diff --git a/lapack/cholesky.inc b/lapack/cholesky.inc
index ea3bc12..d38a10d 100644
--- a/lapack/cholesky.inc
+++ b/lapack/cholesky.inc
@@ -11,59 +11,62 @@
 #include <Eigen/Cholesky>
 
 // POTRF computes the Cholesky factorization of a real symmetric positive definite matrix A.
-EIGEN_LAPACK_FUNC(potrf,(char* uplo, int *n, RealScalar *pa, int *lda, int *info))
-{
+EIGEN_LAPACK_FUNC(potrf, (char *uplo, int *n, RealScalar *pa, int *lda, int *info)) {
   *info = 0;
-        if(UPLO(*uplo)==INVALID) *info = -1;
-  else  if(*n<0)                 *info = -2;
-  else  if(*lda<std::max(1,*n))  *info = -4;
-  if(*info!=0)
-  {
+  if (UPLO(*uplo) == INVALID)
+    *info = -1;
+  else if (*n < 0)
+    *info = -2;
+  else if (*lda < std::max(1, *n))
+    *info = -4;
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"POTRF", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "POTRF", &e, 6);
   }
 
-  Scalar* a = reinterpret_cast<Scalar*>(pa);
-  MatrixType A(a,*n,*n,*lda);
+  Scalar *a = reinterpret_cast<Scalar *>(pa);
+  MatrixType A(a, *n, *n, *lda);
   int ret;
-  if(UPLO(*uplo)==UP) ret = int(internal::llt_inplace<Scalar, Upper>::blocked(A));
-  else                ret = int(internal::llt_inplace<Scalar, Lower>::blocked(A));
+  if (UPLO(*uplo) == UP)
+    ret = int(internal::llt_inplace<Scalar, Upper>::blocked(A));
+  else
+    ret = int(internal::llt_inplace<Scalar, Lower>::blocked(A));
 
-  if(ret>=0)
-    *info = ret+1;
-  
+  if (ret >= 0) *info = ret + 1;
+
   return 0;
 }
 
 // POTRS solves a system of linear equations A*X = B with a symmetric
 // positive definite matrix A using the Cholesky factorization
 // A = U**T*U or A = L*L**T computed by DPOTRF.
-EIGEN_LAPACK_FUNC(potrs,(char* uplo, int *n, int *nrhs, RealScalar *pa, int *lda, RealScalar *pb, int *ldb, int *info))
-{
+EIGEN_LAPACK_FUNC(potrs,
+                  (char *uplo, int *n, int *nrhs, RealScalar *pa, int *lda, RealScalar *pb, int *ldb, int *info)) {
   *info = 0;
-        if(UPLO(*uplo)==INVALID) *info = -1;
-  else  if(*n<0)                 *info = -2;
-  else  if(*nrhs<0)              *info = -3;
-  else  if(*lda<std::max(1,*n))  *info = -5;
-  else  if(*ldb<std::max(1,*n))  *info = -7;
-  if(*info!=0)
-  {
+  if (UPLO(*uplo) == INVALID)
+    *info = -1;
+  else if (*n < 0)
+    *info = -2;
+  else if (*nrhs < 0)
+    *info = -3;
+  else if (*lda < std::max(1, *n))
+    *info = -5;
+  else if (*ldb < std::max(1, *n))
+    *info = -7;
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"POTRS", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "POTRS", &e, 6);
   }
 
-  Scalar* a = reinterpret_cast<Scalar*>(pa);
-  Scalar* b = reinterpret_cast<Scalar*>(pb);
-  MatrixType A(a,*n,*n,*lda);
-  MatrixType B(b,*n,*nrhs,*ldb);
+  Scalar *a = reinterpret_cast<Scalar *>(pa);
+  Scalar *b = reinterpret_cast<Scalar *>(pb);
+  MatrixType A(a, *n, *n, *lda);
+  MatrixType B(b, *n, *nrhs, *ldb);
 
-  if(UPLO(*uplo)==UP)
-  {
+  if (UPLO(*uplo) == UP) {
     A.triangularView<Upper>().adjoint().solveInPlace(B);
     A.triangularView<Upper>().solveInPlace(B);
-  }
-  else
-  {
+  } else {
     A.triangularView<Lower>().solveInPlace(B);
     A.triangularView<Lower>().adjoint().solveInPlace(B);
   }
diff --git a/lapack/eigenvalues.inc b/lapack/eigenvalues.inc
index 921c515..62192f4 100644
--- a/lapack/eigenvalues.inc
+++ b/lapack/eigenvalues.inc
@@ -11,52 +11,53 @@
 #include <Eigen/Eigenvalues>
 
 // computes eigen values and vectors of a general N-by-N matrix A
-EIGEN_LAPACK_FUNC(syev,(char *jobz, char *uplo, int* n, Scalar* a, int *lda, Scalar* w, Scalar* /*work*/, int* lwork, int *info))
-{
+EIGEN_LAPACK_FUNC(syev, (char* jobz, char* uplo, int* n, Scalar* a, int* lda, Scalar* w, Scalar* /*work*/, int* lwork,
+                         int* info)) {
   // TODO exploit the work buffer
-  bool query_size = *lwork==-1;
-  
+  bool query_size = *lwork == -1;
+
   *info = 0;
-        if(*jobz!='N' && *jobz!='V')                    *info = -1;
-  else  if(UPLO(*uplo)==INVALID)                        *info = -2;
-  else  if(*n<0)                                        *info = -3;
-  else  if(*lda<std::max(1,*n))                         *info = -5;
-  else  if((!query_size) && *lwork<std::max(1,3**n-1))  *info = -8;
-    
-  if(*info!=0)
-  {
+  if (*jobz != 'N' && *jobz != 'V')
+    *info = -1;
+  else if (UPLO(*uplo) == INVALID)
+    *info = -2;
+  else if (*n < 0)
+    *info = -3;
+  else if (*lda < std::max(1, *n))
+    *info = -5;
+  else if ((!query_size) && *lwork < std::max(1, 3 * *n - 1))
+    *info = -8;
+
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"SYEV ", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "SYEV ", &e, 6);
   }
-  
-  if(query_size)
-  {
+
+  if (query_size) {
     *lwork = 0;
     return 0;
   }
-  
-  if(*n==0)
-    return 0;
-  
-  PlainMatrixType mat(*n,*n);
-  if(UPLO(*uplo)==UP) mat = matrix(a,*n,*n,*lda).adjoint();
-  else                mat = matrix(a,*n,*n,*lda);
-  
-  bool computeVectors = *jobz=='V' || *jobz=='v';
-  SelfAdjointEigenSolver<PlainMatrixType> eig(mat,computeVectors?ComputeEigenvectors:EigenvaluesOnly);
-  
-  if(eig.info()==NoConvergence)
-  {
-    make_vector(w,*n).setZero();
-    if(computeVectors)
-      matrix(a,*n,*n,*lda).setIdentity();
+
+  if (*n == 0) return 0;
+
+  PlainMatrixType mat(*n, *n);
+  if (UPLO(*uplo) == UP)
+    mat = matrix(a, *n, *n, *lda).adjoint();
+  else
+    mat = matrix(a, *n, *n, *lda);
+
+  bool computeVectors = *jobz == 'V' || *jobz == 'v';
+  SelfAdjointEigenSolver<PlainMatrixType> eig(mat, computeVectors ? ComputeEigenvectors : EigenvaluesOnly);
+
+  if (eig.info() == NoConvergence) {
+    make_vector(w, *n).setZero();
+    if (computeVectors) matrix(a, *n, *n, *lda).setIdentity();
     //*info = 1;
     return 0;
   }
-  
-  make_vector(w,*n) = eig.eigenvalues();
-  if(computeVectors)
-    matrix(a,*n,*n,*lda) = eig.eigenvectors();
-  
+
+  make_vector(w, *n) = eig.eigenvalues();
+  if (computeVectors) matrix(a, *n, *n, *lda) = eig.eigenvectors();
+
   return 0;
 }
diff --git a/lapack/lu.inc b/lapack/lu.inc
index 90cebe0..ca64d90 100644
--- a/lapack/lu.inc
+++ b/lapack/lu.inc
@@ -11,79 +11,74 @@
 #include <Eigen/LU>
 
 // computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges
-EIGEN_LAPACK_FUNC(getrf,(int *m, int *n, RealScalar *pa, int *lda, int *ipiv, int *info))
-{
+EIGEN_LAPACK_FUNC(getrf, (int *m, int *n, RealScalar *pa, int *lda, int *ipiv, int *info)) {
   *info = 0;
-        if(*m<0)                  *info = -1;
-  else  if(*n<0)                  *info = -2;
-  else  if(*lda<std::max(1,*m))   *info = -4;
-  if(*info!=0)
-  {
+  if (*m < 0)
+    *info = -1;
+  else if (*n < 0)
+    *info = -2;
+  else if (*lda < std::max(1, *m))
+    *info = -4;
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"GETRF", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "GETRF", &e, 6);
   }
 
-  if(*m==0 || *n==0)
-    return 0;
+  if (*m == 0 || *n == 0) return 0;
 
-  Scalar* a = reinterpret_cast<Scalar*>(pa);
+  Scalar *a = reinterpret_cast<Scalar *>(pa);
   int nb_transpositions;
-  int ret = int(Eigen::internal::partial_lu_impl<Scalar,ColMajor,int>
-                     ::blocked_lu(*m, *n, a, *lda, ipiv, nb_transpositions));
+  int ret = int(
+      Eigen::internal::partial_lu_impl<Scalar, ColMajor, int>::blocked_lu(*m, *n, a, *lda, ipiv, nb_transpositions));
 
-  for(int i=0; i<std::min(*m,*n); ++i)
-    ipiv[i]++;
+  for (int i = 0; i < std::min(*m, *n); ++i) ipiv[i]++;
 
-  if(ret>=0)
-    *info = ret+1;
+  if (ret >= 0) *info = ret + 1;
 
   return 0;
 }
 
-//GETRS solves a system of linear equations
-//    A * X = B  or  A' * X = B
-//  with a general N-by-N matrix A using the LU factorization computed  by GETRF
-EIGEN_LAPACK_FUNC(getrs,(char *trans, int *n, int *nrhs, RealScalar *pa, int *lda, int *ipiv, RealScalar *pb, int *ldb, int *info))
-{
+// GETRS solves a system of linear equations
+//     A * X = B  or  A' * X = B
+//   with a general N-by-N matrix A using the LU factorization computed  by GETRF
+EIGEN_LAPACK_FUNC(getrs, (char *trans, int *n, int *nrhs, RealScalar *pa, int *lda, int *ipiv, RealScalar *pb, int *ldb,
+                          int *info)) {
   *info = 0;
-        if(OP(*trans)==INVALID)  *info = -1;
-  else  if(*n<0)                 *info = -2;
-  else  if(*nrhs<0)              *info = -3;
-  else  if(*lda<std::max(1,*n))  *info = -5;
-  else  if(*ldb<std::max(1,*n))  *info = -8;
-  if(*info!=0)
-  {
+  if (OP(*trans) == INVALID)
+    *info = -1;
+  else if (*n < 0)
+    *info = -2;
+  else if (*nrhs < 0)
+    *info = -3;
+  else if (*lda < std::max(1, *n))
+    *info = -5;
+  else if (*ldb < std::max(1, *n))
+    *info = -8;
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"GETRS", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "GETRS", &e, 6);
   }
 
-  Scalar* a = reinterpret_cast<Scalar*>(pa);
-  Scalar* b = reinterpret_cast<Scalar*>(pb);
-  MatrixType lu(a,*n,*n,*lda);
-  MatrixType B(b,*n,*nrhs,*ldb);
+  Scalar *a = reinterpret_cast<Scalar *>(pa);
+  Scalar *b = reinterpret_cast<Scalar *>(pb);
+  MatrixType lu(a, *n, *n, *lda);
+  MatrixType B(b, *n, *nrhs, *ldb);
 
-  for(int i=0; i<*n; ++i)
-    ipiv[i]--;
-  if(OP(*trans)==NOTR)
-  {
-    B = PivotsType(ipiv,*n) * B;
+  for (int i = 0; i < *n; ++i) ipiv[i]--;
+  if (OP(*trans) == NOTR) {
+    B = PivotsType(ipiv, *n) * B;
     lu.triangularView<UnitLower>().solveInPlace(B);
     lu.triangularView<Upper>().solveInPlace(B);
-  }
-  else if(OP(*trans)==TR)
-  {
+  } else if (OP(*trans) == TR) {
     lu.triangularView<Upper>().transpose().solveInPlace(B);
     lu.triangularView<UnitLower>().transpose().solveInPlace(B);
-    B = PivotsType(ipiv,*n).transpose() * B;
-  }
-  else if(OP(*trans)==ADJ)
-  {
+    B = PivotsType(ipiv, *n).transpose() * B;
+  } else if (OP(*trans) == ADJ) {
     lu.triangularView<Upper>().adjoint().solveInPlace(B);
     lu.triangularView<UnitLower>().adjoint().solveInPlace(B);
-    B = PivotsType(ipiv,*n).transpose() * B;
+    B = PivotsType(ipiv, *n).transpose() * B;
   }
-  for(int i=0; i<*n; ++i)
-    ipiv[i]++;
+  for (int i = 0; i < *n; ++i) ipiv[i]++;
 
   return 0;
 }
diff --git a/lapack/svd.inc b/lapack/svd.inc
index 83544cf..a278cf0 100644
--- a/lapack/svd.inc
+++ b/lapack/svd.inc
@@ -11,128 +11,135 @@
 #include <Eigen/SVD>
 
 // computes the singular values/vectors a general M-by-N matrix A using divide-and-conquer
-EIGEN_LAPACK_FUNC(gesdd,(char *jobz, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork,
-                         EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int * /*iwork*/, int *info))
-{
+EIGEN_LAPACK_FUNC(gesdd, (char *jobz, int *m, int *n, Scalar *a, int *lda, RealScalar *s, Scalar *u, int *ldu,
+                          Scalar *vt, int *ldvt, Scalar * /*work*/, int *lwork,
+                          EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar * /*rwork*/) int * /*iwork*/, int *info)) {
   // TODO exploit the work buffer
-  bool query_size = *lwork==-1;
-  int diag_size = (std::min)(*m,*n);
-  
+  bool query_size = *lwork == -1;
+  int diag_size = (std::min)(*m, *n);
+
   *info = 0;
-        if(*jobz!='A' && *jobz!='S' && *jobz!='O' && *jobz!='N')  *info = -1;
-  else  if(*m<0)                                                  *info = -2;
-  else  if(*n<0)                                                  *info = -3;
-  else  if(*lda<std::max(1,*m))                                   *info = -5;
-  else  if(*lda<std::max(1,*m))                                   *info = -8;
-  else  if(*ldu <1 || (*jobz=='A' && *ldu <*m)
-                   || (*jobz=='O' && *m<*n && *ldu<*m))           *info = -8;
-  else  if(*ldvt<1 || (*jobz=='A' && *ldvt<*n)
-                   || (*jobz=='S' && *ldvt<diag_size)
-                   || (*jobz=='O' && *m>=*n && *ldvt<*n))         *info = -10;
-  
-  if(*info!=0)
-  {
+  if (*jobz != 'A' && *jobz != 'S' && *jobz != 'O' && *jobz != 'N')
+    *info = -1;
+  else if (*m < 0)
+    *info = -2;
+  else if (*n < 0)
+    *info = -3;
+  else if (*lda < std::max(1, *m))
+    *info = -5;
+  else if (*lda < std::max(1, *m))
+    *info = -8;
+  else if (*ldu < 1 || (*jobz == 'A' && *ldu < *m) || (*jobz == 'O' && *m < *n && *ldu < *m))
+    *info = -8;
+  else if (*ldvt < 1 || (*jobz == 'A' && *ldvt < *n) || (*jobz == 'S' && *ldvt < diag_size) ||
+           (*jobz == 'O' && *m >= *n && *ldvt < *n))
+    *info = -10;
+
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"GESDD ", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "GESDD ", &e, 6);
   }
-  
-  if(query_size)
-  {
+
+  if (query_size) {
     *lwork = 0;
     return 0;
   }
-  
-  if(*n==0 || *m==0)
-    return 0;
-  
-  PlainMatrixType mat(*m,*n);
-  mat = matrix(a,*m,*n,*lda);
-  
-  int option = *jobz=='A' ? ComputeFullU|ComputeFullV
-             : *jobz=='S' ? ComputeThinU|ComputeThinV
-             : *jobz=='O' ? ComputeThinU|ComputeThinV
-             : 0;
 
-  BDCSVD<PlainMatrixType> svd(mat,option);
-  
-  make_vector(s,diag_size) = svd.singularValues().head(diag_size);
+  if (*n == 0 || *m == 0) return 0;
 
-  if(*jobz=='A')
-  {
-    matrix(u,*m,*m,*ldu)   = svd.matrixU();
-    matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
+  PlainMatrixType mat(*m, *n);
+  mat = matrix(a, *m, *n, *lda);
+
+  int option = *jobz == 'A'   ? ComputeFullU | ComputeFullV
+               : *jobz == 'S' ? ComputeThinU | ComputeThinV
+               : *jobz == 'O' ? ComputeThinU | ComputeThinV
+                              : 0;
+
+  BDCSVD<PlainMatrixType> svd(mat, option);
+
+  make_vector(s, diag_size) = svd.singularValues().head(diag_size);
+
+  if (*jobz == 'A') {
+    matrix(u, *m, *m, *ldu) = svd.matrixU();
+    matrix(vt, *n, *n, *ldvt) = svd.matrixV().adjoint();
+  } else if (*jobz == 'S') {
+    matrix(u, *m, diag_size, *ldu) = svd.matrixU();
+    matrix(vt, diag_size, *n, *ldvt) = svd.matrixV().adjoint();
+  } else if (*jobz == 'O' && *m >= *n) {
+    matrix(a, *m, *n, *lda) = svd.matrixU();
+    matrix(vt, *n, *n, *ldvt) = svd.matrixV().adjoint();
+  } else if (*jobz == 'O') {
+    matrix(u, *m, *m, *ldu) = svd.matrixU();
+    matrix(a, diag_size, *n, *lda) = svd.matrixV().adjoint();
   }
-  else if(*jobz=='S')
-  {
-    matrix(u,*m,diag_size,*ldu)   = svd.matrixU();
-    matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
-  }
-  else if(*jobz=='O' && *m>=*n)
-  {
-    matrix(a,*m,*n,*lda)   = svd.matrixU();
-    matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
-  }
-  else if(*jobz=='O')
-  {
-    matrix(u,*m,*m,*ldu)        = svd.matrixU();
-    matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
-  }
-    
+
   return 0;
 }
 
 // computes the singular values/vectors a general M-by-N matrix A using two sided jacobi algorithm
-EIGEN_LAPACK_FUNC(gesvd,(char *jobu, char *jobv, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork,
-                         EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int *info))
-{
+EIGEN_LAPACK_FUNC(gesvd, (char *jobu, char *jobv, int *m, int *n, Scalar *a, int *lda, RealScalar *s, Scalar *u,
+                          int *ldu, Scalar *vt, int *ldvt, Scalar * /*work*/, int *lwork,
+                          EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar * /*rwork*/) int *info)) {
   // TODO exploit the work buffer
-  bool query_size = *lwork==-1;
-  int diag_size = (std::min)(*m,*n);
-  
+  bool query_size = *lwork == -1;
+  int diag_size = (std::min)(*m, *n);
+
   *info = 0;
-        if( *jobu!='A' && *jobu!='S' && *jobu!='O' && *jobu!='N') *info = -1;
-  else  if((*jobv!='A' && *jobv!='S' && *jobv!='O' && *jobv!='N')
-           || (*jobu=='O' && *jobv=='O'))                         *info = -2;
-  else  if(*m<0)                                                  *info = -3;
-  else  if(*n<0)                                                  *info = -4;
-  else  if(*lda<std::max(1,*m))                                   *info = -6;
-  else  if(*ldu <1 || ((*jobu=='A' || *jobu=='S') && *ldu<*m))    *info = -9;
-  else  if(*ldvt<1 || (*jobv=='A' && *ldvt<*n)
-                   || (*jobv=='S' && *ldvt<diag_size))            *info = -11;
-  
-  if(*info!=0)
-  {
+  if (*jobu != 'A' && *jobu != 'S' && *jobu != 'O' && *jobu != 'N')
+    *info = -1;
+  else if ((*jobv != 'A' && *jobv != 'S' && *jobv != 'O' && *jobv != 'N') || (*jobu == 'O' && *jobv == 'O'))
+    *info = -2;
+  else if (*m < 0)
+    *info = -3;
+  else if (*n < 0)
+    *info = -4;
+  else if (*lda < std::max(1, *m))
+    *info = -6;
+  else if (*ldu < 1 || ((*jobu == 'A' || *jobu == 'S') && *ldu < *m))
+    *info = -9;
+  else if (*ldvt < 1 || (*jobv == 'A' && *ldvt < *n) || (*jobv == 'S' && *ldvt < diag_size))
+    *info = -11;
+
+  if (*info != 0) {
     int e = -*info;
-    return xerbla_(SCALAR_SUFFIX_UP"GESVD ", &e, 6);
+    return xerbla_(SCALAR_SUFFIX_UP "GESVD ", &e, 6);
   }
-  
-  if(query_size)
-  {
+
+  if (query_size) {
     *lwork = 0;
     return 0;
   }
-  
-  if(*n==0 || *m==0)
-    return 0;
-  
-  PlainMatrixType mat(*m,*n);
-  mat = matrix(a,*m,*n,*lda);
-  
-  int option = (*jobu=='A' ? ComputeFullU : *jobu=='S' || *jobu=='O' ? ComputeThinU : 0)
-             | (*jobv=='A' ? ComputeFullV : *jobv=='S' || *jobv=='O' ? ComputeThinV : 0);
-  
-  JacobiSVD<PlainMatrixType> svd(mat,option);
-  
-  make_vector(s,diag_size) = svd.singularValues().head(diag_size);
+
+  if (*n == 0 || *m == 0) return 0;
+
+  PlainMatrixType mat(*m, *n);
+  mat = matrix(a, *m, *n, *lda);
+
+  int option = (*jobu == 'A'                   ? ComputeFullU
+                : *jobu == 'S' || *jobu == 'O' ? ComputeThinU
+                                               : 0) |
+               (*jobv == 'A'                   ? ComputeFullV
+                : *jobv == 'S' || *jobv == 'O' ? ComputeThinV
+                                               : 0);
+
+  JacobiSVD<PlainMatrixType> svd(mat, option);
+
+  make_vector(s, diag_size) = svd.singularValues().head(diag_size);
   {
-        if(*jobu=='A') matrix(u,*m,*m,*ldu)           = svd.matrixU();
-  else  if(*jobu=='S') matrix(u,*m,diag_size,*ldu)    = svd.matrixU();
-  else  if(*jobu=='O') matrix(a,*m,diag_size,*lda)    = svd.matrixU();
+    if (*jobu == 'A')
+      matrix(u, *m, *m, *ldu) = svd.matrixU();
+    else if (*jobu == 'S')
+      matrix(u, *m, diag_size, *ldu) = svd.matrixU();
+    else if (*jobu == 'O')
+      matrix(a, *m, diag_size, *lda) = svd.matrixU();
   }
   {
-        if(*jobv=='A') matrix(vt,*n,*n,*ldvt)         = svd.matrixV().adjoint();
-  else  if(*jobv=='S') matrix(vt,diag_size,*n,*ldvt)  = svd.matrixV().adjoint();
-  else  if(*jobv=='O') matrix(a,diag_size,*n,*lda)    = svd.matrixV().adjoint();
+    if (*jobv == 'A')
+      matrix(vt, *n, *n, *ldvt) = svd.matrixV().adjoint();
+    else if (*jobv == 'S')
+      matrix(vt, diag_size, *n, *ldvt) = svd.matrixV().adjoint();
+    else if (*jobv == 'O')
+      matrix(a, diag_size, *n, *lda) = svd.matrixV().adjoint();
   }
   return 0;
 }
\ No newline at end of file