make diag be an internal variable too

This commit is contained in:
Thomas Capricelli 2009-08-25 21:59:10 +02:00
parent e465ea82e1
commit fa0183e7c7
3 changed files with 66 additions and 81 deletions

View File

@ -34,7 +34,6 @@ public:
Status solve(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev, int &njev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode=1
);
@ -46,7 +45,6 @@ public:
Status solveNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode=1,
int nb_of_subdiagonals = -1,
@ -58,6 +56,7 @@ public:
Matrix< Scalar, Dynamic, Dynamic > fjac;
Matrix< Scalar, Dynamic, 1 > R;
Matrix< Scalar, Dynamic, 1 > qtf;
Matrix< Scalar, Dynamic, 1 > diag;
private:
const FunctorType &functor;
};
@ -73,7 +72,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
{
const int n = x.size();
int nfev=0, njev=0;
Matrix< Scalar, Dynamic, 1> diag;
Parameters parameters;
/* check the input parameters for errors. */
@ -88,7 +86,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
return solve(
x,
nfev, njev,
diag,
parameters,
2
);
@ -102,7 +99,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode
)
@ -115,6 +111,8 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
R.resize( (n*(n+1))/2);
fjac.resize(n, n);
fvec.resize(n);
if (mode != 2)
diag.resize(n);
/* Local variables */
int i, j, l, iwa[1];
@ -388,7 +386,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
{
const int n = x.size();
int nfev=0;
Matrix< Scalar, Dynamic, 1> diag;
Parameters parameters;
/* check the input parameters for errors. */
@ -404,7 +401,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
return solveNumericalDiff(
x,
nfev,
diag,
parameters,
2,
-1, -1,
@ -418,7 +414,6 @@ typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode,
int nb_of_subdiagonals,
@ -436,6 +431,8 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
R.resize( (n*(n+1))/2);
fjac.resize(n, n);
fvec.resize(n);
if (mode != 2)
diag.resize(n);
/* Local variables */
int i, j, l, iwa[1];

View File

@ -43,7 +43,6 @@ public:
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode=1
);
@ -56,7 +55,6 @@ public:
Status minimizeNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode=1,
const Scalar epsfcn = Scalar(0.)
@ -71,7 +69,6 @@ public:
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode=1
);
@ -80,6 +77,7 @@ public:
Matrix< Scalar, Dynamic, Dynamic > fjac;
VectorXi ipvt;
Matrix< Scalar, Dynamic, 1 > qtf;
Matrix< Scalar, Dynamic, 1 > diag;
private:
const FunctorType &functor;
};
@ -94,9 +92,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
const int n = x.size();
const int m = functor.nbOfFunctions();
int nfev=0, njev=0;
Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
Matrix< Scalar, Dynamic, 1> diag, qtf;
VectorXi ipvt;
Parameters parameters;
/* check the input parameters for errors. */
@ -112,7 +107,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
return minimize(
x,
nfev, njev,
diag,
parameters,
1
);
@ -125,7 +119,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode
)
@ -137,7 +130,8 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
fvec.resize(m);
ipvt.resize(n);
fjac.resize(m, n);
diag.resize(n);
if (mode != 2)
diag.resize(n);
qtf.resize(n);
/* Local variables */
@ -376,9 +370,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
const int n = x.size();
const int m = functor.nbOfFunctions();
int nfev=0;
Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
Matrix< Scalar, Dynamic, 1> diag, qtf;
VectorXi ipvt;
Parameters parameters;
/* check the input parameters for errors. */
@ -394,7 +385,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
return minimizeNumericalDiff(
x,
nfev,
diag,
parameters,
1,
Scalar(0.)
@ -406,7 +396,6 @@ typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode,
const Scalar epsfcn
@ -419,7 +408,8 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
fvec.resize(m);
ipvt.resize(n);
fjac.resize(m, n);
diag.resize(n);
if (mode != 2 )
diag.resize(n);
qtf.resize(n);
/* Local variables */
@ -658,7 +648,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
const int m = functor.nbOfFunctions();
int nfev=0, njev=0;
Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
Matrix< Scalar, Dynamic, 1> diag, qtf;
VectorXi ipvt;
Parameters parameters;
@ -675,7 +664,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
return minimizeOptimumStorage(
x,
nfev, njev,
diag,
parameters,
1
);
@ -687,7 +675,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
Matrix< Scalar, Dynamic, 1 > &x,
int &nfev,
int &njev,
Matrix< Scalar, Dynamic, 1 > &diag,
const Parameters &parameters,
const int mode
)
@ -699,7 +686,8 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
fvec.resize(m);
ipvt.resize(n);
fjac.resize(m, n);
diag.resize(n);
if (mode != 2)
diag.resize(n);
qtf.resize(n);
/* Local variables */

View File

@ -173,7 +173,7 @@ void testLmder()
const int m=15, n=3;
int info, nfev=0, njev=0;
double fnorm, covfac;
VectorXd x, diag;
VectorXd x;
/* the following starting values provide a rough fit. */
x.setConstant(n, 1.);
@ -182,7 +182,7 @@ void testLmder()
lmder_functor functor;
LevenbergMarquardt<lmder_functor> lm(functor);
LevenbergMarquardt<lmder_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return values
VERIFY( 1 == info);
@ -292,18 +292,18 @@ void testHybrj()
{
const int n=9;
int info, nfev=0, njev=0;
VectorXd x(n), diag(n);
VectorXd x(n);
/* the following starting values provide a rough fit. */
x.setConstant(n, -1.);
diag.setConstant(n, 1.);
// do the computation
hybrj_functor functor;
HybridNonLinearSolver<hybrj_functor> solver(functor);
solver.diag.setConstant(n, 1.);
HybridNonLinearSolver<hybrj_functor>::Parameters parameters;
info = solver.solve(x, nfev, njev, diag, parameters, 2);
info = solver.solve(x, nfev, njev, parameters, 2);
// check return value
VERIFY( 1 == info);
@ -374,20 +374,20 @@ void testHybrd()
{
const int n=9;
int info, nfev=0, ml, mu;
VectorXd x, diag(n);
VectorXd x;
/* the following starting values provide a rough fit. */
x.setConstant(n, -1.);
ml = 1;
mu = 1;
diag.setConstant(n, 1.);
// do the computation
hybrd_functor functor;
HybridNonLinearSolver<hybrd_functor> solver(functor);
HybridNonLinearSolver<hybrd_functor>::Parameters parameters;
info = solver.solveNumericalDiff(x, nfev, diag, parameters, 2, ml, mu);
solver.diag.setConstant(n, 1.);
info = solver.solveNumericalDiff(x, nfev, parameters, 2, ml, mu);
// check return value
VERIFY( 1 == info);
@ -477,7 +477,7 @@ void testLmstr()
const int n=3;
int info, nfev=0, njev=0;
double fnorm;
VectorXd x(n), diag;
VectorXd x(n);
/* the following starting values provide a rough fit. */
x.setConstant(n, 1.);
@ -486,7 +486,7 @@ void testLmstr()
lmstr_functor functor;
LevenbergMarquardt<lmstr_functor> lm(functor);
LevenbergMarquardt<lmstr_functor>::Parameters parameters;
info = lm.minimizeOptimumStorage(x, nfev, njev, diag, parameters);
info = lm.minimizeOptimumStorage(x, nfev, njev, parameters);
// check return values
VERIFY( 1 == info);
@ -564,7 +564,7 @@ void testLmdif()
const int m=15, n=3;
int info, nfev=0;
double fnorm, covfac;
VectorXd x(n), diag;
VectorXd x(n);
/* the following starting values provide a rough fit. */
x.setConstant(n, 1.);
@ -573,7 +573,7 @@ void testLmdif()
lmdif_functor functor;
LevenbergMarquardt<lmdif_functor> lm(functor);
LevenbergMarquardt<lmdif_functor>::Parameters parameters;
info = lm.minimizeNumericalDiff(x, nfev, diag, parameters);
info = lm.minimizeNumericalDiff(x, nfev, parameters);
// check return values
VERIFY( 1 == info);
@ -649,7 +649,7 @@ void testNistChwirut2(void)
const int n=3;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -659,7 +659,7 @@ void testNistChwirut2(void)
chwirut2_functor functor;
LevenbergMarquardt<chwirut2_functor> lm(functor);
LevenbergMarquardt<chwirut2_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -680,7 +680,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -730,7 +730,7 @@ void testNistMisra1a(void)
const int n=2;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -740,7 +740,7 @@ void testNistMisra1a(void)
misra1a_functor functor;
LevenbergMarquardt<misra1a_functor> lm(functor);
LevenbergMarquardt<misra1a_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -757,7 +757,7 @@ void testNistMisra1a(void)
*/
x<< 250., 0.0005;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -817,7 +817,7 @@ void testNistHahn1(void)
const int n=7;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -827,7 +827,7 @@ void testNistHahn1(void)
hahn1_functor functor;
LevenbergMarquardt<hahn1_functor> lm(functor);
LevenbergMarquardt<hahn1_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -849,7 +849,7 @@ void testNistHahn1(void)
*/
x<< .1, -.1, .005, -.000001, -.005, .0001, -.0000001;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -904,7 +904,7 @@ void testNistMisra1d(void)
const int n=2;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -914,7 +914,7 @@ void testNistMisra1d(void)
misra1d_functor functor;
LevenbergMarquardt<misra1d_functor> lm(functor);
LevenbergMarquardt<misra1d_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 3 == info);
@ -931,7 +931,7 @@ void testNistMisra1d(void)
*/
x<< 450., 0.0003;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -983,7 +983,7 @@ void testNistLanczos1(void)
const int n=6;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -993,7 +993,7 @@ void testNistLanczos1(void)
lanczos1_functor functor;
LevenbergMarquardt<lanczos1_functor> lm(functor);
LevenbergMarquardt<lanczos1_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 2 == info);
@ -1014,7 +1014,7 @@ void testNistLanczos1(void)
*/
x<< 0.5, 0.7, 3.6, 4.2, 4., 6.3;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 2 == info);
@ -1070,7 +1070,7 @@ void testNistRat42(void)
const int n=3;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1080,7 +1080,7 @@ void testNistRat42(void)
rat42_functor functor;
LevenbergMarquardt<rat42_functor> lm(functor);
LevenbergMarquardt<rat42_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1098,7 +1098,7 @@ void testNistRat42(void)
*/
x<< 75., 2.5, 0.07;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1149,7 +1149,7 @@ void testNistMGH10(void)
const int n=3;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1159,7 +1159,7 @@ void testNistMGH10(void)
MGH10_functor functor;
LevenbergMarquardt<MGH10_functor> lm(functor);
LevenbergMarquardt<MGH10_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 2 == info);
@ -1177,7 +1177,7 @@ void testNistMGH10(void)
*/
x<< 0.02, 4000., 250.;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 2 == info);
@ -1226,7 +1226,7 @@ void testNistBoxBOD(void)
const int n=2;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1239,7 +1239,7 @@ void testNistBoxBOD(void)
parameters.ftol = 1.E6*epsilon<double>();
parameters.xtol = 1.E6*epsilon<double>();
parameters.factor = 10.;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1259,7 +1259,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1309,7 +1309,7 @@ void testNistMGH17(void)
const int n=5;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1322,7 +1322,7 @@ void testNistMGH17(void)
parameters.ftol = epsilon<double>();
parameters.xtol = epsilon<double>();
parameters.maxfev = 1000;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1343,7 +1343,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1399,7 +1399,7 @@ void testNistMGH09(void)
const int n=4;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1410,7 +1410,7 @@ void testNistMGH09(void)
LevenbergMarquardt<MGH09_functor> lm(functor);
LevenbergMarquardt<MGH09_functor>::Parameters parameters;
parameters.maxfev = 1000;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1430,7 +1430,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1483,7 +1483,7 @@ void testNistBennett5(void)
const int n=3;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1494,7 +1494,7 @@ void testNistBennett5(void)
LevenbergMarquardt<Bennett5_functor> lm(functor);
LevenbergMarquardt<Bennett5_functor>::Parameters parameters;
parameters.maxfev = 1000;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1512,7 +1512,7 @@ void testNistBennett5(void)
x<< -1500., 45., 0.85;
// do the computation
parameters = LevenbergMarquardt<Bennett5_functor>::Parameters();
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1571,7 +1571,7 @@ void testNistThurber(void)
const int n=7;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1583,7 +1583,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1608,7 +1608,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1664,7 +1664,7 @@ void testNistRat43(void)
const int n=4;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1676,7 +1676,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1698,7 +1698,7 @@ 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, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1752,7 +1752,7 @@ void testNistEckerle4(void)
const int n=3;
int info, nfev=0, njev=0;
VectorXd x(n), diag;
VectorXd x(n);
/*
* First try
@ -1762,7 +1762,7 @@ void testNistEckerle4(void)
eckerle4_functor functor;
LevenbergMarquardt<eckerle4_functor> lm(functor);
LevenbergMarquardt<eckerle4_functor>::Parameters parameters;
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);
@ -1780,7 +1780,7 @@ void testNistEckerle4(void)
*/
x<< 1.5, 5., 450.;
// do the computation
info = lm.minimize(x, nfev, njev, diag, parameters);
info = lm.minimize(x, nfev, njev, parameters);
// check return value
VERIFY( 1 == info);