Apply clang-format to lapack/blas directories
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
