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move Parameters as a class member, simplify calling sequence. Convenience

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methods from minpack ( "*1()" ) get their original name back : they are
only useful when porting, anyway. Still, i prefer to keep them.
This commit is contained in:
Thomas Capricelli
2009-08-26 14:23:05 +02:00
parent c1be96967e
commit 458947af5e
3 changed files with 106 additions and 166 deletions
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54
unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h
View File

@@ -33,48 +33,44 @@ public:
Scalar epsfcn;
};
Status solve(
Status hybrj1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status solveInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status solveOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status solve(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status solveNumericalDiff(
Status hybrd1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status solveNumericalDiffInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status solveNumericalDiffOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status solveNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
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;
@@ -105,24 +101,23 @@ private:
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solve(
HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol
)
{
n = x.size();
Parameters parameters;
/* check the input parameters for errors. */
if (n <= 0 || tol < 0.)
return ImproperInputParameters;
resetParameters();
parameters.maxfev = 100*(n+1);
parameters.xtol = tol;
diag.setConstant(n, 1.);
return solve(
x,
parameters,
2
);
}
@@ -131,7 +126,6 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -182,7 +176,6 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -404,13 +397,12 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solve(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
Status status = solveInit(x, parameters, mode);
Status status = solveInit(x, mode);
while (status==Running)
status = solveOneStep(x, parameters, mode);
status = solveOneStep(x, mode);
return status;
}
@@ -418,25 +410,24 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol
)
{
n = x.size();
Parameters parameters;
/* check the input parameters for errors. */
if (n <= 0 || tol < 0.)
return ImproperInputParameters;
resetParameters();
parameters.maxfev = 200*(n+1);
parameters.xtol = tol;
diag.setConstant(n, 1.);
return solveNumericalDiff(
x,
parameters,
2
);
}
@@ -445,16 +436,13 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
n = x.size();
int nsub = parameters.nb_of_subdiagonals;
int nsup = parameters.nb_of_superdiagonals;
if (nsub<0) nsub= n-1;
if (nsup<0) nsup= n-1;
if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
qtf.resize(n);
@@ -472,7 +460,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
/* check the input parameters for errors. */
if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || nsub< 0 || nsup< 0 || parameters.factor <= 0. )
if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
return ImproperInputParameters;
if (mode == 2)
for (int j = 0; j < n; ++j)
@@ -502,22 +490,19 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
int i, j, l, iwa[1];
jeval = true;
int nsub = parameters.nb_of_subdiagonals;
int nsup = parameters.nb_of_superdiagonals;
if (nsub<0) nsub= n-1;
if (nsup<0) nsup= n-1;
if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
/* calculate the jacobian matrix. */
if (ei_fdjac1(functor, x, fvec, fjac, nsub, nsup, parameters.epsfcn) <0)
if (ei_fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
return UserAksed;
nfev += std::min(nsub+ nsup+ 1, n);
nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
/* compute the qr factorization of the jacobian. */
@@ -728,13 +713,12 @@ template<typename FunctorType, typename Scalar>
typename HybridNonLinearSolver<FunctorType,Scalar>::Status
HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
Status status = solveNumericalDiffInit(x, parameters, mode);
Status status = solveNumericalDiffInit(x, mode);
while (status==Running)
status = solveNumericalDiffOneStep(x, parameters, mode);
status = solveNumericalDiffOneStep(x, mode);
return status;
}

65
unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
View File

@@ -36,69 +36,62 @@ public:
Scalar epsfcn;
};
Status minimize(
Status lmder1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status minimize(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeNumericalDiff(
Status lmdif1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status minimizeNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeNumericalDiffInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeNumericalDiffOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeOptimumStorage(
Status lmstr1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol = ei_sqrt(epsilon<Scalar>())
);
Status minimizeOptimumStorage(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeOptimumStorageInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
Status minimizeOptimumStorageOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode=1
);
void resetParameters(void) { parameters = Parameters(); }
Parameters parameters;
Matrix< Scalar, Dynamic, 1 > fvec;
Matrix< Scalar, Dynamic, Dynamic > fjac;
VectorXi ipvt;
@@ -123,27 +116,24 @@ private:
template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimize(
LevenbergMarquardt<FunctorType,Scalar>::lmder1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol
)
{
n = x.size();
m = functor.nbOfFunctions();
Parameters parameters;
/* check the input parameters for errors. */
if (n <= 0 || m < n || tol < 0.)
return ImproperInputParameters;
resetParameters();
parameters.ftol = tol;
parameters.xtol = tol;
parameters.maxfev = 100*(n+1);
return minimize(
x,
parameters
);
return minimize(x);
}
@@ -151,13 +141,12 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimize(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
Status status = minimizeInit(x, parameters, mode);
Status status = minimizeInit(x, mode);
while (status==Running)
status = minimizeOneStep(x, parameters, mode);
status = minimizeOneStep(x, mode);
return status;
}
@@ -165,7 +154,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -216,7 +204,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -408,34 +395,30 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol
)
{
n = x.size();
m = functor.nbOfFunctions();
Parameters parameters;
/* check the input parameters for errors. */
if (n <= 0 || m < n || tol < 0.)
return ImproperInputParameters;
resetParameters();
parameters.ftol = tol;
parameters.xtol = tol;
parameters.maxfev = 200*(n+1);
return minimizeNumericalDiff(
x,
parameters
);
return minimizeNumericalDiff(x);
}
template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiffInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -484,7 +467,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiffOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -679,20 +661,19 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
Status status = minimizeNumericalDiffInit(x, parameters, mode);
Status status = minimizeNumericalDiffInit(x, mode);
while (status==Running)
status = minimizeNumericalDiffOneStep(x, parameters, mode);
status = minimizeNumericalDiffOneStep(x, mode);
return status;
}
template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
Matrix< Scalar, Dynamic, 1 > &x,
const Scalar tol
)
@@ -701,27 +682,23 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
m = functor.nbOfFunctions();
Matrix< Scalar, Dynamic, Dynamic > fjac(m, n);
VectorXi ipvt;
Parameters parameters;
/* check the input parameters for errors. */
if (n <= 0 || m < n || tol < 0.)
return ImproperInputParameters;
resetParameters();
parameters.ftol = tol;
parameters.xtol = tol;
parameters.maxfev = 100*(n+1);
return minimizeOptimumStorage(
x,
parameters
);
return minimizeOptimumStorage(x);
}
template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -773,7 +750,6 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
@@ -986,13 +962,12 @@ template<typename FunctorType, typename Scalar>
typename LevenbergMarquardt<FunctorType,Scalar>::Status
LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
Matrix< Scalar, Dynamic, 1 > &x,
const Parameters &parameters,
const int mode
)
{
Status status = minimizeOptimumStorageInit(x, parameters, mode);
Status status = minimizeOptimumStorageInit(x, mode);
while (status==Running)
status = minimizeOptimumStorageOneStep(x, parameters, mode);
status = minimizeOptimumStorageOneStep(x, mode);
return status;
}