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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "lapack_common.h"
+#include <Eigen/SVD>
+
+#if ISCOMPLEX
+#define EIGEN_LAPACK_ARG_IF_COMPLEX(X) X,
+#else
+#define EIGEN_LAPACK_ARG_IF_COMPLEX(X)
+#endif
+
+// 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) {
+  // TODO exploit the work buffer
+  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) {
+    int e = -*info;
+    return xerbla_(SCALAR_SUFFIX_UP "GESDD ", &e);
+  }
+
+  if (query_size) {
+    *lwork = 0;
+    return;
+  }
+
+  if (*n == 0 || *m == 0) return;
+
+  PlainMatrixType mat(*m, *n);
+  mat = matrix(a, *m, *n, *lda);
+
+  int option = *jobz == 'A'   ? Eigen::ComputeFullU | Eigen::ComputeFullV
+               : *jobz == 'S' ? Eigen::ComputeThinU | Eigen::ComputeThinV
+               : *jobz == 'O' ? Eigen::ComputeThinU | Eigen::ComputeThinV
+                              : 0;
+
+  Eigen::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();
+  }
+}
+
+// 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) {
+  // TODO exploit the work buffer
+  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) {
+    int e = -*info;
+    return xerbla_(SCALAR_SUFFIX_UP "GESVD ", &e);
+  }
+
+  if (query_size) {
+    *lwork = 0;
+    return;
+  }
+
+  if (*n == 0 || *m == 0) return;
+
+  PlainMatrixType mat(*m, *n);
+  mat = matrix(a, *m, *n, *lda);
+
+  int option = (*jobu == 'A'                   ? Eigen::ComputeFullU
+                : *jobu == 'S' || *jobu == 'O' ? Eigen::ComputeThinU
+                                               : 0) |
+               (*jobv == 'A'                   ? Eigen::ComputeFullV
+                : *jobv == 'S' || *jobv == 'O' ? Eigen::ComputeThinV
+                                               : 0);
+
+  Eigen::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 (*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();
+  }
+}
+
+#undef EIGEN_LAPACK_ARG_IF_COMPLEX