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use a plain matrix to store the upper triangular matrix 'R', instead

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of the "compact inside a vector" scheme used by fortran/minpack.
The most difficult part is to fix all related code. Tests pass.
This commit is contained in:
Thomas Capricelli
2010-01-26 05:59:58 +01:00
parent 4b859c8554
commit 91561cded4
3 changed files with 48 additions and 116 deletions
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61
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
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@@ -74,6 +74,8 @@ public:
};
typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
/* TODO: if eigen provides a triangular storage, use it here */
typedef Matrix< Scalar, Dynamic, Dynamic > UpperTriangularType;
Status hybrj1(
FVectorType &x,
@@ -113,8 +115,9 @@ public:
void resetParameters(void) { parameters = Parameters(); }
Parameters parameters;
FVectorType fvec, R, qtf, diag;
FVectorType fvec, qtf, diag;
JacobianType fjac;
UpperTriangularType R;
int nfev;
int njev;
int iter;
@@ -173,7 +176,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
fvec.resize(n);
qtf.resize(n);
R.resize( (n*(n+1))/2);
fjac.resize(n, n);
if (mode != 2)
diag.resize(n);
@@ -218,7 +220,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
const int mode
)
{
int i, j, l;
int i, j;
jeval = true;
/* calculate the jacobian matrix. */
@@ -272,17 +274,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* copy the triangular factor of the qr factorization into r. */
R = qrfac.matrixQR();
sing = false;
for (j = 0; j < n; ++j) {
l = j;
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;
}
for (j = 0; j < n; ++j)
if (wa1[j] == 0.) sing = true;
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
@@ -328,13 +323,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* compute the scaled predicted reduction. */
l = 0;
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = i; j < n; ++j) {
sum += R[l] * wa1[j];
++l;
}
for (j = i; j < n; ++j)
sum += R(i,j) * wa1[j];
wa3[i] = qtf[i] + sum;
}
temp = wa3.stableNorm();
@@ -421,7 +413,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(n, n, R.data(), R.size(), wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1updt<Scalar>(n, n, R, wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), fjac.rows(), wa2.data(), wa3.data());
ei_r1mpyq<Scalar>(1, n, qtf.data(), 1, wa2.data(), wa3.data());
@@ -488,7 +480,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
qtf.resize(n);
R.resize( (n*(n+1))/2);
fjac.resize(n, n);
fvec.resize(n);
if (mode != 2)
@@ -536,7 +527,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
const int mode
)
{
int i, j, l;
int i, 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;
@@ -591,26 +582,13 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
}
/* copy the triangular factor of the qr factorization into r. */
R = qrfac.matrixQR();
sing = false;
for (j = 0; j < n; ++j) {
l = j;
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;
}
for (j = 0; j < n; ++j)
if (wa1[j] == 0.) sing = true;
/* accumulate the orthogonal factor in fjac. */
ei_qform<Scalar>(n, n, fjac.data(), fjac.rows(), wa1.data());
#if 0
std::cout << "ei_qform<Scalar>: " << fjac << std::endl;
fjac = qrfac.matrixQ();
std::cout << "qrfac.matrixQ():" << fjac << std::endl;
#endif
/* rescale if necessary. */
@@ -653,13 +631,10 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* compute the scaled predicted reduction. */
l = 0;
for (i = 0; i < n; ++i) {
sum = 0.;
for (j = i; j < n; ++j) {
sum += R[l] * wa1[j];
++l;
}
for (j = i; j < n; ++j)
sum += R(i,j) * wa1[j];
wa3[i] = qtf[i] + sum;
}
temp = wa3.stableNorm();
@@ -747,7 +722,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(n, n, R.data(), R.size(), wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1updt<Scalar>(n, n, R, wa1.data(), wa2.data(), wa3.data(), &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), fjac.rows(), wa2.data(), wa3.data());
ei_r1mpyq<Scalar>(1, n, qtf.data(), 1, wa2.data(), wa3.data());