Add Scalar + Copy bounds in nalgebra-lapack.

```bash
export RELEVANT_SOURCEFILES="$(find nalgebra-lapack -name '*.rs')"
for f in $RELEVANT_SOURCEFILES; do sed -i 's/N\([0-9]\?\): *Scalar,/N\1: Scalar + Copy,/' $f; done
for f in $RELEVANT_SOURCEFILES; do sed -i 's/N\([0-9]\?\): *Scalar>/N\1: Scalar + Copy>/' $f; done
for f in $RELEVANT_SOURCEFILES; do sed -i 's/\([A-Z]*Scalar\): Scalar {/\1: Scalar + Copy {/' $f; done
for f in $RELEVANT_SOURCEFILES; do sed -i 's/SVDScalar<R: DimMin<C>, C: Dim>: Scalar/SVDScalar<R: DimMin<C>, C: Dim>: Scalar + Copy/' $f; done
```

nalgebra-lapack/src/cholesky.rs

@@ -28,13 +28,13 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct Cholesky<N: Scalar, D: Dim>
+pub struct Cholesky<N: Scalar + Copy, D: Dim>
 where DefaultAllocator: Allocator<N, D, D>
 {
     l: MatrixN<N, D>,
 }
 
-impl<N: Scalar, D: Dim> Copy for Cholesky<N, D>
+impl<N: Scalar + Copy, D: Dim> Copy for Cholesky<N, D>
 where
     DefaultAllocator: Allocator<N, D, D>,
     MatrixN<N, D>: Copy,
@@ -175,7 +175,7 @@ where DefaultAllocator: Allocator<N, D, D>
  */
 /// Trait implemented by floats (`f32`, `f64`) and complex floats (`Complex<f32>`, `Complex<f64>`)
 /// supported by the cholesky decomposition.
-pub trait CholeskyScalar: Scalar {
+pub trait CholeskyScalar: Scalar + Copy {
     #[allow(missing_docs)]
     fn xpotrf(uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32);
     #[allow(missing_docs)]

nalgebra-lapack/src/eigen.rs

@@ -33,7 +33,7 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct Eigen<N: Scalar, D: Dim>
+pub struct Eigen<N: Scalar + Copy, D: Dim>
 where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
 {
     /// The eigenvalues of the decomposed matrix.
@@ -44,7 +44,7 @@ where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
     pub left_eigenvectors: Option<MatrixN<N, D>>,
 }
 
-impl<N: Scalar, D: Dim> Copy for Eigen<N, D>
+impl<N: Scalar + Copy, D: Dim> Copy for Eigen<N, D>
 where
     DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>,
     VectorN<N, D>: Copy,
@@ -311,7 +311,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>
  */
 /// Trait implemented by scalar type for which Lapack function exist to compute the
 /// eigendecomposition.
-pub trait EigenScalar: Scalar {
+pub trait EigenScalar: Scalar + Copy {
     #[allow(missing_docs)]
     fn xgeev(
         jobvl: u8,

nalgebra-lapack/src/hessenberg.rs

@@ -30,14 +30,14 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct Hessenberg<N: Scalar, D: DimSub<U1>>
+pub struct Hessenberg<N: Scalar + Copy, D: DimSub<U1>>
 where DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>
 {
     h: MatrixN<N, D>,
     tau: VectorN<N, DimDiff<D, U1>>,
 }
 
-impl<N: Scalar, D: DimSub<U1>> Copy for Hessenberg<N, D>
+impl<N: Scalar + Copy, D: DimSub<U1>> Copy for Hessenberg<N, D>
 where
     DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>,
     MatrixN<N, D>: Copy,
@@ -137,7 +137,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>
  * Lapack functions dispatch.
  *
  */
-pub trait HessenbergScalar: Scalar {
+pub trait HessenbergScalar: Scalar + Copy {
     fn xgehrd(
         n: i32,
         ilo: i32,

nalgebra-lapack/src/lu.rs

@@ -37,14 +37,14 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct LU<N: Scalar, R: DimMin<C>, C: Dim>
+pub struct LU<N: Scalar + Copy, R: DimMin<C>, C: Dim>
 where DefaultAllocator: Allocator<i32, DimMinimum<R, C>> + Allocator<N, R, C>
 {
     lu: MatrixMN<N, R, C>,
     p: VectorN<i32, DimMinimum<R, C>>,
 }
 
-impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for LU<N, R, C>
+impl<N: Scalar + Copy, R: DimMin<C>, C: Dim> Copy for LU<N, R, C>
 where
     DefaultAllocator: Allocator<N, R, C> + Allocator<i32, DimMinimum<R, C>>,
     MatrixMN<N, R, C>: Copy,
@@ -306,7 +306,7 @@ where
  *
  */
 /// Trait implemented by scalars for which Lapack implements the LU decomposition.
-pub trait LUScalar: Scalar {
+pub trait LUScalar: Scalar + Copy {
     #[allow(missing_docs)]
     fn xgetrf(m: i32, n: i32, a: &mut [Self], lda: i32, ipiv: &mut [i32], info: &mut i32);
     #[allow(missing_docs)]

nalgebra-lapack/src/qr.rs

@@ -33,14 +33,14 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct QR<N: Scalar, R: DimMin<C>, C: Dim>
+pub struct QR<N: Scalar + Copy, R: DimMin<C>, C: Dim>
 where DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>>
 {
     qr: MatrixMN<N, R, C>,
     tau: VectorN<N, DimMinimum<R, C>>,
 }
 
-impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for QR<N, R, C>
+impl<N: Scalar + Copy, R: DimMin<C>, C: Dim> Copy for QR<N, R, C>
 where
     DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>>,
     MatrixMN<N, R, C>: Copy,
@@ -166,7 +166,7 @@ where DefaultAllocator: Allocator<N, R, C>
  */
 /// Trait implemented by scalar types for which Lapack function exist to compute the
 /// QR decomposition.
-pub trait QRScalar: Scalar {
+pub trait QRScalar: Scalar + Copy {
     fn xgeqrf(
         m: i32,
         n: i32,

nalgebra-lapack/src/schur.rs

@@ -33,7 +33,7 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct Schur<N: Scalar, D: Dim>
+pub struct Schur<N: Scalar + Copy, D: Dim>
 where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
 {
     re: VectorN<N, D>,
@@ -42,7 +42,7 @@ where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
     q: MatrixN<N, D>,
 }
 
-impl<N: Scalar, D: Dim> Copy for Schur<N, D>
+impl<N: Scalar + Copy, D: Dim> Copy for Schur<N, D>
 where
     DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
     MatrixN<N, D>: Copy,
@@ -162,7 +162,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>
  *
  */
 /// Trait implemented by scalars for which Lapack implements the RealField Schur decomposition.
-pub trait SchurScalar: Scalar {
+pub trait SchurScalar: Scalar + Copy {
     #[allow(missing_docs)]
     fn xgees(
         jobvs: u8,

nalgebra-lapack/src/svd.rs

@@ -36,7 +36,7 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct SVD<N: Scalar, R: DimMin<C>, C: Dim>
+pub struct SVD<N: Scalar + Copy, R: DimMin<C>, C: Dim>
 where DefaultAllocator: Allocator<N, R, R> + Allocator<N, DimMinimum<R, C>> + Allocator<N, C, C>
 {
     /// The left-singular vectors `U` of this SVD.
@@ -47,7 +47,7 @@ where DefaultAllocator: Allocator<N, R, R> + Allocator<N, DimMinimum<R, C>> + Al
     pub singular_values: VectorN<N, DimMinimum<R, C>>,
 }
 
-impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for SVD<N, R, C>
+impl<N: Scalar + Copy, R: DimMin<C>, C: Dim> Copy for SVD<N, R, C>
 where
     DefaultAllocator: Allocator<N, C, C> + Allocator<N, R, R> + Allocator<N, DimMinimum<R, C>>,
     MatrixMN<N, R, R>: Copy,
@@ -57,7 +57,7 @@ where
 
 /// Trait implemented by floats (`f32`, `f64`) and complex floats (`Complex<f32>`, `Complex<f64>`)
 /// supported by the Singular Value Decompotition.
-pub trait SVDScalar<R: DimMin<C>, C: Dim>: Scalar
+pub trait SVDScalar<R: DimMin<C>, C: Dim>: Scalar + Copy
 where DefaultAllocator: Allocator<Self, R, R>
         + Allocator<Self, R, C>
         + Allocator<Self, DimMinimum<R, C>>

nalgebra-lapack/src/symmetric_eigen.rs

@@ -35,7 +35,7 @@ use lapack;
     ))
 )]
 #[derive(Clone, Debug)]
-pub struct SymmetricEigen<N: Scalar, D: Dim>
+pub struct SymmetricEigen<N: Scalar + Copy, D: Dim>
 where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
 {
     /// The eigenvectors of the decomposed matrix.
@@ -45,7 +45,7 @@ where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
     pub eigenvalues: VectorN<N, D>,
 }
 
-impl<N: Scalar, D: Dim> Copy for SymmetricEigen<N, D>
+impl<N: Scalar + Copy, D: Dim> Copy for SymmetricEigen<N, D>
 where
     DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
     MatrixN<N, D>: Copy,
@@ -169,7 +169,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>
  */
 /// Trait implemented by scalars for which Lapack implements the eigendecomposition of symmetric
 /// real matrices.
-pub trait SymmetricEigenScalar: Scalar {
+pub trait SymmetricEigenScalar: Scalar + Copy {
     #[allow(missing_docs)]
     fn xsyev(
         jobz: u8,