pub struct LeastSquaresResult<E: Scalar, I: Dimension> {
pub singular_values: Array1<E::Real>,
pub solution: Array<E, I>,
pub rank: i32,
pub residual_sum_of_squares: Option<Array<E::Real, I::Smaller>>,
}
Expand description
Result of a LeastSquares computation
Takes two type parameters, E
, the element type of the matrix
(one of f32
, f64
, c32
or c64
) and I
, the dimension of
b in the equation Ax = b
(one of Ix1
or Ix2
). If I
is Ix1
,
the right-hand-side (RHS) is a n x 1
column vector and the solution
is a m x 1
column vector. If I
is Ix2
, the RHS is a n x k
matrix
(which can be seen as solving Ax = b
k times for different b) and
the solution is a m x k
matrix.
Fields§
§singular_values: Array1<E::Real>
The singular values of the matrix A in Ax = b
solution: Array<E, I>
The solution vector or matrix x
which is the best
solution to Ax = b
, i.e. minimizing the 2-norm ||b - Ax||
rank: i32
The rank of the matrix A in Ax = b
residual_sum_of_squares: Option<Array<E::Real, I::Smaller>>
If n < m and rank(A) == n, the sum of squares If b is a (m x 1) vector, this is a 0-dimensional array (single value) If b is a (m x k) matrix, this is a (k x 1) column vector