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put nfev/njev as internal variables as well

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This commit is contained in:
Thomas Capricelli 2009-08-25 22:13:08 +02:00
parent 41b6ea81db
commit 470ea55834
3 changed files with 121 additions and 142 deletions
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11
unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h
View File

@ -33,7 +33,6 @@ public:
);
Status solve(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev, int &njev,
const Parameters &parameters,
const int mode=1
);
@ -44,7 +43,6 @@ public:
);
Status solveNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
const Parameters &parameters,
const int mode=1,
int nb_of_subdiagonals = -1,
@ -57,6 +55,8 @@ public:
Matrix< Scalar, Dynamic, 1 > R;
Matrix< Scalar, Dynamic, 1 > qtf;
Matrix< Scalar, Dynamic, 1 > diag;
int nfev;
int njev;
private:
const FunctorType &functor;
};
@ -71,7 +71,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
)
{
const int n = x.size();
int nfev=0, njev=0;
Parameters parameters;
/* check the input parameters for errors. */
@ -85,7 +84,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
diag.setConstant(n, 1.);
return solve(
x,
nfev, njev,
parameters,
2
);
@ -97,8 +95,6 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solve(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
const Parameters &parameters,
const int mode
)
@ -385,7 +381,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
)
{
const int n = x.size();
int nfev=0;
Parameters parameters;
/* check the input parameters for errors. */
@ -400,7 +395,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
diag.setConstant(n, 1.);
return solveNumericalDiff(
x,
nfev,
parameters,
2,
-1, -1,
@ -413,7 +407,6 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
const Parameters &parameters,
const int mode,
int nb_of_subdiagonals,

18
unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
View File

@ -41,8 +41,6 @@ public:
Status minimize(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
const Parameters &parameters,
const int mode=1
);
@ -54,7 +52,6 @@ public:
Status minimizeNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
const Parameters &parameters,
const int mode=1,
const Scalar epsfcn = Scalar(0.)
@ -67,8 +64,6 @@ public:
Status minimizeOptimumStorage(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
const Parameters &parameters,
const int mode=1
);
@ -78,6 +73,8 @@ public:
VectorXi ipvt;
Matrix< Scalar, Dynamic, 1 > qtf;
Matrix< Scalar, Dynamic, 1 > diag;
int nfev;
int njev;
private:
const FunctorType &functor;
};
@ -91,7 +88,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
{
const int n = x.size();
const int m = functor.nbOfFunctions();
int nfev=0, njev=0;
Parameters parameters;
/* check the input parameters for errors. */
@ -106,7 +102,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
return minimize(
x,
nfev, njev,
parameters,
1
);
@ -117,8 +112,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimize(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
const Parameters &parameters,
const int mode
)
@ -370,7 +363,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
{
const int n = x.size();
const int m = functor.nbOfFunctions();
int nfev=0;
Parameters parameters;
/* check the input parameters for errors. */
@ -385,7 +377,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
return minimizeNumericalDiff(
x,
nfev,
parameters,
1,
Scalar(0.)
@ -396,7 +387,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
const Parameters &parameters,
const int mode,
const Scalar epsfcn
@ -648,7 +638,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
{
const int n = x.size();
const int m = functor.nbOfFunctions();
int nfev=0, njev=0;
Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
VectorXi ipvt;
Parameters parameters;
@ -665,7 +654,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
return minimizeOptimumStorage(
x,
nfev, njev,
parameters,
1
);
@ -675,8 +663,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
const Parameters &parameters,
const int mode
)

234
unsupported/test/NonLinear.cpp
View File

@ -171,7 +171,7 @@ void testLmder1()
void testLmder()
{
const int m=15, n=3;
int info, nfev=0, njev=0;
int info;
double fnorm, covfac;
VectorXd x;
@ -182,12 +182,12 @@ void testLmder()
lmder_functor functor;
LevenbergMarquardt<lmder_functor> lm(functor);
LevenbergMarquardt<lmder_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return values
VERIFY( 1 == info);
VERIFY(nfev==6);
VERIFY(njev==5);
VERIFY(lm.nfev==6);
VERIFY(lm.njev==5);
// check norm
fnorm = lm.fvec.blueNorm();
@ -291,7 +291,7 @@ void testHybrj1()
void testHybrj()
{
const int n=9;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
/* the following starting values provide a rough fit. */
@ -303,12 +303,12 @@ void testHybrj()
HybridNonLinearSolver<hybrj_functor> solver(functor);
solver.diag.setConstant(n, 1.);
HybridNonLinearSolver<hybrj_functor>::Parameters parameters;
info = solver.solve(x, nfev, njev, parameters, 2);
info = solver.solve(x, parameters, 2);
// check return value
VERIFY( 1 == info);
VERIFY(nfev==11);
VERIFY(njev==1);
VERIFY(solver.nfev==11);
VERIFY(solver.njev==1);
// check norm
VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
@ -373,7 +373,7 @@ void testHybrd1()
void testHybrd()
{
const int n=9;
int info, nfev=0, ml, mu;
int info, ml, mu;
VectorXd x;
/* the following starting values provide a rough fit. */
@ -387,11 +387,11 @@ void testHybrd()
HybridNonLinearSolver<hybrd_functor> solver(functor);
HybridNonLinearSolver<hybrd_functor>::Parameters parameters;
solver.diag.setConstant(n, 1.);
info = solver.solveNumericalDiff(x, nfev, parameters, 2, ml, mu);
info = solver.solveNumericalDiff(x, parameters, 2, ml, mu);
// check return value
VERIFY( 1 == info);
VERIFY(nfev==14);
VERIFY(solver.nfev==14);
// check norm
VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
@ -475,7 +475,7 @@ void testLmstr1()
void testLmstr()
{
const int n=3;
int info, nfev=0, njev=0;
int info;
double fnorm;
VectorXd x(n);
@ -486,12 +486,12 @@ void testLmstr()
lmstr_functor functor;
LevenbergMarquardt<lmstr_functor> lm(functor);
LevenbergMarquardt<lmstr_functor>::Parameters parameters;
info = lm.minimizeOptimumStorage(x, nfev, njev, parameters);
info = lm.minimizeOptimumStorage(x, parameters);
// check return values
VERIFY( 1 == info);
VERIFY(nfev==6);
VERIFY(njev==5);
VERIFY(lm.nfev==6);
VERIFY(lm.njev==5);
// check norm
fnorm = lm.fvec.blueNorm();
@ -562,7 +562,7 @@ void testLmdif1()
void testLmdif()
{
const int m=15, n=3;
int info, nfev=0;
int info;
double fnorm, covfac;
VectorXd x(n);
@ -573,11 +573,11 @@ void testLmdif()
lmdif_functor functor;
LevenbergMarquardt<lmdif_functor> lm(functor);
LevenbergMarquardt<lmdif_functor>::Parameters parameters;
info = lm.minimizeNumericalDiff(x, nfev, parameters);
info = lm.minimizeNumericalDiff(x, parameters);
// check return values
VERIFY( 1 == info);
VERIFY(nfev==21);
VERIFY(lm.nfev==21);
// check norm
fnorm = lm.fvec.blueNorm();
@ -647,7 +647,7 @@ const double chwirut2_functor::m_y[54] = { 92.9000E0 ,57.1000E0 ,31.0500E0 ,11.5
void testNistChwirut2(void)
{
const int n=3;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -659,12 +659,12 @@ void testNistChwirut2(void)
chwirut2_functor functor;
LevenbergMarquardt<chwirut2_functor> lm(functor);
LevenbergMarquardt<chwirut2_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 10 == nfev);
VERIFY( 8 == njev);
VERIFY( 10 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
// check x
@ -680,12 +680,12 @@ void testNistChwirut2(void)
parameters = LevenbergMarquardt<chwirut2_functor>::Parameters(); // get default back
parameters.ftol = 1.E6*epsilon<double>();
parameters.xtol = 1.E6*epsilon<double>();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 7 == nfev);
VERIFY( 6 == njev);
VERIFY( 7 == lm.nfev);
VERIFY( 6 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.1304802941E+02);
// check x
@ -728,7 +728,7 @@ const double misra1a_functor::m_y[14] = { 10.07E0, 14.73E0, 17.94E0, 23.93E0, 29
void testNistMisra1a(void)
{
const int n=2;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -740,12 +740,12 @@ void testNistMisra1a(void)
misra1a_functor functor;
LevenbergMarquardt<misra1a_functor> lm(functor);
LevenbergMarquardt<misra1a_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 19 == nfev);
VERIFY( 15 == njev);
VERIFY( 19 == lm.nfev);
VERIFY( 15 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
// check x
@ -757,12 +757,12 @@ void testNistMisra1a(void)
*/
x<< 250., 0.0005;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 5 == nfev);
VERIFY( 4 == njev);
VERIFY( 5 == lm.nfev);
VERIFY( 4 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.2455138894E-01);
// check x
@ -815,7 +815,7 @@ const double hahn1_functor::m_x[236] = { 24.41E0 , 34.82E0 , 44.09E0 , 45.07E0 ,
void testNistHahn1(void)
{
const int n=7;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -827,12 +827,12 @@ void testNistHahn1(void)
hahn1_functor functor;
LevenbergMarquardt<hahn1_functor> lm(functor);
LevenbergMarquardt<hahn1_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 11== nfev);
VERIFY( 10== njev);
VERIFY( 11== lm.nfev);
VERIFY( 10== lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
// check x
@ -849,12 +849,12 @@ void testNistHahn1(void)
*/
x<< .1, -.1, .005, -.000001, -.005, .0001, -.0000001;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 11 == nfev);
VERIFY( 10 == njev);
VERIFY( 11 == lm.nfev);
VERIFY( 10 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.5324382854E+00);
// check x
@ -902,7 +902,7 @@ const double misra1d_functor::y[14] = { 10.07E0, 14.73E0, 17.94E0, 23.93E0, 29.6
void testNistMisra1d(void)
{
const int n=2;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -914,12 +914,12 @@ void testNistMisra1d(void)
misra1d_functor functor;
LevenbergMarquardt<misra1d_functor> lm(functor);
LevenbergMarquardt<misra1d_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 3 == info);
VERIFY( 9 == nfev);
VERIFY( 7 == njev);
VERIFY( 9 == lm.nfev);
VERIFY( 7 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
// check x
@ -931,12 +931,12 @@ void testNistMisra1d(void)
*/
x<< 450., 0.0003;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 4 == nfev);
VERIFY( 3 == njev);
VERIFY( 4 == lm.nfev);
VERIFY( 3 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6419295283E-02);
// check x
@ -981,7 +981,7 @@ const double lanczos1_functor::y[24] = { 2.513400000000E+00 ,2.044333373291E+00
void testNistLanczos1(void)
{
const int n=6;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -993,12 +993,12 @@ void testNistLanczos1(void)
lanczos1_functor functor;
LevenbergMarquardt<lanczos1_functor> lm(functor);
LevenbergMarquardt<lanczos1_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 2 == info);
VERIFY( 79 == nfev);
VERIFY( 72 == njev);
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
@ -1014,12 +1014,12 @@ void testNistLanczos1(void)
*/
x<< 0.5, 0.7, 3.6, 4.2, 4., 6.3;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 2 == info);
VERIFY( 9 == nfev);
VERIFY( 8 == njev);
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
@ -1068,7 +1068,7 @@ const double rat42_functor::y[9] = { 8.930E0 ,10.800E0 ,18.590E0 ,22.330E0 ,39.3
void testNistRat42(void)
{
const int n=3;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1080,12 +1080,12 @@ void testNistRat42(void)
rat42_functor functor;
LevenbergMarquardt<rat42_functor> lm(functor);
LevenbergMarquardt<rat42_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 10 == nfev);
VERIFY( 8 == njev);
VERIFY( 10 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
// check x
@ -1098,12 +1098,12 @@ void testNistRat42(void)
*/
x<< 75., 2.5, 0.07;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 6 == nfev);
VERIFY( 5 == njev);
VERIFY( 6 == lm.nfev);
VERIFY( 5 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.0565229338E+00);
// check x
@ -1147,7 +1147,7 @@ const double MGH10_functor::y[16] = { 3.478000E+04, 2.861000E+04, 2.365000E+04,
void testNistMGH10(void)
{
const int n=3;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1159,12 +1159,12 @@ void testNistMGH10(void)
MGH10_functor functor;
LevenbergMarquardt<MGH10_functor> lm(functor);
LevenbergMarquardt<MGH10_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 2 == info);
VERIFY( 285 == nfev);
VERIFY( 250 == njev);
VERIFY( 285 == lm.nfev);
VERIFY( 250 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
// check x
@ -1177,12 +1177,12 @@ void testNistMGH10(void)
*/
x<< 0.02, 4000., 250.;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 2 == info);
VERIFY( 126 == nfev);
VERIFY( 116 == njev);
VERIFY( 126 == lm.nfev);
VERIFY( 116 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7945855171E+01);
// check x
@ -1224,7 +1224,7 @@ const double BoxBOD_functor::x[6] = { 1., 2., 3., 5., 7., 10. };
void testNistBoxBOD(void)
{
const int n=2;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1239,12 +1239,12 @@ void testNistBoxBOD(void)
parameters.ftol = 1.E6*epsilon<double>();
parameters.xtol = 1.E6*epsilon<double>();
parameters.factor = 10.;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 31 == nfev);
VERIFY( 25 == njev);
VERIFY( 31 == lm.nfev);
VERIFY( 25 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
// check x
@ -1259,12 +1259,12 @@ void testNistBoxBOD(void)
parameters = LevenbergMarquardt<BoxBOD_functor>::Parameters(); // get default back
parameters.ftol = epsilon<double>();
parameters.xtol = epsilon<double>();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 15 == nfev);
VERIFY( 14 == njev);
VERIFY( 15 == lm.nfev);
VERIFY( 14 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.1680088766E+03);
// check x
@ -1307,7 +1307,7 @@ const double MGH17_functor::y[33] = { 8.440000E-01, 9.080000E-01, 9.320000E-01,
void testNistMGH17(void)
{
const int n=5;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1322,12 +1322,12 @@ void testNistMGH17(void)
parameters.ftol = epsilon<double>();
parameters.xtol = epsilon<double>();
parameters.maxfev = 1000;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 599 == nfev);
VERIFY( 544 == njev);
VERIFY( 599 == lm.nfev);
VERIFY( 544 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
// check x
@ -1343,12 +1343,12 @@ void testNistMGH17(void)
x<< 0.5 ,1.5 ,-1 ,0.01 ,0.02;
// do the computation
parameters = LevenbergMarquardt<MGH17_functor>::Parameters(); // get default back
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 18 == nfev);
VERIFY( 15 == njev);
VERIFY( 18 == lm.nfev);
VERIFY( 15 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.4648946975E-05);
// check x
@ -1397,7 +1397,7 @@ const double MGH09_functor::y[11] = { 1.957000E-01, 1.947000E-01, 1.735000E-01,
void testNistMGH09(void)
{
const int n=4;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1410,12 +1410,12 @@ void testNistMGH09(void)
LevenbergMarquardt<MGH09_functor> lm(functor);
LevenbergMarquardt<MGH09_functor>::Parameters parameters;
parameters.maxfev = 1000;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 503== nfev);
VERIFY( 385 == njev);
VERIFY( 503== lm.nfev);
VERIFY( 385 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
// check x
@ -1430,12 +1430,12 @@ void testNistMGH09(void)
x<< 0.25, 0.39, 0.415, 0.39;
// do the computation
parameters = LevenbergMarquardt<MGH09_functor>::Parameters();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 18 == nfev);
VERIFY( 16 == njev);
VERIFY( 18 == lm.nfev);
VERIFY( 16 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 3.0750560385E-04);
// check x
@ -1481,7 +1481,7 @@ const double Bennett5_functor::y[154] = { -34.834702E0 ,-34.393200E0 ,-34.152901
void testNistBennett5(void)
{
const int n=3;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1494,12 +1494,12 @@ void testNistBennett5(void)
LevenbergMarquardt<Bennett5_functor> lm(functor);
LevenbergMarquardt<Bennett5_functor>::Parameters parameters;
parameters.maxfev = 1000;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 758 == nfev);
VERIFY( 744 == njev);
VERIFY( 758 == lm.nfev);
VERIFY( 744 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
// check x
@ -1512,12 +1512,12 @@ void testNistBennett5(void)
x<< -1500., 45., 0.85;
// do the computation
parameters = LevenbergMarquardt<Bennett5_functor>::Parameters();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 203 == nfev);
VERIFY( 192 == njev);
VERIFY( 203 == lm.nfev);
VERIFY( 192 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.2404744073E-04);
// check x
@ -1569,7 +1569,7 @@ const double thurber_functor::_y[37] = { 80.574E0, 84.248E0, 87.264E0, 87.195E0,
void testNistThurber(void)
{
const int n=7;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1583,12 +1583,12 @@ void testNistThurber(void)
LevenbergMarquardt<thurber_functor>::Parameters parameters;
parameters.ftol = 1.E4*epsilon<double>();
parameters.xtol = 1.E4*epsilon<double>();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 39 == nfev);
VERIFY( 36== njev);
VERIFY( 39 == lm.nfev);
VERIFY( 36== lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
// check x
@ -1608,12 +1608,12 @@ void testNistThurber(void)
parameters = LevenbergMarquardt<thurber_functor>::Parameters();
parameters.ftol = 1.E4*epsilon<double>();
parameters.xtol = 1.E4*epsilon<double>();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 29 == nfev);
VERIFY( 28 == njev);
VERIFY( 29 == lm.nfev);
VERIFY( 28 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 5.6427082397E+03);
// check x
@ -1662,7 +1662,7 @@ const double rat43_functor::y[15] = { 16.08, 33.83, 65.80, 97.20, 191.55, 326.20
void testNistRat43(void)
{
const int n=4;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1676,12 +1676,12 @@ void testNistRat43(void)
LevenbergMarquardt<rat43_functor>::Parameters parameters;
parameters.ftol = 1.E6*epsilon<double>();
parameters.xtol = 1.E6*epsilon<double>();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 27 == nfev);
VERIFY( 20 == njev);
VERIFY( 27 == lm.nfev);
VERIFY( 20 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
// check x
@ -1698,12 +1698,12 @@ void testNistRat43(void)
parameters = LevenbergMarquardt<rat43_functor>::Parameters(); // get default back
parameters.ftol = 1.E5*epsilon<double>();
parameters.xtol = 1.E5*epsilon<double>();
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 9 == nfev);
VERIFY( 8 == njev);
VERIFY( 9 == lm.nfev);
VERIFY( 8 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 8.7864049080E+03);
// check x
@ -1750,7 +1750,7 @@ const double eckerle4_functor::y[35] = { 0.0001575, 0.0001699, 0.0002350, 0.0003
void testNistEckerle4(void)
{
const int n=3;
int info, nfev=0, njev=0;
int info;
VectorXd x(n);
@ -1762,12 +1762,12 @@ void testNistEckerle4(void)
eckerle4_functor functor;
LevenbergMarquardt<eckerle4_functor> lm(functor);
LevenbergMarquardt<eckerle4_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 18 == nfev);
VERIFY( 15 == njev);
VERIFY( 18 == lm.nfev);
VERIFY( 15 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
// check x
@ -1780,12 +1780,12 @@ void testNistEckerle4(void)
*/
x<< 1.5, 5., 450.;
// do the computation
info = lm.minimize(x, nfev, njev, parameters);
info = lm.minimize(x, parameters);
// check return value
VERIFY( 1 == info);
VERIFY( 7 == nfev);
VERIFY( 6 == njev);
VERIFY( 7 == lm.nfev);
VERIFY( 6 == lm.njev);
// check norm^2
VERIFY_IS_APPROX(lm.fvec.squaredNorm(), 1.4635887487E-03);
// check x
@ -1832,7 +1832,7 @@ void test_NonLinear()
/*
* Can be useful for debugging...
printf("info, nfev, njev : %d, %d, %d\n", info, nfev, njev);
printf("info, nfev, njev : %d, %d, %d\n", info, lm.nfev, lm.njev);
printf("x[0] : %.32g\n", x[0]);
printf("x[1] : %.32g\n", x[1]);
printf("x[2] : %.32g\n", x[2]);