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get rid of ei_qform + lot of other cleaning, now that we do not depend on

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minpack qr factorization.
This commit is contained in:
Thomas Capricelli
2010-01-26 08:42:48 +01:00
parent ba2a9cce03
commit 113995355b
2 changed files with 10 additions and 101 deletions
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51
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
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@@ -217,7 +217,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
const int mode
)
{
int i, j;
int j;
jeval = true;
/* calculate the jacobian matrix. */
@@ -246,30 +246,15 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* compute the qr factorization of the jacobian. */
wa2 = fjac.colwise().blueNorm();
HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
fjac = qrfac.matrixQR();
wa1 = fjac.diagonal();
fjac.diagonal() = qrfac.hCoeffs();
// TODO : avoid this:
for(int ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
for (j = 0; j < n; ++j)
if (fjac(j,j) != 0.) {
sum = 0.;
for (i = j; i < n; ++i)
sum += fjac(i,j) * qtf[i];
temp = -sum / fjac(j,j);
for (i = j; i < n; ++i)
qtf[i] += fjac(i,j) * temp;
}
/* copy the triangular factor of the qr factorization into r. */
R = qrfac.matrixQR();
sing = wa1.cwiseAbs().minCoeff()==0.;
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
fjac = qrfac.householderQ();
/* form (q transpose)*fvec and store in qtf. */
qtf = fjac.transpose() * fvec;
/* rescale if necessary. */
if (mode != 2)
@@ -480,13 +465,12 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
const int mode
)
{
int i, j;
int j;
jeval = true;
if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
/* calculate the jacobian matrix. */
if (ei_fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
return UserAksed;
nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
@@ -512,30 +496,15 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* compute the qr factorization of the jacobian. */
wa2 = fjac.colwise().blueNorm();
HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
fjac = qrfac.matrixQR();
wa1 = fjac.diagonal();
fjac.diagonal() = qrfac.hCoeffs();
// TODO : avoid this:
for(int ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
for (j = 0; j < n; ++j)
if (fjac(j,j) != 0.) {
sum = 0.;
for (i = j; i < n; ++i)
sum += fjac(i,j) * qtf[i];
temp = -sum / fjac(j,j);
for (i = j; i < n; ++i)
qtf[i] += fjac(i,j) * temp;
}
/* copy the triangular factor of the qr factorization into r. */
R = qrfac.matrixQR();
sing = wa1.cwiseAbs().minCoeff()==0.;
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
fjac = qrfac.householderQ();
/* form (q transpose)*fvec and store in qtf. */
qtf = fjac.transpose() * fvec;
/* rescale if necessary. */
if (mode != 2)