mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2024-12-21 07:19:46 +08:00
Code Issues Packages Projects Releases Wiki Activity

oops... fixing return values, some copy/paste was done far too quickly

Browse Source
This commit is contained in:
Thomas Capricelli 2009-08-25 22:06:58 +02:00
parent 3bca4bba87
commit 41b6ea81db
2 changed files with 10 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
unsupported/Eigen/src/NonLinear/HybridNonLinearSolver.h
View File

@ -138,11 +138,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
/* check the input parameters for errors. */ /* check the input parameters for errors. */
if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. ) if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
return RelativeErrorTooSmall; return ImproperInputParameters;
if (mode == 2) if (mode == 2)
for (j = 0; j < n; ++j) for (j = 0; j < n; ++j)
if (diag[j] <= 0.) if (diag[j] <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
/* evaluate the function at the starting point */ /* evaluate the function at the starting point */
/* and calculate its norm. */ /* and calculate its norm. */
@ -337,7 +337,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solve(
if (nfev >= parameters.maxfev) if (nfev >= parameters.maxfev)
return TooManyFunctionEvaluation; return TooManyFunctionEvaluation;
/* Computing MAX */
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm) if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
return TolTooSmall; return TolTooSmall;
if (nslow2 == 5) if (nslow2 == 5)
@ -460,11 +459,11 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
/* check the input parameters for errors. */ /* check the input parameters for errors. */
if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || nb_of_subdiagonals < 0 || nb_of_superdiagonals < 0 || parameters.factor <= 0. ) if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || nb_of_subdiagonals < 0 || nb_of_superdiagonals < 0 || parameters.factor <= 0. )
return RelativeErrorTooSmall; return ImproperInputParameters;
if (mode == 2) if (mode == 2)
for (j = 0; j < n; ++j) for (j = 0; j < n; ++j)
if (diag[j] <= 0.) if (diag[j] <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
/* evaluate the function at the starting point */ /* evaluate the function at the starting point */
/* and calculate its norm. */ /* and calculate its norm. */
@ -665,7 +664,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(
if (nfev >= parameters.maxfev) if (nfev >= parameters.maxfev)
return TooManyFunctionEvaluation; return TooManyFunctionEvaluation;
/* Computing MAX */
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm) if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= epsilon<Scalar>() * xnorm)
return TolTooSmall; return TolTooSmall;
if (nslow2 == 5) if (nslow2 == 5)

12
unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
View File

@ -152,12 +152,12 @@ LevenbergMarquardt<FunctorType,Scalar>::minimize(
/* check the input parameters for errors. */ /* check the input parameters for errors. */
if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.) if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
if (mode == 2) if (mode == 2)
for (j = 0; j < n; ++j) for (j = 0; j < n; ++j)
if (diag[j] <= 0.) if (diag[j] <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
/* evaluate the function at the starting point */ /* evaluate the function at the starting point */
/* and calculate its norm. */ /* and calculate its norm. */
@ -430,11 +430,11 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeNumericalDiff(
/* check the input parameters for errors. */ /* check the input parameters for errors. */
if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.) if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
if (mode == 2) if (mode == 2)
for (j = 0; j < n; ++j) for (j = 0; j < n; ++j)
if (diag[j] <= 0.) if (diag[j] <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
/* evaluate the function at the starting point */ /* evaluate the function at the starting point */
/* and calculate its norm. */ /* and calculate its norm. */
@ -711,12 +711,12 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(
/* check the input parameters for errors. */ /* check the input parameters for errors. */
if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.) if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
if (mode == 2) if (mode == 2)
for (j = 0; j < n; ++j) for (j = 0; j < n; ++j)
if (diag[j] <= 0.) if (diag[j] <= 0.)
return RelativeErrorTooSmall; return ImproperInputParameters;
/* evaluate the function at the starting point */ /* evaluate the function at the starting point */
/* and calculate its norm. */ /* and calculate its norm. */