Trait LeastSquaresSvdInPlace 

pub trait LeastSquaresSvdInPlace<D, E, I>
where
    D: Data<Elem = E>,
    E: Scalar + Lapack,
    I: Dimension,
{
    // Required method
    fn least_squares_in_place(
        &mut self,
        rhs: &mut ArrayBase<D, I>,
    ) -> Result<LeastSquaresResult<E, I>, LinalgError>;
}

Solve least squares for mutable references, overwriting the input fields in the process

Required Methods

fn least_squares_in_place( &mut self, rhs: &mut ArrayBase<D, I>, ) -> Result<LeastSquaresResult<E, I>, LinalgError>

Solve a least squares problem of the form Ax = rhs by calling A.least_squares(&mut rhs), overwriting both A and rhs. This uses the memory location of A and rhs, which avoids some extra memory allocations.

A and rhs must have the same layout, i.e. they must be both either row- or column-major format, otherwise a IncompatibleShape error is raised.

Implementors

impl<E, D1, D2> LeastSquaresSvdInPlace<D2, E, Dim<[usize; 1]>> for ArrayBase<D1, Dim<[usize; 2]>>

where E: Scalar + Lapack, D1: DataMut<Elem = E>, D2: DataMut<Elem = E>,

Solve least squares for mutable references and a vector as a right-hand side. Both values are overwritten in the call.

E is one of f32, f64, c32, c64. D1, D2 can be any valid representation for ArrayBase (over E).

impl<E, D1, D2> LeastSquaresSvdInPlace<D2, E, Dim<[usize; 2]>> for ArrayBase<D1, Dim<[usize; 2]>>

where E: Scalar + Lapack, D1: DataMut<Elem = E>, D2: DataMut<Elem = E>,

Solve least squares for mutable references and a matrix as a right-hand side. Both values are overwritten in the call.

E is one of f32, f64, c32, c64. D1, D2 can be any valid representation for ArrayBase (over E).