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 ```
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@ -28,13 +28,13 @@ use lapack;
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))
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)]
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#[derive(Clone, Debug)]
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pub struct Cholesky<N: Scalar, D: Dim>
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pub struct Cholesky<N: Scalar + Copy, D: Dim>
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where DefaultAllocator: Allocator<N, D, D>
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{
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l: MatrixN<N, D>,
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}
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impl<N: Scalar, D: Dim> Copy for Cholesky<N, D>
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impl<N: Scalar + Copy, D: Dim> Copy for Cholesky<N, D>
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where
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DefaultAllocator: Allocator<N, D, D>,
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MatrixN<N, D>: Copy,
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@ -175,7 +175,7 @@ where DefaultAllocator: Allocator<N, D, D>
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*/
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/// Trait implemented by floats (`f32`, `f64`) and complex floats (`Complex<f32>`, `Complex<f64>`)
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/// supported by the cholesky decomposition.
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pub trait CholeskyScalar: Scalar {
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pub trait CholeskyScalar: Scalar + Copy {
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#[allow(missing_docs)]
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fn xpotrf(uplo: u8, n: i32, a: &mut [Self], lda: i32, info: &mut i32);
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#[allow(missing_docs)]
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@ -33,7 +33,7 @@ use lapack;
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))
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)]
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#[derive(Clone, Debug)]
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pub struct Eigen<N: Scalar, D: Dim>
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pub struct Eigen<N: Scalar + Copy, D: Dim>
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where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
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{
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/// The eigenvalues of the decomposed matrix.
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@ -44,7 +44,7 @@ where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
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pub left_eigenvectors: Option<MatrixN<N, D>>,
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}
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impl<N: Scalar, D: Dim> Copy for Eigen<N, D>
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impl<N: Scalar + Copy, D: Dim> Copy for Eigen<N, D>
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where
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DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>,
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VectorN<N, D>: Copy,
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@ -311,7 +311,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>
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*/
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/// Trait implemented by scalar type for which Lapack function exist to compute the
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/// eigendecomposition.
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pub trait EigenScalar: Scalar {
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pub trait EigenScalar: Scalar + Copy {
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#[allow(missing_docs)]
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fn xgeev(
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jobvl: u8,
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@ -30,14 +30,14 @@ use lapack;
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))
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)]
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#[derive(Clone, Debug)]
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pub struct Hessenberg<N: Scalar, D: DimSub<U1>>
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pub struct Hessenberg<N: Scalar + Copy, D: DimSub<U1>>
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where DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>
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{
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h: MatrixN<N, D>,
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tau: VectorN<N, DimDiff<D, U1>>,
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}
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impl<N: Scalar, D: DimSub<U1>> Copy for Hessenberg<N, D>
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impl<N: Scalar + Copy, D: DimSub<U1>> Copy for Hessenberg<N, D>
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where
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DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>,
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MatrixN<N, D>: Copy,
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@ -137,7 +137,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, DimDiff<D, U1>>
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* Lapack functions dispatch.
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*
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*/
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pub trait HessenbergScalar: Scalar {
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pub trait HessenbergScalar: Scalar + Copy {
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fn xgehrd(
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n: i32,
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ilo: i32,
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@ -37,14 +37,14 @@ use lapack;
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))
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)]
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#[derive(Clone, Debug)]
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pub struct LU<N: Scalar, R: DimMin<C>, C: Dim>
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pub struct LU<N: Scalar + Copy, R: DimMin<C>, C: Dim>
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where DefaultAllocator: Allocator<i32, DimMinimum<R, C>> + Allocator<N, R, C>
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{
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lu: MatrixMN<N, R, C>,
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p: VectorN<i32, DimMinimum<R, C>>,
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}
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impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for LU<N, R, C>
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impl<N: Scalar + Copy, R: DimMin<C>, C: Dim> Copy for LU<N, R, C>
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where
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DefaultAllocator: Allocator<N, R, C> + Allocator<i32, DimMinimum<R, C>>,
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MatrixMN<N, R, C>: Copy,
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@ -306,7 +306,7 @@ where
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*
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*/
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/// Trait implemented by scalars for which Lapack implements the LU decomposition.
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pub trait LUScalar: Scalar {
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pub trait LUScalar: Scalar + Copy {
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#[allow(missing_docs)]
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fn xgetrf(m: i32, n: i32, a: &mut [Self], lda: i32, ipiv: &mut [i32], info: &mut i32);
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#[allow(missing_docs)]
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@ -33,14 +33,14 @@ use lapack;
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))
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)]
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#[derive(Clone, Debug)]
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pub struct QR<N: Scalar, R: DimMin<C>, C: Dim>
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pub struct QR<N: Scalar + Copy, R: DimMin<C>, C: Dim>
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where DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>>
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{
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qr: MatrixMN<N, R, C>,
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tau: VectorN<N, DimMinimum<R, C>>,
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}
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impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for QR<N, R, C>
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impl<N: Scalar + Copy, R: DimMin<C>, C: Dim> Copy for QR<N, R, C>
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where
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DefaultAllocator: Allocator<N, R, C> + Allocator<N, DimMinimum<R, C>>,
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MatrixMN<N, R, C>: Copy,
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@ -166,7 +166,7 @@ where DefaultAllocator: Allocator<N, R, C>
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*/
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/// Trait implemented by scalar types for which Lapack function exist to compute the
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/// QR decomposition.
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pub trait QRScalar: Scalar {
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pub trait QRScalar: Scalar + Copy {
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fn xgeqrf(
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m: i32,
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n: i32,
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@ -33,7 +33,7 @@ use lapack;
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))
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)]
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#[derive(Clone, Debug)]
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pub struct Schur<N: Scalar, D: Dim>
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pub struct Schur<N: Scalar + Copy, D: Dim>
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where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
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{
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re: VectorN<N, D>,
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@ -42,7 +42,7 @@ where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
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q: MatrixN<N, D>,
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}
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impl<N: Scalar, D: Dim> Copy for Schur<N, D>
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impl<N: Scalar + Copy, D: Dim> Copy for Schur<N, D>
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where
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DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
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MatrixN<N, D>: Copy,
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@ -162,7 +162,7 @@ where DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>
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*
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*/
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/// Trait implemented by scalars for which Lapack implements the RealField Schur decomposition.
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pub trait SchurScalar: Scalar {
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pub trait SchurScalar: Scalar + Copy {
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#[allow(missing_docs)]
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fn xgees(
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jobvs: u8,
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))
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)]
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#[derive(Clone, Debug)]
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pub struct SVD<N: Scalar, R: DimMin<C>, C: Dim>
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pub struct SVD<N: Scalar + Copy, R: DimMin<C>, C: Dim>
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where DefaultAllocator: Allocator<N, R, R> + Allocator<N, DimMinimum<R, C>> + Allocator<N, C, C>
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{
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/// The left-singular vectors `U` of this SVD.
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pub singular_values: VectorN<N, DimMinimum<R, C>>,
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}
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impl<N: Scalar, R: DimMin<C>, C: Dim> Copy for SVD<N, R, C>
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impl<N: Scalar + Copy, R: DimMin<C>, C: Dim> Copy for SVD<N, R, C>
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where
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DefaultAllocator: Allocator<N, C, C> + Allocator<N, R, R> + Allocator<N, DimMinimum<R, C>>,
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MatrixMN<N, R, R>: Copy,
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/// Trait implemented by floats (`f32`, `f64`) and complex floats (`Complex<f32>`, `Complex<f64>`)
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/// supported by the Singular Value Decompotition.
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pub trait SVDScalar<R: DimMin<C>, C: Dim>: Scalar
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pub trait SVDScalar<R: DimMin<C>, C: Dim>: Scalar + Copy
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where DefaultAllocator: Allocator<Self, R, R>
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+ Allocator<Self, R, C>
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+ Allocator<Self, DimMinimum<R, C>>
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))
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)]
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#[derive(Clone, Debug)]
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pub struct SymmetricEigen<N: Scalar, D: Dim>
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pub struct SymmetricEigen<N: Scalar + Copy, D: Dim>
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where DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>
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{
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/// The eigenvectors of the decomposed matrix.
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pub eigenvalues: VectorN<N, D>,
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}
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impl<N: Scalar, D: Dim> Copy for SymmetricEigen<N, D>
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impl<N: Scalar + Copy, D: Dim> Copy for SymmetricEigen<N, D>
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where
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DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
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MatrixN<N, D>: Copy,
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*/
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/// Trait implemented by scalars for which Lapack implements the eigendecomposition of symmetric
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/// real matrices.
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pub trait SymmetricEigenScalar: Scalar {
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pub trait SymmetricEigenScalar: Scalar + Copy {
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#[allow(missing_docs)]
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fn xsyev(
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jobz: u8,
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