Struct nalgebra::linalg::Schur [−][src]
pub struct Schur<N: ComplexField, D: Dim> where
DefaultAllocator: Allocator<N, D, D>, { /* fields omitted */ }
Expand description
Schur decomposition of a square matrix.
If this is a real matrix, this will be a RealField Schur decomposition.
Implementations
Attempts to compute the Schur decomposition of a square matrix.
If only eigenvalues are needed, it is more efficient to call the matrix method
.eigenvalues() instead.
Arguments
eps− tolerance used to determine when a value converged to 0.max_niter− maximum total number of iterations performed by the algorithm. If this number of iteration is exceeded,Noneis returned. Ifniter == 0, then the algorithm continues indefinitely until convergence.
Retrieves the unitary matrix Q and the upper-quasitriangular matrix T such that the
decomposed matrix equals Q * T * Q.transpose().
Computes the real eigenvalues of the decomposed matrix.
Return None if some eigenvalues are complex.
pub fn complex_eigenvalues(&self) -> VectorN<NumComplex<N>, D> where
N: RealField,
DefaultAllocator: Allocator<NumComplex<N>, D>,
pub fn complex_eigenvalues(&self) -> VectorN<NumComplex<N>, D> where
N: RealField,
DefaultAllocator: Allocator<NumComplex<N>, D>,
Computes the complex eigenvalues of the decomposed matrix.
Trait Implementations
impl<N: Clone + ComplexField, D: Clone + Dim> Clone for Schur<N, D> where
DefaultAllocator: Allocator<N, D, D>,
impl<N: Clone + ComplexField, D: Clone + Dim> Clone for Schur<N, D> where
DefaultAllocator: Allocator<N, D, D>,
impl<N: Debug + ComplexField, D: Debug + Dim> Debug for Schur<N, D> where
DefaultAllocator: Allocator<N, D, D>,
impl<N: Debug + ComplexField, D: Debug + Dim> Debug for Schur<N, D> where
DefaultAllocator: Allocator<N, D, D>,
impl<N: ComplexField, D: Dim> Copy for Schur<N, D> where
DefaultAllocator: Allocator<N, D, D>,
MatrixN<N, D>: Copy,
Auto Trait Implementations
impl<N, D> !RefUnwindSafe for Schur<N, D>
impl<N, D> !UnwindSafe for Schur<N, D>
Blanket Implementations
Mutably borrows from an owned value. Read more
The inverse inclusion map: attempts to construct self from the equivalent element of its
superset. Read more
Checks if self is actually part of its subset T (and can be converted to it).
Use with care! Same as self.to_subset but without any property checks. Always succeeds.
The inclusion map: converts self to the equivalent element of its superset.