Struct nalgebra::linalg::QR [−][src]
pub struct QR<T: ComplexField, R: DimMin<C>, C: Dim> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>, { /* fields omitted */ }
Expand description
The QR decomposition of a general matrix.
Implementations
impl<T: ComplexField, R: DimMin<C>, C: Dim> QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, R> + Allocator<T, DimMinimum<R, C>>,
impl<T: ComplexField, R: DimMin<C>, C: Dim> QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, R> + Allocator<T, DimMinimum<R, C>>,
Computes the QR decomposition using householder reflections.
pub fn r(&self) -> OMatrix<T, DimMinimum<R, C>, C> where
DefaultAllocator: Allocator<T, DimMinimum<R, C>, C>,
pub fn r(&self) -> OMatrix<T, DimMinimum<R, C>, C> where
DefaultAllocator: Allocator<T, DimMinimum<R, C>, C>,
Retrieves the upper trapezoidal submatrix R
of this decomposition.
pub fn unpack_r(self) -> OMatrix<T, DimMinimum<R, C>, C> where
DefaultAllocator: Reallocator<T, R, C, DimMinimum<R, C>, C>,
pub fn unpack_r(self) -> OMatrix<T, DimMinimum<R, C>, C> where
DefaultAllocator: Reallocator<T, R, C, DimMinimum<R, C>, C>,
Retrieves the upper trapezoidal submatrix R
of this decomposition.
This is usually faster than r
but consumes self
.
pub fn q(&self) -> OMatrix<T, R, DimMinimum<R, C>> where
DefaultAllocator: Allocator<T, R, DimMinimum<R, C>>,
pub fn q(&self) -> OMatrix<T, R, DimMinimum<R, C>> where
DefaultAllocator: Allocator<T, R, DimMinimum<R, C>>,
Computes the orthogonal matrix Q
of this decomposition.
pub fn unpack(
self
) -> (OMatrix<T, R, DimMinimum<R, C>>, OMatrix<T, DimMinimum<R, C>, C>) where
DimMinimum<R, C>: DimMin<C, Output = DimMinimum<R, C>>,
DefaultAllocator: Allocator<T, R, DimMinimum<R, C>> + Reallocator<T, R, C, DimMinimum<R, C>, C>,
pub fn unpack(
self
) -> (OMatrix<T, R, DimMinimum<R, C>>, OMatrix<T, DimMinimum<R, C>, C>) where
DimMinimum<R, C>: DimMin<C, Output = DimMinimum<R, C>>,
DefaultAllocator: Allocator<T, R, DimMinimum<R, C>> + Reallocator<T, R, C, DimMinimum<R, C>, C>,
Unpacks this decomposition into its two matrix factors.
impl<T: ComplexField, D: DimMin<D, Output = D>> QR<T, D, D> where
DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
impl<T: ComplexField, D: DimMin<D, Output = D>> QR<T, D, D> where
DefaultAllocator: Allocator<T, D, D> + Allocator<T, D>,
pub fn solve<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<T, R2, C2, S2>
) -> Option<OMatrix<T, R2, C2>> where
S2: Storage<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
DefaultAllocator: Allocator<T, R2, C2>,
pub fn solve<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<T, R2, C2, S2>
) -> Option<OMatrix<T, R2, C2>> where
S2: Storage<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
DefaultAllocator: Allocator<T, R2, C2>,
Solves the linear system self * x = b
, where x
is the unknown to be determined.
Returns None
if self
is not invertible.
pub fn solve_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<T, R2, C2, S2>
) -> bool where
S2: StorageMut<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
pub fn solve_mut<R2: Dim, C2: Dim, S2>(
&self,
b: &mut Matrix<T, R2, C2, S2>
) -> bool where
S2: StorageMut<T, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
Solves the linear system self * x = b
, where x
is the unknown to be determined.
If the decomposed matrix is not invertible, this returns false
and its input b
is
overwritten with garbage.
Computes the inverse of the decomposed matrix.
Returns None
if the decomposed matrix is not invertible.
Indicates if the decomposed matrix is invertible.
Trait Implementations
impl<T: Clone + ComplexField, R: Clone + DimMin<C>, C: Clone + Dim> Clone for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
impl<T: Clone + ComplexField, R: Clone + DimMin<C>, C: Clone + Dim> Clone for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
impl<T: Debug + ComplexField, R: Debug + DimMin<C>, C: Debug + Dim> Debug for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
impl<T: Debug + ComplexField, R: Debug + DimMin<C>, C: Debug + Dim> Debug for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
impl<'de, T: ComplexField, R: DimMin<C>, C: Dim> Deserialize<'de> for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
OMatrix<T, R, C>: Deserialize<'de>,
OVector<T, DimMinimum<R, C>>: Deserialize<'de>,
impl<'de, T: ComplexField, R: DimMin<C>, C: Dim> Deserialize<'de> for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
OMatrix<T, R, C>: Deserialize<'de>,
OVector<T, DimMinimum<R, C>>: Deserialize<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
impl<T: ComplexField, R: DimMin<C>, C: Dim> Serialize for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
OMatrix<T, R, C>: Serialize,
OVector<T, DimMinimum<R, C>>: Serialize,
impl<T: ComplexField, R: DimMin<C>, C: Dim> Serialize for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
OMatrix<T, R, C>: Serialize,
OVector<T, DimMinimum<R, C>>: Serialize,
impl<T: ComplexField, R: DimMin<C>, C: Dim> Copy for QR<T, R, C> where
DefaultAllocator: Allocator<T, R, C> + Allocator<T, DimMinimum<R, C>>,
OMatrix<T, R, C>: Copy,
OVector<T, DimMinimum<R, C>>: Copy,
Auto Trait Implementations
impl<T, R, C> !RefUnwindSafe for QR<T, R, C>
impl<T, R, C> !UnwindSafe for QR<T, R, C>
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.