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bug #86 : use internal:: namespace instead of ei_ prefix

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This commit is contained in:
Benoit Jacob
2010-10-25 10:15:22 -04:00
parent ca85a1f6c5
commit 4716040703
330 changed files with 7615 additions and 7032 deletions
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36
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
View File

@@ -65,7 +65,7 @@ public:
Parameters()
: factor(Scalar(100.))
, maxfev(1000)
, xtol(ei_sqrt(NumTraits<Scalar>::epsilon()))
, xtol(internal::sqrt(NumTraits<Scalar>::epsilon()))
, nb_of_subdiagonals(-1)
, nb_of_superdiagonals(-1)
, epsfcn(Scalar(0.)) {}
@@ -83,7 +83,7 @@ public:
HybridNonLinearSolverSpace::Status hybrj1(
FVectorType &x,
const Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
);
HybridNonLinearSolverSpace::Status solveInit(FVectorType &x);
@@ -92,7 +92,7 @@ public:
HybridNonLinearSolverSpace::Status hybrd1(
FVectorType &x,
const Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon())
const Scalar tol = internal::sqrt(NumTraits<Scalar>::epsilon())
);
HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType &x);
@@ -246,7 +246,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
while (true) {
/* determine the direction p. */
ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
@@ -266,14 +266,14 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
/* compute the scaled actual reduction. */
actred = -1.;
if (fnorm1 < fnorm) /* Computing 2nd power */
actred = 1. - ei_abs2(fnorm1 / fnorm);
actred = 1. - internal::abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction. */
wa3 = R.template triangularView<Upper>()*wa1 + qtf;
temp = wa3.stableNorm();
prered = 0.;
if (temp < fnorm) /* Computing 2nd power */
prered = 1. - ei_abs2(temp / fnorm);
prered = 1. - internal::abs2(temp / fnorm);
/* compute the ratio of the actual to the predicted reduction. */
ratio = 0.;
@@ -290,7 +290,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = std::max(delta, pnorm / Scalar(.5));
if (ei_abs(ratio - 1.) <= Scalar(.1)) {
if (internal::abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
}
@@ -342,9 +342,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
wa2 = (wa2-wa3)/pnorm;
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
ei_r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
jeval = false;
}
@@ -447,7 +447,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
/* calculate the jacobian matrix. */
if (ei_fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
return HybridNonLinearSolverSpace::UserAksed;
nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
@@ -487,7 +487,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
while (true) {
/* determine the direction p. */
ei_dogleg<Scalar>(R, diag, qtf, delta, wa1);
internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
/* store the direction p and x + p. calculate the norm of p. */
wa1 = -wa1;
@@ -507,14 +507,14 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
/* compute the scaled actual reduction. */
actred = -1.;
if (fnorm1 < fnorm) /* Computing 2nd power */
actred = 1. - ei_abs2(fnorm1 / fnorm);
actred = 1. - internal::abs2(fnorm1 / fnorm);
/* compute the scaled predicted reduction. */
wa3 = R.template triangularView<Upper>()*wa1 + qtf;
temp = wa3.stableNorm();
prered = 0.;
if (temp < fnorm) /* Computing 2nd power */
prered = 1. - ei_abs2(temp / fnorm);
prered = 1. - internal::abs2(temp / fnorm);
/* compute the ratio of the actual to the predicted reduction. */
ratio = 0.;
@@ -531,7 +531,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = std::max(delta, pnorm / Scalar(.5));
if (ei_abs(ratio - 1.) <= Scalar(.1)) {
if (internal::abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
}
@@ -583,9 +583,9 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
wa2 = (wa2-wa3)/pnorm;
/* compute the qr factorization of the updated jacobian. */
ei_r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
ei_r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
ei_r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
jeval = false;
}

44
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
View File

@@ -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;

19
unsupported/Eigen/src/NonLinearOptimization/chkder.h
View File

@@ -2,8 +2,10 @@
#define chkder_log10e 0.43429448190325182765
#define chkder_factor 100.
namespace internal {
template<typename Scalar>
void ei_chkder(
void chkder(
const Matrix< Scalar, Dynamic, 1 > &x,
const Matrix< Scalar, Dynamic, 1 > &fvec,
const Matrix< Scalar, Dynamic, Dynamic > &fjac,
@@ -15,9 +17,9 @@ void ei_chkder(
{
typedef DenseIndex Index;
const Scalar eps = ei_sqrt(NumTraits<Scalar>::epsilon());
const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());
const Scalar epsf = chkder_factor * NumTraits<Scalar>::epsilon();
const Scalar epslog = chkder_log10e * ei_log(eps);
const Scalar epslog = chkder_log10e * log(eps);
Scalar temp;
const Index m = fvec.size(), n = x.size();
@@ -26,7 +28,7 @@ void ei_chkder(
/* mode = 1. */
xp.resize(n);
for (Index j = 0; j < n; ++j) {
temp = eps * ei_abs(x[j]);
temp = eps * abs(x[j]);
if (temp == 0.)
temp = eps;
xp[j] = x[j] + temp;
@@ -36,21 +38,22 @@ void ei_chkder(
/* mode = 2. */
err.setZero(m);
for (Index j = 0; j < n; ++j) {
temp = ei_abs(x[j]);
temp = abs(x[j]);
if (temp == 0.)
temp = 1.;
err += temp * fjac.col(j);
}
for (Index i = 0; i < m; ++i) {
temp = 1.;
if (fvec[i] != 0. && fvecp[i] != 0. && ei_abs(fvecp[i] - fvec[i]) >= epsf * ei_abs(fvec[i]))
temp = eps * ei_abs((fvecp[i] - fvec[i]) / eps - err[i]) / (ei_abs(fvec[i]) + ei_abs(fvecp[i]));
if (fvec[i] != 0. && fvecp[i] != 0. && abs(fvecp[i] - fvec[i]) >= epsf * abs(fvec[i]))
temp = eps * abs((fvecp[i] - fvec[i]) / eps - err[i]) / (abs(fvec[i]) + abs(fvecp[i]));
err[i] = 1.;
if (temp > NumTraits<Scalar>::epsilon() && temp < eps)
err[i] = (chkder_log10e * ei_log(temp) - epslog) / epslog;
err[i] = (chkder_log10e * log(temp) - epslog) / epslog;
if (temp >= eps)
err[i] = 0.;
}
}
}
} // end namespace internal

10
unsupported/Eigen/src/NonLinearOptimization/covar.h
View File

@@ -1,9 +1,10 @@
namespace internal {
template <typename Scalar>
void ei_covar(
void covar(
Matrix< Scalar, Dynamic, Dynamic > &r,
const VectorXi &ipvt,
Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon()) )
Scalar tol = sqrt(NumTraits<Scalar>::epsilon()) )
{
typedef DenseIndex Index;
@@ -14,14 +15,14 @@ void ei_covar(
/* Function Body */
const Index n = r.cols();
const Scalar tolr = tol * ei_abs(r(0,0));
const Scalar tolr = tol * abs(r(0,0));
Matrix< Scalar, Dynamic, 1 > wa(n);
assert(ipvt.size()==n);
/* form the inverse of r in the full upper triangle of r. */
l = -1;
for (k = 0; k < n; ++k)
if (ei_abs(r(k,k)) > tolr) {
if (abs(r(k,k)) > tolr) {
r(k,k) = 1. / r(k,k);
for (j = 0; j <= k-1; ++j) {
temp = r(k,k) * r(j,k);
@@ -61,3 +62,4 @@ void ei_covar(
r.diagonal() = wa;
}
} // end namespace internal

8
unsupported/Eigen/src/NonLinearOptimization/dogleg.h
View File

@@ -1,6 +1,7 @@
namespace internal {
template <typename Scalar>
void ei_dogleg(
void dogleg(
const Matrix< Scalar, Dynamic, Dynamic > &qrfac,
const Matrix< Scalar, Dynamic, 1 > &diag,
const Matrix< Scalar, Dynamic, 1 > &qtb,
@@ -86,8 +87,8 @@ void ei_dogleg(
/* at which the quadratic is minimized. */
bnorm = qtb.stableNorm();
temp = bnorm / gnorm * (bnorm / qnorm) * (sgnorm / delta);
temp = temp - delta / qnorm * ei_abs2(sgnorm / delta) + ei_sqrt(ei_abs2(temp - delta / qnorm) + (1.-ei_abs2(delta / qnorm)) * (1.-ei_abs2(sgnorm / delta)));
alpha = delta / qnorm * (1. - ei_abs2(sgnorm / delta)) / temp;
temp = temp - delta / qnorm * abs2(sgnorm / delta) + sqrt(abs2(temp - delta / qnorm) + (1.-abs2(delta / qnorm)) * (1.-abs2(sgnorm / delta)));
alpha = delta / qnorm * (1. - abs2(sgnorm / delta)) / temp;
algo_end:
/* form appropriate convex combination of the gauss-newton */
@@ -96,3 +97,4 @@ algo_end:
x = temp * wa1 + alpha * x;
}
} // end namespace internal

12
unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
View File

@@ -1,6 +1,7 @@
namespace internal {
template<typename FunctorType, typename Scalar>
DenseIndex ei_fdjac1(
DenseIndex fdjac1(
const FunctorType &Functor,
Matrix< Scalar, Dynamic, 1 > &x,
Matrix< Scalar, Dynamic, 1 > &fvec,
@@ -25,13 +26,13 @@ DenseIndex ei_fdjac1(
Matrix< Scalar, Dynamic, 1 > wa1(n);
Matrix< Scalar, Dynamic, 1 > wa2(n);
eps = ei_sqrt(std::max(epsfcn,epsmch));
eps = sqrt(std::max(epsfcn,epsmch));
msum = ml + mu + 1;
if (msum >= n) {
/* computation of dense approximate jacobian. */
for (j = 0; j < n; ++j) {
temp = x[j];
h = eps * ei_abs(temp);
h = eps * abs(temp);
if (h == 0.)
h = eps;
x[j] = temp + h;
@@ -47,7 +48,7 @@ DenseIndex ei_fdjac1(
for (k = 0; k < msum; ++k) {
for (j = k; (msum<0) ? (j>n): (j<n); j += msum) {
wa2[j] = x[j];
h = eps * ei_abs(wa2[j]);
h = eps * abs(wa2[j]);
if (h == 0.) h = eps;
x[j] = wa2[j] + h;
}
@@ -56,7 +57,7 @@ DenseIndex ei_fdjac1(
return iflag;
for (j = k; (msum<0) ? (j>n): (j<n); j += msum) {
x[j] = wa2[j];
h = eps * ei_abs(wa2[j]);
h = eps * abs(wa2[j]);
if (h == 0.) h = eps;
fjac.col(j).setZero();
start = std::max<Index>(0,j-mu);
@@ -68,3 +69,4 @@ DenseIndex ei_fdjac1(
return 0;
}
} // end namespace internal

18
unsupported/Eigen/src/NonLinearOptimization/lmpar.h
View File

@@ -1,6 +1,7 @@
namespace internal {
template <typename Scalar>
void ei_lmpar(
void lmpar(
Matrix< Scalar, Dynamic, Dynamic > &r,
const VectorXi &ipvt,
const Matrix< Scalar, Dynamic, 1 > &diag,
@@ -106,10 +107,10 @@ void ei_lmpar(
/* evaluate the function at the current value of par. */
if (par == 0.)
par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
wa1 = ei_sqrt(par)* diag;
wa1 = sqrt(par)* diag;
Matrix< Scalar, Dynamic, 1 > sdiag(n);
ei_qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
wa2 = diag.cwiseProduct(x);
dxnorm = wa2.blueNorm();
@@ -119,7 +120,7 @@ void ei_lmpar(
/* if the function is small enough, accept the current value */
/* of par. also test for the exceptional cases where parl */
/* is zero or the number of iterations has reached 10. */
if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
break;
/* compute the newton correction. */
@@ -156,7 +157,7 @@ void ei_lmpar(
}
template <typename Scalar>
void ei_lmpar2(
void lmpar2(
const ColPivHouseholderQR<Matrix< Scalar, Dynamic, Dynamic> > &qr,
const Matrix< Scalar, Dynamic, 1 > &diag,
const Matrix< Scalar, Dynamic, 1 > &qtb,
@@ -243,10 +244,10 @@ void ei_lmpar2(
/* evaluate the function at the current value of par. */
if (par == 0.)
par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
wa1 = ei_sqrt(par)* diag;
wa1 = sqrt(par)* diag;
Matrix< Scalar, Dynamic, 1 > sdiag(n);
ei_qrsolv<Scalar>(s, qr.colsPermutation().indices(), wa1, qtb, x, sdiag);
qrsolv<Scalar>(s, qr.colsPermutation().indices(), wa1, qtb, x, sdiag);
wa2 = diag.cwiseProduct(x);
dxnorm = wa2.blueNorm();
@@ -256,7 +257,7 @@ void ei_lmpar2(
/* if the function is small enough, accept the current value */
/* of par. also test for the exceptional cases where parl */
/* is zero or the number of iterations has reached 10. */
if (ei_abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
break;
/* compute the newton correction. */
@@ -286,3 +287,4 @@ void ei_lmpar2(
return;
}
} // end namespace internal

6
unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
View File

@@ -1,9 +1,10 @@
namespace internal {
// TODO : once qrsolv2 is removed, use ColPivHouseholderQR or PermutationMatrix instead of ipvt
template <typename Scalar>
void ei_qrsolv(
void qrsolv(
Matrix< Scalar, Dynamic, Dynamic > &s,
// TODO : use a PermutationMatrix once ei_lmpar is no more:
// TODO : use a PermutationMatrix once lmpar is no more:
const VectorXi &ipvt,
const Matrix< Scalar, Dynamic, 1 > &diag,
const Matrix< Scalar, Dynamic, 1 > &qtb,
@@ -83,3 +84,4 @@ void ei_qrsolv(
for (j = 0; j < n; ++j) x[ipvt[j]] = wa[j];
}
} // end namespace internal

4
unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
View File

@@ -1,8 +1,9 @@
namespace internal {
// TODO : move this to GivensQR once there's such a thing in Eigen
template <typename Scalar>
void ei_r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<JacobiRotation<Scalar> > &v_givens, const std::vector<JacobiRotation<Scalar> > &w_givens)
void r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<JacobiRotation<Scalar> > &v_givens, const std::vector<JacobiRotation<Scalar> > &w_givens)
{
typedef DenseIndex Index;
@@ -22,3 +23,4 @@ void ei_r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<JacobiRo
}
}
} // end namespace internal

6
unsupported/Eigen/src/NonLinearOptimization/r1updt.h
View File

@@ -1,6 +1,7 @@
namespace internal {
template <typename Scalar>
void ei_r1updt(
void r1updt(
Matrix< Scalar, Dynamic, Dynamic > &s,
const Matrix< Scalar, Dynamic, 1> &u,
std::vector<JacobiRotation<Scalar> > &v_givens,
@@ -18,7 +19,7 @@ void ei_r1updt(
Scalar temp;
JacobiRotation<Scalar> givens;
// ei_r1updt had a broader usecase, but we dont use it here. And, more
// r1updt had a broader usecase, but we dont use it here. And, more
// importantly, we can not test it.
assert(m==n);
assert(u.size()==m);
@@ -88,3 +89,4 @@ void ei_r1updt(
return;
}
} // end namespace internal

4
unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
View File

@@ -1,6 +1,7 @@
namespace internal {
template <typename Scalar>
void ei_rwupdt(
void rwupdt(
Matrix< Scalar, Dynamic, Dynamic > &r,
const Matrix< Scalar, Dynamic, 1> &w,
Matrix< Scalar, Dynamic, 1> &b,
@@ -44,3 +45,4 @@ void ei_rwupdt(
}
}
} // end namespace internal