diff --git a/lapack/lapack64/lapack64.go b/lapack/lapack64/lapack64.go
index 68a9a50a..208ee1f4 100644
--- a/lapack/lapack64/lapack64.go
+++ b/lapack/lapack64/lapack64.go
@@ -19,6 +19,13 @@ func Use(l lapack.Float64) {
 	lapack64 = l
 }
 
+func max(a, b int) int {
+	if a > b {
+		return a
+	}
+	return b
+}
+
 // Potrf computes the Cholesky factorization of a.
 // The factorization has the form
 //  A = U^T * U if a.Uplo == blas.Upper, or
@@ -28,7 +35,7 @@ func Use(l lapack.Float64) {
 // a and t is shared. The returned bool indicates whether a is positive
 // definite and the factorization could be finished.
 func Potrf(a blas64.Symmetric) (t blas64.Triangular, ok bool) {
-	ok = lapack64.Dpotrf(a.Uplo, a.N, a.Data, a.Stride)
+	ok = lapack64.Dpotrf(a.Uplo, a.N, a.Data, max(1, a.Stride))
 	t.Uplo = a.Uplo
 	t.N = a.N
 	t.Data = a.Data
@@ -49,7 +56,7 @@ func Potrf(a blas64.Symmetric) (t blas64.Triangular, ok bool) {
 //
 // The returned bool indicates whether the inverse was computed successfully.
 func Potri(t blas64.Triangular) (a blas64.Symmetric, ok bool) {
-	ok = lapack64.Dpotri(t.Uplo, t.N, t.Data, t.Stride)
+	ok = lapack64.Dpotri(t.Uplo, t.N, t.Data, max(1, t.Stride))
 	a.Uplo = t.Uplo
 	a.N = t.N
 	a.Data = t.Data
@@ -63,7 +70,7 @@ func Potri(t blas64.Triangular) (a blas64.Symmetric, ok bool) {
 // triangular factor as returned by Potrf. On entry, B contains the right-hand
 // side matrix B, on return it contains the solution matrix X.
 func Potrs(t blas64.Triangular, b blas64.General) {
-	lapack64.Dpotrs(t.Uplo, t.N, b.Cols, t.Data, t.Stride, b.Data, b.Stride)
+	lapack64.Dpotrs(t.Uplo, t.N, b.Cols, t.Data, max(1, t.Stride), b.Data, max(1, b.Stride))
 }
 
 // Gecon estimates the reciprocal of the condition number of the n×n matrix A
@@ -78,7 +85,7 @@ func Potrs(t blas64.Triangular, b blas64.General) {
 //
 // iwork is a temporary data slice of length at least n and Gecon will panic otherwise.
 func Gecon(norm lapack.MatrixNorm, a blas64.General, anorm float64, work []float64, iwork []int) float64 {
-	return lapack64.Dgecon(norm, a.Cols, a.Data, a.Stride, anorm, work, iwork)
+	return lapack64.Dgecon(norm, a.Cols, a.Data, max(1, a.Stride), anorm, work, iwork)
 }
 
 // Gels finds a minimum-norm solution based on the matrices A and B using the
@@ -111,7 +118,7 @@ func Gecon(norm lapack.MatrixNorm, a blas64.General, anorm float64, work []float
 // In the special case that lwork == -1, work[0] will be set to the optimal working
 // length.
 func Gels(trans blas.Transpose, a blas64.General, b blas64.General, work []float64, lwork int) bool {
-	return lapack64.Dgels(trans, a.Rows, a.Cols, b.Cols, a.Data, a.Stride, b.Data, b.Stride, work, lwork)
+	return lapack64.Dgels(trans, a.Rows, a.Cols, b.Cols, a.Data, max(1, a.Stride), b.Data, max(1, b.Stride), work, lwork)
 }
 
 // Geqrf computes the QR factorization of the m×n matrix A using a blocked
@@ -137,7 +144,7 @@ func Gels(trans blas.Transpose, a blas64.General, b blas64.General, work []float
 // by the temporary space available. If lwork == -1, instead of performing Geqrf,
 // the optimal work length will be stored into work[0].
 func Geqrf(a blas64.General, tau, work []float64, lwork int) {
-	lapack64.Dgeqrf(a.Rows, a.Cols, a.Data, a.Stride, tau, work, lwork)
+	lapack64.Dgeqrf(a.Rows, a.Cols, a.Data, max(1, a.Stride), tau, work, lwork)
 }
 
 // Gelqf computes the LQ factorization of the m×n matrix A using a blocked
@@ -157,7 +164,7 @@ func Geqrf(a blas64.General, tau, work []float64, lwork int) {
 // by the temporary space available. If lwork == -1, instead of performing Gelqf,
 // the optimal work length will be stored into work[0].
 func Gelqf(a blas64.General, tau, work []float64, lwork int) {
-	lapack64.Dgelqf(a.Rows, a.Cols, a.Data, a.Stride, tau, work, lwork)
+	lapack64.Dgelqf(a.Rows, a.Cols, a.Data, max(1, a.Stride), tau, work, lwork)
 }
 
 // Gesvd computes the singular value decomposition of the input matrix A.
@@ -203,7 +210,7 @@ func Gelqf(a blas64.General, tau, work []float64, lwork int) {
 //
 // Gesvd returns whether the decomposition successfully completed.
 func Gesvd(jobU, jobVT lapack.SVDJob, a, u, vt blas64.General, s, work []float64, lwork int) (ok bool) {
-	return lapack64.Dgesvd(jobU, jobVT, a.Rows, a.Cols, a.Data, a.Stride, s, u.Data, u.Stride, vt.Data, vt.Stride, work, lwork)
+	return lapack64.Dgesvd(jobU, jobVT, a.Rows, a.Cols, a.Data, max(1, a.Stride), s, u.Data, max(1, u.Stride), vt.Data, max(1, vt.Stride), work, lwork)
 }
 
 // Getrf computes the LU decomposition of the m×n matrix A.
@@ -224,7 +231,7 @@ func Gesvd(jobU, jobVT lapack.SVDJob, a, u, vt blas64.General, s, work []float64
 // will occur if the false is returned and the result is used to solve a
 // system of equations.
 func Getrf(a blas64.General, ipiv []int) bool {
-	return lapack64.Dgetrf(a.Rows, a.Cols, a.Data, a.Stride, ipiv)
+	return lapack64.Dgetrf(a.Rows, a.Cols, a.Data, max(1, a.Stride), ipiv)
 }
 
 // Getri computes the inverse of the matrix A using the LU factorization computed
@@ -240,7 +247,7 @@ func Getrf(a blas64.General, ipiv []int) bool {
 // by the temporary space available. If lwork == -1, instead of performing Getri,
 // the optimal work length will be stored into work[0].
 func Getri(a blas64.General, ipiv []int, work []float64, lwork int) (ok bool) {
-	return lapack64.Dgetri(a.Cols, a.Data, a.Stride, ipiv, work, lwork)
+	return lapack64.Dgetri(a.Cols, a.Data, max(1, a.Stride), ipiv, work, lwork)
 }
 
 // Getrs solves a system of equations using an LU factorization.
@@ -255,7 +262,7 @@ func Getri(a blas64.General, ipiv []int, work []float64, lwork int) (ok bool) {
 // a and ipiv contain the LU factorization of A and the permutation indices as
 // computed by Getrf. ipiv is zero-indexed.
 func Getrs(trans blas.Transpose, a blas64.General, b blas64.General, ipiv []int) {
-	lapack64.Dgetrs(trans, a.Cols, b.Cols, a.Data, a.Stride, ipiv, b.Data, b.Stride)
+	lapack64.Dgetrs(trans, a.Cols, b.Cols, a.Data, max(1, a.Stride), ipiv, b.Data, max(1, b.Stride))
 }
 
 // Ggsvd3 computes the generalized singular value decomposition (GSVD)
@@ -355,7 +362,7 @@ func Getrs(trans blas.Transpose, a blas64.General, b blas64.General, ipiv []int)
 // lwork is -1, work[0] holds the optimal lwork on return, but Ggsvd3 does
 // not perform the GSVD.
 func Ggsvd3(jobU, jobV, jobQ lapack.GSVDJob, a, b blas64.General, alpha, beta []float64, u, v, q blas64.General, work []float64, lwork int, iwork []int) (k, l int, ok bool) {
-	return lapack64.Dggsvd3(jobU, jobV, jobQ, a.Rows, a.Cols, b.Rows, a.Data, a.Stride, b.Data, b.Stride, alpha, beta, u.Data, u.Stride, v.Data, v.Stride, q.Data, q.Stride, work, lwork, iwork)
+	return lapack64.Dggsvd3(jobU, jobV, jobQ, a.Rows, a.Cols, b.Rows, a.Data, max(1, a.Stride), b.Data, max(1, b.Stride), alpha, beta, u.Data, max(1, u.Stride), v.Data, max(1, v.Stride), q.Data, max(1, q.Stride), work, lwork, iwork)
 }
 
 // Lange computes the matrix norm of the general m×n matrix A. The input norm
@@ -367,7 +374,7 @@ func Ggsvd3(jobU, jobV, jobQ lapack.GSVDJob, a, b blas64.General, alpha, beta []
 // If norm == lapack.MaxColumnSum, work must be of length n, and this function will panic otherwise.
 // There are no restrictions on work for the other matrix norms.
 func Lange(norm lapack.MatrixNorm, a blas64.General, work []float64) float64 {
-	return lapack64.Dlange(norm, a.Rows, a.Cols, a.Data, a.Stride, work)
+	return lapack64.Dlange(norm, a.Rows, a.Cols, a.Data, max(1, a.Stride), work)
 }
 
 // Lansy computes the specified norm of an n×n symmetric matrix. If
@@ -375,14 +382,14 @@ func Lange(norm lapack.MatrixNorm, a blas64.General, work []float64) float64 {
 // at least n and this function will panic otherwise.
 // There are no restrictions on work for the other matrix norms.
 func Lansy(norm lapack.MatrixNorm, a blas64.Symmetric, work []float64) float64 {
-	return lapack64.Dlansy(norm, a.Uplo, a.N, a.Data, a.Stride, work)
+	return lapack64.Dlansy(norm, a.Uplo, a.N, a.Data, max(1, a.Stride), work)
 }
 
 // Lantr computes the specified norm of an m×n trapezoidal matrix A. If
 // norm == lapack.MaxColumnSum work must have length at least n and this function
 // will panic otherwise. There are no restrictions on work for the other matrix norms.
 func Lantr(norm lapack.MatrixNorm, a blas64.Triangular, work []float64) float64 {
-	return lapack64.Dlantr(norm, a.Uplo, a.Diag, a.N, a.N, a.Data, a.Stride, work)
+	return lapack64.Dlantr(norm, a.Uplo, a.Diag, a.N, a.N, a.Data, max(1, a.Stride), work)
 }
 
 // Lapmt rearranges the columns of the m×n matrix X as specified by the
@@ -398,7 +405,7 @@ func Lantr(norm lapack.MatrixNorm, a blas64.Triangular, work []float64) float64
 //
 // k must have length n, otherwise Lapmt will panic. k is zero-indexed.
 func Lapmt(forward bool, x blas64.General, k []int) {
-	lapack64.Dlapmt(forward, x.Rows, x.Cols, x.Data, x.Stride, k)
+	lapack64.Dlapmt(forward, x.Rows, x.Cols, x.Data, max(1, x.Stride), k)
 }
 
 // Ormlq multiplies the matrix C by the othogonal matrix Q defined by
@@ -420,7 +427,7 @@ func Lapmt(forward bool, x blas64.General, k []int) {
 // Tau contains the Householder scales and must have length at least k, and
 // this function will panic otherwise.
 func Ormlq(side blas.Side, trans blas.Transpose, a blas64.General, tau []float64, c blas64.General, work []float64, lwork int) {
-	lapack64.Dormlq(side, trans, c.Rows, c.Cols, a.Rows, a.Data, a.Stride, tau, c.Data, c.Stride, work, lwork)
+	lapack64.Dormlq(side, trans, c.Rows, c.Cols, a.Rows, a.Data, max(1, a.Stride), tau, c.Data, max(1, c.Stride), work, lwork)
 }
 
 // Ormqr multiplies an m×n matrix C by an orthogonal matrix Q as
@@ -451,7 +458,7 @@ func Ormlq(side blas.Side, trans blas.Transpose, a blas64.General, tau []float64
 // If lwork is -1, instead of performing Ormqr, the optimal workspace size will
 // be stored into work[0].
 func Ormqr(side blas.Side, trans blas.Transpose, a blas64.General, tau []float64, c blas64.General, work []float64, lwork int) {
-	lapack64.Dormqr(side, trans, c.Rows, c.Cols, a.Cols, a.Data, a.Stride, tau, c.Data, c.Stride, work, lwork)
+	lapack64.Dormqr(side, trans, c.Rows, c.Cols, a.Cols, a.Data, max(1, a.Stride), tau, c.Data, max(1, c.Stride), work, lwork)
 }
 
 // Pocon estimates the reciprocal of the condition number of a positive-definite
@@ -464,7 +471,7 @@ func Ormqr(side blas.Side, trans blas.Transpose, a blas64.General, tau []float64
 //
 // iwork is a temporary data slice of length at least n and Pocon will panic otherwise.
 func Pocon(a blas64.Symmetric, anorm float64, work []float64, iwork []int) float64 {
-	return lapack64.Dpocon(a.Uplo, a.N, a.Data, a.Stride, anorm, work, iwork)
+	return lapack64.Dpocon(a.Uplo, a.N, a.Data, max(1, a.Stride), anorm, work, iwork)
 }
 
 // Syev computes all eigenvalues and, optionally, the eigenvectors of a real
@@ -483,7 +490,7 @@ func Pocon(a blas64.Symmetric, anorm float64, work []float64, iwork []int) float
 // limited by the usable length. If lwork == -1, instead of computing Syev the
 // optimal work length is stored into work[0].
 func Syev(jobz lapack.EVJob, a blas64.Symmetric, w, work []float64, lwork int) (ok bool) {
-	return lapack64.Dsyev(jobz, a.Uplo, a.N, a.Data, a.Stride, w, work, lwork)
+	return lapack64.Dsyev(jobz, a.Uplo, a.N, a.Data, max(1, a.Stride), w, work, lwork)
 }
 
 // Trcon estimates the reciprocal of the condition number of a triangular matrix A.
@@ -493,7 +500,7 @@ func Syev(jobz lapack.EVJob, a blas64.Symmetric, w, work []float64, lwork int) (
 //
 // iwork is a temporary data slice of length at least n and Trcon will panic otherwise.
 func Trcon(norm lapack.MatrixNorm, a blas64.Triangular, work []float64, iwork []int) float64 {
-	return lapack64.Dtrcon(norm, a.Uplo, a.Diag, a.N, a.Data, a.Stride, work, iwork)
+	return lapack64.Dtrcon(norm, a.Uplo, a.Diag, a.N, a.Data, max(1, a.Stride), work, iwork)
 }
 
 // Trtri computes the inverse of a triangular matrix, storing the result in place
@@ -502,13 +509,13 @@ func Trcon(norm lapack.MatrixNorm, a blas64.Triangular, work []float64, iwork []
 // Trtri will not perform the inversion if the matrix is singular, and returns
 // a boolean indicating whether the inversion was successful.
 func Trtri(a blas64.Triangular) (ok bool) {
-	return lapack64.Dtrtri(a.Uplo, a.Diag, a.N, a.Data, a.Stride)
+	return lapack64.Dtrtri(a.Uplo, a.Diag, a.N, a.Data, max(1, a.Stride))
 }
 
 // Trtrs solves a triangular system of the form A * X = B or A^T * X = B. Trtrs
 // returns whether the solve completed successfully. If A is singular, no solve is performed.
 func Trtrs(trans blas.Transpose, a blas64.Triangular, b blas64.General) (ok bool) {
-	return lapack64.Dtrtrs(a.Uplo, trans, a.Diag, a.N, b.Cols, a.Data, a.Stride, b.Data, b.Stride)
+	return lapack64.Dtrtrs(a.Uplo, trans, a.Diag, a.N, b.Cols, a.Data, max(1, a.Stride), b.Data, max(1, b.Stride))
 }
 
 // Geev computes the eigenvalues and, optionally, the left and/or right
@@ -570,5 +577,5 @@ func Geev(jobvl lapack.LeftEVJob, jobvr lapack.RightEVJob, a blas64.General, wr,
 	if jobvr == lapack.RightEVCompute && (vr.Rows != n || vr.Cols != n) {
 		panic("lapack64: bad size of VR")
 	}
-	return lapack64.Dgeev(jobvl, jobvr, n, a.Data, a.Stride, wr, wi, vl.Data, vl.Stride, vr.Data, vr.Stride, work, lwork)
+	return lapack64.Dgeev(jobvl, jobvr, n, a.Data, max(1, a.Stride), wr, wi, vl.Data, max(1, vl.Stride), vr.Data, max(1, vr.Stride), work, lwork)
 }
diff --git a/mat/eigen.go b/mat/eigen.go
index 03869e6a..ee971e4a 100644
--- a/mat/eigen.go
+++ b/mat/eigen.go
@@ -187,15 +187,10 @@ func (e *Eigen) Factorize(a Matrix, kind EigenKind) (ok bool) {
 	if left {
 		vl = *NewDense(r, r, nil)
 		jobvl = lapack.LeftEVCompute
-	} else {
-		vl.mat.Stride = 1
 	}
-
 	if right {
 		vr = *NewDense(c, c, nil)
 		jobvr = lapack.RightEVCompute
-	} else {
-		vr.mat.Stride = 1
 	}
 
 	wr := getFloats(c, false)
diff --git a/mat/svd.go b/mat/svd.go
index 17c96588..2f55c411 100644
--- a/mat/svd.go
+++ b/mat/svd.go
@@ -99,7 +99,6 @@ func (svd *SVD) Factorize(a Matrix, kind SVDKind) (ok bool) {
 			Data:   use(svd.u.Data, m*min(m, n)),
 		}
 	default:
-		svd.u.Stride = 1
 		jobU = lapack.SVDNone
 	}
 	switch {
@@ -120,7 +119,6 @@ func (svd *SVD) Factorize(a Matrix, kind SVDKind) (ok bool) {
 		}
 		jobVT = lapack.SVDStore
 	default:
-		svd.vt.Stride = 1
 		jobVT = lapack.SVDNone
 	}