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-rw-r--r--unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h86
1 files changed, 40 insertions, 46 deletions
diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index e944f0e1b..bcf1016db 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -29,6 +29,7 @@
#define EIGEN_HYBRIDNONLINEARSOLVER_H
/**
+ * \ingroup NonLinearOptimization_Module
* \brief Finds a zero of a system of n
* nonlinear functions in n variables by a modification of the Powell
* hybrid method ("dogleg").
@@ -71,50 +72,49 @@ public:
int nb_of_superdiagonals;
Scalar epsfcn;
};
+ typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
+ typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
Status hybrj1(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status solveInit(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode=1
);
Status solveOneStep(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode=1
);
Status solve(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode=1
);
Status hybrd1(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status solveNumericalDiffInit(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode=1
);
Status solveNumericalDiffOneStep(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode=1
);
Status solveNumericalDiff(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode=1
);
void resetParameters(void) { parameters = Parameters(); }
Parameters parameters;
- Matrix< Scalar, Dynamic, 1 > fvec;
- Matrix< Scalar, Dynamic, Dynamic > fjac;
- Matrix< Scalar, Dynamic, 1 > R;
- Matrix< Scalar, Dynamic, 1 > qtf;
- Matrix< Scalar, Dynamic, 1 > diag;
+ FVectorType fvec, R, qtf, diag;
+ JacobianType fjac;
int nfev;
int njev;
int iter;
@@ -133,7 +133,7 @@ private:
int nslow1, nslow2;
int ncfail;
Scalar actred, prered;
- Matrix< Scalar, Dynamic, 1 > wa1, wa2, wa3, wa4;
+ FVectorType wa1, wa2, wa3, wa4;
};
@@ -141,7 +141,7 @@ private:
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const Scalar tol
)
{
@@ -164,7 +164,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode
)
{
@@ -214,7 +214,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode
)
{
@@ -227,15 +227,11 @@ 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];
@@ -252,6 +248,9 @@ 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;
@@ -270,18 +269,16 @@ 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. */
@@ -435,7 +432,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solve(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode
)
{
@@ -450,7 +447,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const Scalar tol
)
{
@@ -474,7 +471,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode
)
{
@@ -529,7 +526,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode
)
{
@@ -544,13 +541,10 @@ 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) {
@@ -561,7 +555,6 @@ 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;
@@ -569,6 +562,9 @@ 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;
@@ -587,18 +583,16 @@ 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. */
@@ -752,7 +746,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
- Matrix< Scalar, Dynamic, 1 > &x,
+ FVectorType &x,
const int mode
)
{
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-12 18:13:36 +0000