-\mbox{squarings}}M) \f$ around \f$ M = 0 \f$. The
* degree of the Pad&eacute; approximant and the value of
* squarings are chosen such that the approximation error is no
@ -164,7 +164,7 @@ class MatrixExponential {
*/
void computeUV(double);
/** \brief Compute Pad&eacute; approximant to the exponential.
/** \brief Compute Pad&eacute; approximant to the exponential.
*
* \sa computeUV(double);
*/
@ -174,7 +174,7 @@ class MatrixExponential {
typedef typename NumTraits<typename ei_traits<MatrixType>::Scalar>::Real RealScalar;
/** \brief Pointer to matrix whose exponential is to be computed. */
const MatrixType* m_M;
const MatrixType* m_M;
/** \brief Even-degree terms in numerator of Pad&eacute; approximant. */
MatrixType m_U;
@ -200,14 +200,14 @@ class MatrixExponential {
template <typename MatrixType>
MatrixExponential<MatrixType>::MatrixExponential(const MatrixType &M, MatrixType *result) :
m_M(&M),
m_U(M.rows(),M.cols()),
m_V(M.rows(),M.cols()),
m_tmp1(M.rows(),M.cols()),
m_tmp2(M.rows(),M.cols()),
m_Id(MatrixType::Identity(M.rows(), M.cols())),
m_squarings(0),
m_l1norm(static_cast<float>(M.cwise().abs().colwise().sum().maxCoeff()))
m_M(&M),
m_U(M.rows(),M.cols()),
m_V(M.rows(),M.cols()),
m_tmp1(M.rows(),M.cols()),
m_tmp2(M.rows(),M.cols()),
m_Id(MatrixType::Identity(M.rows(), M.cols())),
m_squarings(0),
m_l1norm(static_cast<float>(M.cwiseAbs().colwise().sum().maxCoeff()))
{
computeUV(RealScalar());
m_tmp1 = m_U + m_V; // numerator of Pade approximant
@ -267,8 +267,8 @@ EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade9(const MatrixType &
template <typename MatrixType>
EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade13(const MatrixType &A)
{
const Scalar b[] = {64764752532480000., 32382376266240000., 7771770303897600.,
1187353796428800., 129060195264000., 10559470521600., 670442572800.,
const Scalar b[] = {64764752532480000., 32382376266240000., 7771770303897600.,
1187353796428800., 129060195264000., 10559470521600., 670442572800.,
33522128640., 1323241920., 40840800., 960960., 16380., 182., 1.};
MatrixType A2 = A * A;
MatrixType A4 = A2 * A2;
@ -317,7 +317,7 @@ void MatrixExponential<MatrixType>::computeUV(double)
}
template <typename Derived>
EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M,
EIGEN_STRONG_INLINE void ei_matrix_exponential(const MatrixBase<Derived> &M,
typename MatrixBase<Derived>::PlainMatrixType* result)
{
ei_assert(M.rows() == M.cols());

16
unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
View File

@ -25,7 +25,7 @@
#ifndef EIGEN_MATRIX_FUNCTION_ATOMIC
#define EIGEN_MATRIX_FUNCTION_ATOMIC
/** \ingroup MatrixFunctions_Module
/** \ingroup MatrixFunctions_Module
* \class MatrixFunctionAtomic
* \brief Helper class for computing matrix functions of atomic matrices.
*
@ -110,30 +110,30 @@ void MatrixFunctionAtomic<MatrixType>::computeMu()
const MatrixType N = MatrixType::Identity(m_Arows, m_Arows) - m_Ashifted;
VectorType e = VectorType::Ones(m_Arows);
N.template triangularView<UpperTriangular>().solveInPlace(e);
m_mu = e.cwise().abs().maxCoeff();
m_mu = e.cwiseAbs().maxCoeff();
}
/** \brief Determine whether Taylor series has converged */
template <typename MatrixType>
bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F,
bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F,
const MatrixType& Fincr, const MatrixType& P)
{
const int n = F.rows();
const RealScalar F_norm = F.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
if (Fincr_norm < epsilon<Scalar>() * F_norm) {
RealScalar delta = 0;
RealScalar rfactorial = 1;
for (int r = 0; r < n; r++) {
RealScalar mx = 0;
for (int i = 0; i < n; i++)
for (int i = 0; i < n; i++)
mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
if (r != 0)
rfactorial *= r;
delta = std::max(delta, mx / rfactorial);
}
const RealScalar P_norm = P.cwise().abs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm)
const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm)
return true;
}
return false;

22
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
View File

@ -36,7 +36,7 @@
*
* The user must provide a subroutine which calculates the
* functions. The Jacobian is either provided by the user, or approximated
* using a forward-difference method.
* using a forward-difference method.
*
*/
template<typename FunctorType, typename Scalar=double>
@ -50,7 +50,7 @@ public:
Running = -1,
ImproperInputParameters = 0,
RelativeErrorTooSmall = 1,
TooManyFunctionEvaluation = 2,
TooManyFunctionEvaluation = 2,
TolTooSmall = 3,
NotMakingProgressJacobian = 4,
NotMakingProgressIterations = 5,
@ -156,7 +156,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
parameters.xtol = tol;
diag.setConstant(n, 1.);
return solve(
x,
x,
2
);
}
@ -241,7 +241,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -285,7 +285,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* Computing MAX */
if (mode != 2)
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
@ -299,7 +299,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -364,7 +364,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;
@ -555,7 +555,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -599,7 +599,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* Computing MAX */
if (mode != 2)
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
@ -613,7 +613,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -678,7 +678,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;

26
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
View File

@ -37,7 +37,7 @@
* http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
*/
template<typename FunctorType, typename Scalar=double>
class LevenbergMarquardt
class LevenbergMarquardt
{
public:
LevenbergMarquardt(FunctorType &_functor)
@ -50,7 +50,7 @@ public:
RelativeErrorTooSmall = 2,
RelativeErrorAndReductionTooSmall = 3,
CosinusTooSmall = 4,
TooManyFunctionEvaluation = 5,
TooManyFunctionEvaluation = 5,
FtolTooSmall = 6,
XtolTooSmall = 7,
GtolTooSmall = 8,
@ -253,7 +253,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
wa2 = fjac.colwise().blueNorm();
ei_qrfac<Scalar>(m, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
ipvt.array() -= 1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
/* on the first iteration and if mode is 1, scale according */
/* to the norms of the columns of the initial jacobian. */
@ -269,7 +269,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -316,7 +316,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
/* rescale if necessary. */
if (mode != 2) /* Computing MAX */
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
do {
@ -329,7 +329,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -395,7 +395,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;
@ -538,10 +538,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
wa2[j] = fjac.col(j).head(j).stableNorm();
}
if (sing) {
ipvt.cwise()+=1;
ipvt.array() += 1;
wa2 = fjac.colwise().blueNorm();
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), true, ipvt.data(), wa1.data());
ipvt.cwise()-=1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
ipvt.array() -= 1; // qrfac() creates ipvt with fortran convention (1->n), convert it to c (0->n-1)
for (j = 0; j < n; ++j) {
if (fjac(j,j) != 0.) {
sum = 0.;
@ -569,7 +569,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
/* on the first iteration, calculate the norm of the scaled x */
/* and initialize the step bound delta. */
wa3 = diag.cwise() * x;
wa3 = diag.cwiseProduct(x);
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
if (delta == 0.)
@ -599,7 +599,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
/* rescale if necessary. */
if (mode != 2) /* Computing MAX */
diag = diag.cwise().max(wa2);
diag = diag.cwiseMax(wa2);
/* beginning of the inner loop. */
do {
@ -612,7 +612,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
wa1 = -wa1;
wa2 = x + wa1;
wa3 = diag.cwise() * wa1;
wa3 = diag.cwiseProduct(wa1);
pnorm = wa3.stableNorm();
/* on the first iteration, adjust the initial step bound. */
@ -678,7 +678,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
if (ratio >= Scalar(1e-4)) {
/* successful iteration. update x, fvec, and their norms. */
x = wa2;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
fvec = wa4;
xnorm = wa2.stableNorm();
fnorm = fnorm1;

4
unsupported/Eigen/src/NonLinearOptimization/dogleg.h
View File

@ -50,7 +50,7 @@ void ei_dogleg(
/* test whether the gauss-newton direction is acceptable. */
wa1.fill(0.);
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
qnorm = wa2.stableNorm();
if (qnorm <= delta)
return;
@ -80,7 +80,7 @@ void ei_dogleg(
/* calculate the point along the scaled gradient */
/* at which the quadratic is minimized. */
wa1.cwise() /= diag*gnorm;
wa1.array() /= (diag*gnorm).array();
l = 0;
for (j = 0; j < n; ++j) {
sum = 0.;

8
unsupported/Eigen/src/NonLinearOptimization/lmpar.h
View File

@ -36,7 +36,7 @@ void ei_lmpar(
for (j = 0; j < n; ++j) {
if (r(j,j) == 0. && nsing == n-1)
nsing = j - 1;
if (nsing < n-1)
if (nsing < n-1)
wa1[j] = 0.;
}
for (j = nsing; j>=0; --j) {
@ -54,7 +54,7 @@ void ei_lmpar(
/* for acceptance of the gauss-newton direction. */
iter = 0;
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
dxnorm = wa2.blueNorm();
fp = dxnorm - delta;
if (fp <= Scalar(0.1) * delta) {
@ -76,7 +76,7 @@ void ei_lmpar(
// way:
for (j = 0; j < n; ++j) {
Scalar sum = 0.;
for (i = 0; i < j; ++i)
for (i = 0; i < j; ++i)
sum += r(i,j) * wa1[i];
wa1[j] = (wa1[j] - sum) / r(j,j);
}
@ -117,7 +117,7 @@ void ei_lmpar(
Matrix< Scalar, Dynamic, 1 > sdiag(n);
ei_qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
wa2 = diag.cwise() * x;
wa2 = diag.cwiseProduct(x);
dxnorm = wa2.blueNorm();
temp = fp;
fp = dxnorm - delta;

2
unsupported/test/BVH.cpp
View File

@ -45,7 +45,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(double, Dim)
template<typename Scalar, int Dim> AlignedBox<Scalar, Dim> ei_bounding_box(const Matrix<Scalar, Dim, 1> &v) { return AlignedBox<Scalar, Dim>(v); }
template<int Dim> AlignedBox<double, Dim> ei_bounding_box(const Ball<Dim> &b)
{ return AlignedBox<double, Dim>(b.center.cwise() - b.radius, b.center.cwise() + b.radius); }
{ return AlignedBox<double, Dim>(b.center.array() - b.radius, b.center.array() + b.radius); }
template<int Dim>

216
unsupported/test/NonLinearOptimization.cpp
View File

@ -20,10 +20,10 @@ int fcn_chkder(const VectorXd &x, VectorXd &fvec, MatrixXd &fjac, int iflag)
3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39};
if (iflag == 0)
if (iflag == 0)
return 0;
if (iflag != 2)
if (iflag != 2)
for (i=0; i<15; i++) {
tmp1 = i+1;
tmp2 = 16-i-1;
@ -108,12 +108,12 @@ struct Functor
typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
int m_inputs, m_values;
Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
int inputs() const { return m_inputs; }
int values() const { return m_values; }
@ -219,7 +219,7 @@ void testLmder()
ei_covar(lm.fjac, lm.ipvt); // TODO : move this as a function of lm
MatrixXd cov_ref(n,n);
cov_ref <<
cov_ref <<
0.0001531202, 0.002869941, -0.002656662,
0.002869941, 0.09480935, -0.09098995,
-0.002656662, -0.09098995, 0.08778727;
@ -229,7 +229,7 @@ void testLmder()
MatrixXd cov;
cov = covfac*lm.fjac.corner<n,n>(TopLeft);
VERIFY_IS_APPROX( cov, cov_ref);
// TODO: why isn't this allowed ? :
// TODO: why isn't this allowed ? :
// VERIFY_IS_APPROX( covfac*fjac.corner<n,n>(TopLeft) , cov_ref);
}
@ -296,7 +296,7 @@ void testHybrj1()
// check x
VectorXd x_ref(n);
x_ref <<
x_ref <<
-0.5706545, -0.6816283, -0.7017325,
-0.7042129, -0.701369, -0.6918656,
-0.665792, -0.5960342, -0.4164121;
@ -330,7 +330,7 @@ void testHybrj()
// check x
VectorXd x_ref(n);
x_ref <<
x_ref <<
-0.5706545, -0.6816283, -0.7017325,
-0.7042129, -0.701369, -0.6918656,
-0.665792, -0.5960342, -0.4164121;
@ -412,7 +412,7 @@ void testHybrd()
// check x
VectorXd x_ref(n);
x_ref <<
x_ref <<
-0.5706545, -0.6816283, -0.7017325,
-0.7042129, -0.701369, -0.6918656,
-0.665792, -0.5960342, -0.4164121;
@ -608,7 +608,7 @@ void testLmdif()
ei_covar(lm.fjac, lm.ipvt);
MatrixXd cov_ref(n,n);
cov_ref <<
cov_ref <<
0.0001531202, 0.002869942, -0.002656662,
0.002869942, 0.09480937, -0.09098997,
-0.002656662, -0.09098997, 0.08778729;
@ -618,7 +618,7 @@ void testLmdif()
MatrixXd cov;
cov = covfac*lm.fjac.corner<n,n>(TopLeft);
VERIFY_IS_APPROX( cov, cov_ref);
// TODO: why isn't this allowed ? :
// TODO: why isn't this allowed ? :
// VERIFY_IS_APPROX( covfac*fjac.corner<n,n>(TopLeft) , cov_ref);
}
@ -676,11 +676,11 @@ void testNistChwirut2(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 10 == lm.nfev);
VERIFY( 8 == lm.njev);
VERIFY( 1 == info);
VERIFY( 10 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
// check x
VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
@ -697,11 +697,11 @@ void testNistChwirut2(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 7 == lm.nfev);
VERIFY( 6 == lm.njev);
VERIFY( 1 == info);
VERIFY( 7 == lm.nfev);
VERIFY( 6 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
// check x
VERIFY_IS_APPROX(x[0], 1.6657666537E-01);
VERIFY_IS_APPROX(x[1], 5.1653291286E-03);
@ -756,11 +756,11 @@ void testNistMisra1a(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 19 == lm.nfev);
VERIFY( 15 == lm.njev);
VERIFY( 1 == info);
VERIFY( 19 == lm.nfev);
VERIFY( 15 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
// check x
VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
@ -773,11 +773,11 @@ void testNistMisra1a(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 5 == lm.nfev);
VERIFY( 4 == lm.njev);
VERIFY( 1 == info);
VERIFY( 5 == lm.nfev);
VERIFY( 4 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
// check x
VERIFY_IS_APPROX(x[0], 2.3894212918E+02);
VERIFY_IS_APPROX(x[1], 5.5015643181E-04);
@ -842,11 +842,11 @@ void testNistHahn1(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 11== lm.nfev);
VERIFY( 10== lm.njev);
VERIFY( 1 == info);
VERIFY( 11== lm.nfev);
VERIFY( 10== lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
// check x
VERIFY_IS_APPROX(x[0], 1.0776351733E+00 );
VERIFY_IS_APPROX(x[1],-1.2269296921E-01 );
@ -864,18 +864,18 @@ void testNistHahn1(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 11 == lm.nfev);
VERIFY( 10 == lm.njev);
VERIFY( 1 == info);
VERIFY( 11 == lm.nfev);
VERIFY( 10 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
// check x
VERIFY_IS_APPROX(x[0], 1.077640); // should be : 1.0776351733E+00
VERIFY_IS_APPROX(x[1], -0.1226933); // should be : -1.2269296921E-01
VERIFY_IS_APPROX(x[2], 0.004086383); // should be : 4.0863750610E-03
VERIFY_IS_APPROX(x[3], -1.426277e-06); // shoulde be : -1.4262662514E-06
VERIFY_IS_APPROX(x[4],-5.7609940901E-03 );
VERIFY_IS_APPROX(x[5], 0.00024053772); // should be : 2.4053735503E-04
VERIFY_IS_APPROX(x[5], 0.00024053772); // should be : 2.4053735503E-04
VERIFY_IS_APPROX(x[6], -1.231450e-07); // should be : -1.2314450199E-07
}
@ -928,11 +928,11 @@ void testNistMisra1d(void)
info = lm.minimize(x);
// check return value
VERIFY( 3 == info);
VERIFY( 9 == lm.nfev);
VERIFY( 7 == lm.njev);
VERIFY( 3 == info);
VERIFY( 9 == lm.nfev);
VERIFY( 7 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
// check x
VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
@ -945,11 +945,11 @@ void testNistMisra1d(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 4 == lm.nfev);
VERIFY( 3 == lm.njev);
VERIFY( 1 == info);
VERIFY( 4 == lm.nfev);
VERIFY( 3 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
// check x
VERIFY_IS_APPROX(x[0], 4.3736970754E+02);
VERIFY_IS_APPROX(x[1], 3.0227324449E-04);
@ -1006,9 +1006,9 @@ void testNistLanczos1(void)
info = lm.minimize(x);
// check return value
VERIFY( 2 == info);
VERIFY( 79 == lm.nfev);
VERIFY( 72 == lm.njev);
VERIFY( 2 == info);
VERIFY( 79 == lm.nfev);
VERIFY( 72 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.429604433690E-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
// check x
@ -1027,9 +1027,9 @@ void testNistLanczos1(void)
info = lm.minimize(x);
// check return value
VERIFY( 2 == info);
VERIFY( 9 == lm.nfev);
VERIFY( 8 == lm.njev);
VERIFY( 2 == info);
VERIFY( 9 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.43049947737308E-25); // should be 1.4307867721E-25, but nist results are on 128-bit floats
// check x
@ -1092,9 +1092,9 @@ void testNistRat42(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 10 == lm.nfev);
VERIFY( 8 == lm.njev);
VERIFY( 1 == info);
VERIFY( 10 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
// check x
@ -1110,9 +1110,9 @@ void testNistRat42(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 6 == lm.nfev);
VERIFY( 5 == lm.njev);
VERIFY( 1 == info);
VERIFY( 6 == lm.nfev);
VERIFY( 5 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
// check x
@ -1170,9 +1170,9 @@ void testNistMGH10(void)
info = lm.minimize(x);
// check return value
VERIFY( 2 == info);
VERIFY( 285 == lm.nfev);
VERIFY( 250 == lm.njev);
VERIFY( 2 == info);
VERIFY( 285 == lm.nfev);
VERIFY( 250 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
// check x
@ -1188,9 +1188,9 @@ void testNistMGH10(void)
info = lm.minimize(x);
// check return value
VERIFY( 2 == info);
VERIFY( 126 == lm.nfev);
VERIFY( 116 == lm.njev);
VERIFY( 2 == info);
VERIFY( 126 == lm.nfev);
VERIFY( 116 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
// check x
@ -1249,9 +1249,9 @@ void testNistBoxBOD(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 31 == lm.nfev);
VERIFY( 25 == lm.njev);
VERIFY( 1 == info);
VERIFY( 31 == lm.nfev);
VERIFY( 25 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
// check x
@ -1269,9 +1269,9 @@ void testNistBoxBOD(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 15 == lm.nfev);
VERIFY( 14 == lm.njev);
VERIFY( 1 == info);
VERIFY( 15 == lm.nfev);
VERIFY( 14 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
// check x
@ -1288,7 +1288,7 @@ struct MGH17_functor : Functor<double>
{
assert(b.size()==5);
assert(fvec.size()==33);
for(int i=0; i<33; i++)
for(int i=0; i<33; i++)
fvec[i] = b[0] + b[1]*exp(-b[3]*x[i]) + b[2]*exp(-b[4]*x[i]) - y[i];
return 0;
}
@ -1331,9 +1331,9 @@ void testNistMGH17(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 599 == lm.nfev);
VERIFY( 544 == lm.njev);
VERIFY( 1 == info);
VERIFY( 599 == lm.nfev);
VERIFY( 544 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
// check x
@ -1352,9 +1352,9 @@ void testNistMGH17(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 18 == lm.nfev);
VERIFY( 15 == lm.njev);
VERIFY( 1 == info);
VERIFY( 18 == lm.nfev);
VERIFY( 15 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
// check x
@ -1418,9 +1418,9 @@ void testNistMGH09(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 503== lm.nfev);
VERIFY( 385 == lm.njev);
VERIFY( 1 == info);
VERIFY( 503== lm.nfev);
VERIFY( 385 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
// check x
@ -1438,9 +1438,9 @@ void testNistMGH09(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 18 == lm.nfev);
VERIFY( 16 == lm.njev);
VERIFY( 1 == info);
VERIFY( 18 == lm.nfev);
VERIFY( 16 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
// check x
@ -1501,9 +1501,9 @@ void testNistBennett5(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 758 == lm.nfev);
VERIFY( 744 == lm.njev);
VERIFY( 1 == info);
VERIFY( 758 == lm.nfev);
VERIFY( 744 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
// check x
@ -1519,9 +1519,9 @@ void testNistBennett5(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 203 == lm.nfev);
VERIFY( 192 == lm.njev);
VERIFY( 1 == info);
VERIFY( 203 == lm.nfev);
VERIFY( 192 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
// check x
@ -1589,11 +1589,11 @@ void testNistThurber(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 39 == lm.nfev);
VERIFY( 36== lm.njev);
VERIFY( 1 == info);
VERIFY( 39 == lm.nfev);
VERIFY( 36== lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
// check x
VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
@ -1614,11 +1614,11 @@ void testNistThurber(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 29 == lm.nfev);
VERIFY( 28 == lm.njev);
VERIFY( 1 == info);
VERIFY( 29 == lm.nfev);
VERIFY( 28 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
// check x
VERIFY_IS_APPROX(x[0], 1.2881396800E+03);
VERIFY_IS_APPROX(x[1], 1.4910792535E+03);
@ -1681,9 +1681,9 @@ void testNistRat43(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 27 == lm.nfev);
VERIFY( 20 == lm.njev);
VERIFY( 1 == info);
VERIFY( 27 == lm.nfev);
VERIFY( 20 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
// check x
@ -1703,9 +1703,9 @@ void testNistRat43(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 9 == lm.nfev);
VERIFY( 8 == lm.njev);
VERIFY( 1 == info);
VERIFY( 9 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
// check x
@ -1766,9 +1766,9 @@ void testNistEckerle4(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 18 == lm.nfev);
VERIFY( 15 == lm.njev);
VERIFY( 1 == info);
VERIFY( 18 == lm.nfev);
VERIFY( 15 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
// check x
@ -1784,9 +1784,9 @@ void testNistEckerle4(void)
info = lm.minimize(x);
// check return value
VERIFY( 1 == info);
VERIFY( 7 == lm.nfev);
VERIFY( 6 == lm.njev);
VERIFY( 1 == info);
VERIFY( 7 == lm.nfev);
VERIFY( 6 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
// check x

12
unsupported/test/matrix_exponential.cpp
View File

@ -25,7 +25,7 @@
#include "main.h"
#include <unsupported/Eigen/MatrixFunctions>
double binom(int n, int k)
double binom(int n, int k)
{
double res = 1;
for (int i=0; i<k; i++)
@ -36,7 +36,7 @@ double binom(int n, int k)
template <typename Derived, typename OtherDerived>
double relerr(const MatrixBase<Derived>& A, const MatrixBase<OtherDerived>& B)
{
return std::sqrt((A - B).cwise().abs2().sum() / std::min(A.cwise().abs2().sum(), B.cwise().abs2().sum()));
return std::sqrt((A - B).cwiseAbs2().sum() / std::min(A.cwiseAbs2().sum(), B.cwiseAbs2().sum()));
}
template <typename T>
@ -52,7 +52,7 @@ void test2dRotation(double tol)
T angle;
A << 0, 1, -1, 0;
for (int i=0; i<=20; i++)
for (int i=0; i<=20; i++)
{
angle = static_cast<T>(pow(10, i / 5. - 2));
B << cos(angle), sin(angle), -sin(angle), cos(angle);
@ -74,7 +74,7 @@ void test2dHyperbolicRotation(double tol)
std::complex<T> imagUnit(0,1);
T angle, ch, sh;
for (int i=0; i<=20; i++)
for (int i=0; i<=20; i++)
{
angle = static_cast<T>((i-10) / 2.0);
ch = std::cosh(angle);
@ -116,7 +116,7 @@ void testPascal(double tol)
}
}
template<typename MatrixType>
template<typename MatrixType>
void randomTest(const MatrixType& m, double tol)
{
/* this test covers the following files:
@ -157,7 +157,7 @@ void test_matrix_exponential()
CALL_SUBTEST_3(randomTest(Matrix4cd(), 1e-13));
CALL_SUBTEST_4(randomTest(MatrixXd(8,8), 1e-13));
CALL_SUBTEST_1(randomTest(Matrix2f(), 1e-4));
CALL_SUBTEST_5(randomTest(Matrix3cf(), 1e-4));
CALL_SUBTEST_5(randomTest(Matrix3cf(), 1e-4));
CALL_SUBTEST_1(randomTest(Matrix4f(), 1e-4));
CALL_SUBTEST_6(randomTest(MatrixXf(8,8), 1e-4));
}