remove spaces

3 files changed, 0 insertions, 69 deletions

diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
index 4b985db74..cbf89b1c6 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -429,7 +429,6 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(
 
 
     /* Function Body */
-
     nfev = 0;
     njev = 0;
 
diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
index b9e3c808e..0d6e051ca 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -207,7 +207,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
     njev = 0;
 
     /*     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.)
         return ImproperInputParameters;
 
@@ -218,14 +217,12 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(
 
     /*     evaluate the function at the starting point */
     /*     and calculate its norm. */
-
     nfev = 1;
     if ( functor(x, fvec) < 0)
         return UserAsked;
     fnorm = fvec.stableNorm();
 
     /*     initialize levenberg-marquardt parameter and iteration counter. */
-
     par = 0.;
     iter = 1;
 
@@ -242,7 +239,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
     int i, j, l;
 
     /* calculate the jacobian matrix. */
-
     int df_ret = functor.df(x, fjac);
     if (df_ret<0)
         return UserAsked;
@@ -252,7 +248,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
     else njev++;
 
     /* compute the qr factorization of the jacobian. */
-
     wa2 = fjac.colwise().blueNorm();
     ColPivHouseholderQR<JacobianType> qrfac(fjac);
     fjac = qrfac.matrixQR();
@@ -264,7 +259,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
     /* on the first iteration and if mode is 1, scale according */
     /* to the norms of the columns of the initial jacobian. */
-
     if (iter == 1) {
         if (mode != 2)
             for (j = 0; j < n; ++j) {
@@ -275,7 +269,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
-
         wa3 = diag.cwiseProduct(x);
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
@@ -285,7 +278,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
     /* form (q transpose)*fvec and store the first n components in */
     /* qtf. */
-
 #if 0
     // find a way to only compute the first n items, we have m>>n here.
     wa4 = fvec;
@@ -309,7 +301,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 #endif
 
     /* compute the norm of the scaled gradient. */
-
     gnorm = 0.;
     if (fnorm != 0.)
         for (j = 0; j < n; ++j) {
@@ -324,12 +315,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         }
 
     /* test for convergence of the gradient norm. */
-
     if (gnorm <= parameters.gtol)
         return CosinusTooSmall;
 
     /* rescale if necessary. */
-
     if (mode != 2) /* Computing MAX */
         diag = diag.cwiseMax(wa2);
 
@@ -337,37 +326,31 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
     do {
 
         /* determine the levenberg-marquardt parameter. */
-
         ei_lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
 
         /* store the direction p and x + p. calculate the norm of p. */
-
         wa1 = -wa1;
         wa2 = x + wa1;
         wa3 = diag.cwiseProduct(wa1);
         pnorm = wa3.stableNorm();
 
         /* on the first iteration, adjust the initial step bound. */
-
         if (iter == 1)
             delta = std::min(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
-
         if ( functor(wa2, wa4) < 0)
             return UserAsked;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
         /* compute the scaled actual reduction. */
-
         actred = -1.;
         if (Scalar(.1) * fnorm1 < fnorm) /* Computing 2nd power */
             actred = 1. - ei_abs2(fnorm1 / fnorm);
 
         /* compute the scaled predicted reduction and */
         /* the scaled directional derivative. */
-
         wa3.fill(0.);
         for (j = 0; j < n; ++j) {
             l = ipvt[j];
@@ -383,13 +366,11 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
 
         /* compute the ratio of the actual to the predicted */
         /* reduction. */
-
         ratio = 0.;
         if (prered != 0.)
             ratio = actred / prered;
 
         /* update the step bound. */
-
         if (ratio <= Scalar(.25)) {
             if (actred >= 0.)
                 temp = Scalar(.5);
@@ -406,7 +387,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         }
 
         /* test for successful iteration. */
-
         if (ratio >= Scalar(1e-4)) {
             /* successful iteration. update x, fvec, and their norms. */
             x = wa2;
@@ -418,7 +398,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
         }
 
         /* tests for convergence. */
-
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
             return RelativeErrorAndReductionTooSmall;
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
@@ -427,7 +406,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(
             return RelativeErrorTooSmall;
 
         /* tests for termination and stringent tolerances. */
-
         if (nfev >= parameters.maxfev)
             return TooManyFunctionEvaluation;
         if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
@@ -489,7 +467,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
     njev = 0;
 
     /*     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.)
         return ImproperInputParameters;
 
@@ -500,14 +477,12 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(
 
     /*     evaluate the function at the starting point */
     /*     and calculate its norm. */
-
     nfev = 1;
     if ( functor(x, fvec) < 0)
         return UserAsked;
     fnorm = fvec.stableNorm();
 
     /*     initialize levenberg-marquardt parameter and iteration counter. */
-
     par = 0.;
     iter = 1;
 
@@ -529,7 +504,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
     /* calculated one row at a time, while simultaneously */
     /* forming (q transpose)*fvec and storing the first */
     /* n components in qtf. */
-
     qtf.fill(0.);
     fjac.fill(0.);
     int rownb = 2;
@@ -543,7 +517,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
     /* if the jacobian is rank deficient, call qrfac to */
     /* reorder its columns and update the components of qtf. */
-
     sing = false;
     for (j = 0; j < n; ++j) {
         if (fjac(j,j) == 0.) {
@@ -578,7 +551,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
     /* on the first iteration and if mode is 1, scale according */
     /* to the norms of the columns of the initial jacobian. */
-
     if (iter == 1) {
         if (mode != 2)
             for (j = 0; j < n; ++j) {
@@ -589,7 +561,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
         /* on the first iteration, calculate the norm of the scaled x */
         /* and initialize the step bound delta. */
-
         wa3 = diag.cwiseProduct(x);
         xnorm = wa3.stableNorm();
         delta = parameters.factor * xnorm;
@@ -598,7 +569,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
     }
 
     /* compute the norm of the scaled gradient. */
-
     gnorm = 0.;
     if (fnorm != 0.)
         for (j = 0; j < n; ++j) {
@@ -613,12 +583,10 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         }
 
     /* test for convergence of the gradient norm. */
-
     if (gnorm <= parameters.gtol)
         return CosinusTooSmall;
 
     /* rescale if necessary. */
-
     if (mode != 2) /* Computing MAX */
         diag = diag.cwiseMax(wa2);
 
@@ -626,37 +594,31 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
     do {
 
         /* determine the levenberg-marquardt parameter. */
-
         ei_lmpar<Scalar>(fjac, ipvt, diag, qtf, delta, par, wa1);
 
         /* store the direction p and x + p. calculate the norm of p. */
-
         wa1 = -wa1;
         wa2 = x + wa1;
         wa3 = diag.cwiseProduct(wa1);
         pnorm = wa3.stableNorm();
 
         /* on the first iteration, adjust the initial step bound. */
-
         if (iter == 1)
             delta = std::min(delta,pnorm);
 
         /* evaluate the function at x + p and calculate its norm. */
-
         if ( functor(wa2, wa4) < 0)
             return UserAsked;
         ++nfev;
         fnorm1 = wa4.stableNorm();
 
         /* compute the scaled actual reduction. */
-
         actred = -1.;
         if (Scalar(.1) * fnorm1 < fnorm) /* Computing 2nd power */
             actred = 1. - ei_abs2(fnorm1 / fnorm);
 
         /* compute the scaled predicted reduction and */
         /* the scaled directional derivative. */
-
         wa3.fill(0.);
         for (j = 0; j < n; ++j) {
             l = ipvt[j];
@@ -672,13 +634,11 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
 
         /* compute the ratio of the actual to the predicted */
         /* reduction. */
-
         ratio = 0.;
         if (prered != 0.)
             ratio = actred / prered;
 
         /* update the step bound. */
-
         if (ratio <= Scalar(.25)) {
             if (actred >= 0.)
                 temp = Scalar(.5);
@@ -695,7 +655,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         }
 
         /* test for successful iteration. */
-
         if (ratio >= Scalar(1e-4)) {
             /* successful iteration. update x, fvec, and their norms. */
             x = wa2;
@@ -707,7 +666,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
         }
 
         /* tests for convergence. */
-
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
             return RelativeErrorAndReductionTooSmall;
         if (ei_abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
@@ -716,7 +674,6 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(
             return RelativeErrorTooSmall;
 
         /* tests for termination and stringent tolerances. */
-
         if (nfev >= parameters.maxfev)
             return TooManyFunctionEvaluation;
         if (ei_abs(actred) <= epsilon<Scalar>() && prered <= epsilon<Scalar>() && Scalar(.5) * ratio <= 1.)
diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
index 54c84960a..7f471f60e 100644
--- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
+++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -30,7 +30,6 @@ void ei_lmpar(
 
     /* compute and store in x the gauss-newton direction. if the */
     /* jacobian is rank-deficient, obtain a least squares solution. */
-
     int nsing = n-1;
     wa1 = qtb;
     for (j = 0; j < n; ++j) {
@@ -52,7 +51,6 @@ void ei_lmpar(
     /* initialize the iteration counter. */
     /* evaluate the function at the origin, and test */
     /* for acceptance of the gauss-newton direction. */
-
     iter = 0;
     wa2 = diag.cwiseProduct(x);
     dxnorm = wa2.blueNorm();
@@ -65,7 +63,6 @@ void ei_lmpar(
     /* if the jacobian is not rank deficient, the newton */
     /* step provides a lower bound, parl, for the zero of */
     /* the function. otherwise set this bound to zero. */
-
     parl = 0.;
     if (nsing >= n-1) {
         for (j = 0; j < n; ++j) {
@@ -85,7 +82,6 @@ void ei_lmpar(
     }
 
     /* calculate an upper bound, paru, for the zero of the function. */
-
     for (j = 0; j < n; ++j)
         wa1[j] = r.col(j).head(j+1).dot(qtb.head(j+1)) / diag[ipvt[j]];
 
@@ -96,22 +92,18 @@ void ei_lmpar(
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
-
     par = std::max(par,parl);
     par = std::min(par,paru);
     if (par == 0.)
         par = gnorm / dxnorm;
 
     /* beginning of an iteration. */
-
     while (true) {
         ++iter;
 
         /* 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;
 
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
@@ -125,12 +117,10 @@ 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)
             break;
 
         /* compute the newton correction. */
-
         for (j = 0; j < n; ++j) {
             l = ipvt[j];
             wa1[j] = diag[l] * (wa2[l] / dxnorm);
@@ -145,23 +135,19 @@ void ei_lmpar(
         parc = fp / delta / temp / temp;
 
         /* depending on the sign of the function, update parl or paru. */
-
         if (fp > 0.)
             parl = std::max(parl,par);
         if (fp < 0.)
             paru = std::min(paru,par);
 
         /* compute an improved estimate for par. */
-
         /* Computing MAX */
         par = std::max(parl,par+parc);
 
         /* end of an iteration. */
-
     }
 
     /* termination. */
-
     if (iter == 0)
         par = 0.;
     return;
@@ -198,7 +184,6 @@ void ei_lmpar2(
 
     /* compute and store in x the gauss-newton direction. if the */
     /* jacobian is rank-deficient, obtain a least squares solution. */
-
 //    const int rank = qr.nonzeroPivots(); // exactly double(0.)
     const int rank = qr.rank(); // use a threshold
     wa1 = qtb; wa1.segment(rank,n-rank).setZero();
@@ -209,7 +194,6 @@ void ei_lmpar2(
     /* initialize the iteration counter. */
     /* evaluate the function at the origin, and test */
     /* for acceptance of the gauss-newton direction. */
-
     iter = 0;
     wa2 = diag.cwiseProduct(x);
     dxnorm = wa2.blueNorm();
@@ -222,7 +206,6 @@ void ei_lmpar2(
     /* if the jacobian is not rank deficient, the newton */
     /* step provides a lower bound, parl, for the zero of */
     /* the function. otherwise set this bound to zero. */
-
     parl = 0.;
     if (rank==n) {
         wa1 = qr.colsPermutation().inverse() *  diag.cwiseProduct(wa2)/dxnorm;
@@ -232,7 +215,6 @@ void ei_lmpar2(
     }
 
     /* calculate an upper bound, paru, for the zero of the function. */
-
     for (j = 0; j < n; ++j)
         wa1[j] = qr.matrixQR().col(j).head(j+1).dot(qtb.head(j+1)) / diag[qr.colsPermutation().indices()(j)];
 
@@ -243,23 +225,19 @@ void ei_lmpar2(
 
     /* if the input par lies outside of the interval (parl,paru), */
     /* set par to the closer endpoint. */
-
     par = std::max(par,parl);
     par = std::min(par,paru);
     if (par == 0.)
         par = gnorm / dxnorm;
 
     /* beginning of an iteration. */
-
     Matrix< Scalar, Dynamic, Dynamic > s = qr.matrixQR();
     while (true) {
         ++iter;
 
         /* 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;
 
         Matrix< Scalar, Dynamic, 1 > sdiag(n);
@@ -273,12 +251,10 @@ 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)
             break;
 
         /* compute the newton correction. */
-
         wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
         for (j = 0; j < n; ++j) {
             wa1[j] /= sdiag[j];
@@ -290,7 +266,6 @@ void ei_lmpar2(
         parc = fp / delta / temp / temp;
 
         /* depending on the sign of the function, update parl or paru. */
-
         if (fp > 0.)
             parl = std::max(parl,par);
         if (fp < 0.)