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-rw-r--r--unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h44
1 files changed, 22 insertions, 22 deletions
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index a8f3f3e64..c43df3f7a 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -69,8 +69,8 @@ public:
Parameters()
: factor(Scalar(100.))
, maxfev(400)
- , ftol(ei_sqrt(NumTraits<Scalar>::epsilon()))
- , xtol(ei_sqrt(NumTraits<Scalar>::epsilon()))
+ , ftol(internal::sqrt(NumTraits<Scalar>::epsilon()))
+ , xtol(internal::sqrt(NumTraits<Scalar>::epsilon()))
, gtol(Scalar(0.))
, epsfcn(Scalar(0.)) {}
Scalar factor;
@@ -86,7 +86,7 @@ public:
LevenbergMarquardtSpace::Status lmder1(
FVectorType &x,
- const Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon())
+ const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
);
LevenbergMarquardtSpace::Status minimize(FVectorType &x);
@@ -97,12 +97,12 @@ public:
FunctorType &functor,
FVectorType &x,
Index *nfev,
- const Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon())
+ const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
);
LevenbergMarquardtSpace::Status lmstr1(
FVectorType &x,
- const Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon())
+ const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
);
LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType &x);
@@ -263,7 +263,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
if (fnorm != 0.)
for (Index j = 0; j < n; ++j)
if (wa2[permutation.indices()[j]] != 0.)
- gnorm = std::max(gnorm, ei_abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+ gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
/* test for convergence of the gradient norm. */
if (gnorm <= parameters.gtol)
@@ -276,7 +276,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
do {
/* determine the levenberg-marquardt parameter. */
- ei_lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
+ internal::lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
@@ -296,13 +296,13 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
/* compute the scaled actual reduction. */
actred = -1.;
if (Scalar(.1) * fnorm1 < fnorm)
- actred = 1. - ei_abs2(fnorm1 / fnorm);
+ actred = 1. - internal::abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction and */
/* the scaled directional derivative. */
wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1);
- temp1 = ei_abs2(wa3.stableNorm() / fnorm);
- temp2 = ei_abs2(ei_sqrt(par) * pnorm / fnorm);
+ temp1 = internal::abs2(wa3.stableNorm() / fnorm);
+ temp2 = internal::abs2(internal::sqrt(par) * pnorm / fnorm);
prered = temp1 + temp2 / Scalar(.5);
dirder = -(temp1 + temp2);
@@ -340,9 +340,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
}
/* tests for convergence. */
- if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
+ if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
- if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
+ if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::RelativeReductionTooSmall;
if (delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorTooSmall;
@@ -350,7 +350,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
- if (ei_abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
+ if (internal::abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::FtolTooSmall;
if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
return LevenbergMarquardtSpace::XtolTooSmall;
@@ -451,7 +451,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
Index rownb = 2;
for (i = 0; i < m; ++i) {
if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked;
- ei_rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
+ internal::rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
++rownb;
}
++njev;
@@ -510,7 +510,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
if (fnorm != 0.)
for (j = 0; j < n; ++j)
if (wa2[permutation.indices()[j]] != 0.)
- gnorm = std::max(gnorm, ei_abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+ gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
/* test for convergence of the gradient norm. */
if (gnorm <= parameters.gtol)
@@ -523,7 +523,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
do {
/* determine the levenberg-marquardt parameter. */
- ei_lmpar<Scalar>(fjac, permutation.indices(), diag, qtf, delta, par, wa1);
+ internal::lmpar<Scalar>(fjac, permutation.indices(), diag, qtf, delta, par, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
@@ -543,13 +543,13 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
/* compute the scaled actual reduction. */
actred = -1.;
if (Scalar(.1) * fnorm1 < fnorm)
- actred = 1. - ei_abs2(fnorm1 / fnorm);
+ actred = 1. - internal::abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction and */
/* the scaled directional derivative. */
wa3 = fjac.topLeftCorner(n,n).template triangularView<Upper>() * (permutation.inverse() * wa1);
- temp1 = ei_abs2(wa3.stableNorm() / fnorm);
- temp2 = ei_abs2(ei_sqrt(par) * pnorm / fnorm);
+ temp1 = internal::abs2(wa3.stableNorm() / fnorm);
+ temp2 = internal::abs2(internal::sqrt(par) * pnorm / fnorm);
prered = temp1 + temp2 / Scalar(.5);
dirder = -(temp1 + temp2);
@@ -587,9 +587,9 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
}
/* tests for convergence. */
- if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
+ if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
- if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
+ if (internal::abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::RelativeReductionTooSmall;
if (delta <= parameters.xtol * xnorm)
return LevenbergMarquardtSpace::RelativeErrorTooSmall;
@@ -597,7 +597,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
- if (ei_abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
+ if (internal::abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
return LevenbergMarquardtSpace::FtolTooSmall;
if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
return LevenbergMarquardtSpace::XtolTooSmall;
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-23 18:05:36 +0000