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computes column norms outside of ei_qrfac()

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Thomas Capricelli
2009-11-26 02:53:58 +01:00
parent 9cbfdbad22
commit 746c787a76
3 changed files with 27 additions and 36 deletions
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42
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
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@@ -226,15 +226,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
return UserAksed;
++njev;
/* compute the qr factorization of the jacobian. */
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data(), wa2.data());
wa2 = fjac.colwise().blueNorm();
/* on the first iteration and if mode is 1, scale according */
/* to the norms of the columns of the initial jacobian. */
if (iter == 1) {
/* on the first iteration and if mode is 1, scale according */
/* to the norms of the columns of the initial jacobian. */
if (mode != 2)
for (j = 0; j < n; ++j) {
diag[j] = wa2[j];
@@ -251,6 +247,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
delta = parameters.factor;
}
/* compute the qr factorization of the jacobian. */
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data());
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
@@ -269,18 +268,16 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
sing = false;
for (j = 0; j < n; ++j) {
l = j;
if (j)
for (i = 0; i < j; ++i) {
R[l] = fjac(i,j);
l = l + n - i -1;
}
for (i = 0; i < j; ++i) {
R[l] = fjac(i,j);
l = l + n - i -1;
}
R[l] = wa1[j];
if (wa1[j] == 0.)
sing = true;
}
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
/* rescale if necessary. */
@@ -543,13 +540,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
return UserAksed;
nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
/* compute the qr factorization of the jacobian. */
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data(), wa2.data());
wa2 = fjac.colwise().blueNorm();
/* on the first iteration and if mode is 1, scale according */
/* to the norms of the columns of the initial jacobian. */
if (iter == 1) {
if (mode != 2)
for (j = 0; j < n; ++j) {
@@ -560,7 +554,6 @@ 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;
xnorm = wa3.stableNorm();
delta = parameters.factor * xnorm;
@@ -568,6 +561,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
delta = parameters.factor;
}
/* compute the qr factorization of the jacobian. */
ei_qrfac<Scalar>(n, n, fjac.data(), fjac.rows(), false, iwa, wa1.data());
/* form (q transpose)*fvec and store in qtf. */
qtf = fvec;
@@ -586,18 +582,16 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
sing = false;
for (j = 0; j < n; ++j) {
l = j;
if (j)
for (i = 0; i < j; ++i) {
R[l] = fjac(i,j);
l = l + n - i -1;
}
for (i = 0; i < j; ++i) {
R[l] = fjac(i,j);
l = l + n - i -1;
}
R[l] = wa1[j];
if (wa1[j] == 0.)
sing = true;
}
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
/* rescale if necessary. */