nalgebra::base

Type Alias SquareMatrix

Source 
pub type SquareMatrix<T, D, S> = Matrix<T, D, D, S>;
Expand description

A square matrix.

Aliased Type§

struct SquareMatrix<T, D, S> {
    pub data: S,
    /* private fields */
}

Fields§

§data: S

The data storage that contains all the matrix components. Disappointed?

Well, if you came here to see how you can access the matrix components, you may be in luck: you can access the individual components of all vectors with compile-time dimensions <= 6 using field notation like this: vec.x, vec.y, vec.z, vec.w, vec.a, vec.b. Reference and assignation work too:

let mut vec = Vector3::new(1.0, 2.0, 3.0);
vec.x = 10.0;
vec.y += 30.0;
assert_eq!(vec.x, 10.0);
assert_eq!(vec.y + 100.0, 132.0);

Similarly, for matrices with compile-time dimensions <= 6, you can use field notation like this: mat.m11, mat.m42, etc. The first digit identifies the row to address and the second digit identifies the column to address. So mat.m13 identifies the component at the first row and third column (note that the count of rows and columns start at 1 instead of 0 here. This is so we match the mathematical notation).

For all matrices and vectors, independently from their size, individual components can be accessed and modified using indexing: vec[20], mat[(20, 19)]. Here the indexing starts at 0 as you would expect.

Implementations§

Source§

impl<T, D1: Dim, S: StorageMut<T, D1, D1>> SquareMatrix<T, D1, S>
where T: Scalar + Zero + One + ClosedAddAssign + ClosedMulAssign,

Source

pub fn quadform_tr_with_workspace<D2, S2, R3, C3, S3, D4, S4>( &mut self, work: &mut Vector<T, D2, S2>, alpha: T, lhs: &Matrix<T, R3, C3, S3>, mid: &SquareMatrix<T, D4, S4>, beta: T, )
where D2: Dim, R3: Dim, C3: Dim, D4: Dim, S2: StorageMut<T, D2>, S3: Storage<T, R3, C3>, S4: Storage<T, D4, D4>, ShapeConstraint: DimEq<D1, D2> + DimEq<D1, R3> + DimEq<D2, R3> + DimEq<C3, D4>,

Computes the quadratic form self = alpha * lhs * mid * lhs.transpose() + beta * self.

This uses the provided workspace work to avoid allocations for intermediate results.

§Example
// Note that all those would also work with statically-sized matrices.
// We use DMatrix/DVector since that's the only case where pre-allocating the
// workspace is actually useful (assuming the same workspace is re-used for
// several computations) because it avoids repeated dynamic allocations.
let mut mat = DMatrix::identity(2, 2);
let lhs = DMatrix::from_row_slice(2, 3, &[1.0, 2.0, 3.0,
                                          4.0, 5.0, 6.0]);
let mid = DMatrix::from_row_slice(3, 3, &[0.1, 0.2, 0.3,
                                          0.5, 0.6, 0.7,
                                          0.9, 1.0, 1.1]);
// The random shows that values on the workspace do not
// matter as they will be overwritten.
let mut workspace = DVector::new_random(2);
let expected = &lhs * &mid * lhs.transpose() * 10.0 + &mat * 5.0;

mat.quadform_tr_with_workspace(&mut workspace, 10.0, &lhs, &mid, 5.0);
assert_relative_eq!(mat, expected);
Source

pub fn quadform_tr<R3, C3, S3, D4, S4>( &mut self, alpha: T, lhs: &Matrix<T, R3, C3, S3>, mid: &SquareMatrix<T, D4, S4>, beta: T, )
where R3: Dim, C3: Dim, D4: Dim, S3: Storage<T, R3, C3>, S4: Storage<T, D4, D4>, ShapeConstraint: DimEq<D1, D1> + DimEq<D1, R3> + DimEq<C3, D4>, DefaultAllocator: Allocator<D1>,

Computes the quadratic form self = alpha * lhs * mid * lhs.transpose() + beta * self.

This allocates a workspace vector of dimension D1 for intermediate results. If D1 is a type-level integer, then the allocation is performed on the stack. Use .quadform_tr_with_workspace(...) instead to avoid allocations.

§Example
let mut mat = Matrix2::identity();
let lhs = Matrix2x3::new(1.0, 2.0, 3.0,
                         4.0, 5.0, 6.0);
let mid = Matrix3::new(0.1, 0.2, 0.3,
                       0.5, 0.6, 0.7,
                       0.9, 1.0, 1.1);
let expected = lhs * mid * lhs.transpose() * 10.0 + mat * 5.0;

mat.quadform_tr(10.0, &lhs, &mid, 5.0);
assert_relative_eq!(mat, expected);
Source

pub fn quadform_with_workspace<D2, S2, D3, S3, R4, C4, S4>( &mut self, work: &mut Vector<T, D2, S2>, alpha: T, mid: &SquareMatrix<T, D3, S3>, rhs: &Matrix<T, R4, C4, S4>, beta: T, )
where D2: Dim, D3: Dim, R4: Dim, C4: Dim, S2: StorageMut<T, D2>, S3: Storage<T, D3, D3>, S4: Storage<T, R4, C4>, ShapeConstraint: DimEq<D3, R4> + DimEq<D1, C4> + DimEq<D2, D3> + AreMultipliable<C4, R4, D2, U1>,

Computes the quadratic form self = alpha * rhs.transpose() * mid * rhs + beta * self.

This uses the provided workspace work to avoid allocations for intermediate results.

§Example
// Note that all those would also work with statically-sized matrices.
// We use DMatrix/DVector since that's the only case where pre-allocating the
// workspace is actually useful (assuming the same workspace is re-used for
// several computations) because it avoids repeated dynamic allocations.
let mut mat = DMatrix::identity(2, 2);
let rhs = DMatrix::from_row_slice(3, 2, &[1.0, 2.0,
                                          3.0, 4.0,
                                          5.0, 6.0]);
let mid = DMatrix::from_row_slice(3, 3, &[0.1, 0.2, 0.3,
                                          0.5, 0.6, 0.7,
                                          0.9, 1.0, 1.1]);
// The random shows that values on the workspace do not
// matter as they will be overwritten.
let mut workspace = DVector::new_random(3);
let expected = rhs.transpose() * &mid * &rhs * 10.0 + &mat * 5.0;

mat.quadform_with_workspace(&mut workspace, 10.0, &mid, &rhs, 5.0);
assert_relative_eq!(mat, expected);
Source

pub fn quadform<D2, S2, R3, C3, S3>( &mut self, alpha: T, mid: &SquareMatrix<T, D2, S2>, rhs: &Matrix<T, R3, C3, S3>, beta: T, )
where D2: Dim, R3: Dim, C3: Dim, S2: Storage<T, D2, D2>, S3: Storage<T, R3, C3>, ShapeConstraint: DimEq<D2, R3> + DimEq<D1, C3> + AreMultipliable<C3, R3, D2, U1>, DefaultAllocator: Allocator<D2>,

Computes the quadratic form self = alpha * rhs.transpose() * mid * rhs + beta * self.

This allocates a workspace vector of dimension D2 for intermediate results. If D2 is a type-level integer, then the allocation is performed on the stack. Use .quadform_with_workspace(...) instead to avoid allocations.

§Example
let mut mat = Matrix2::identity();
let rhs = Matrix3x2::new(1.0, 2.0,
                         3.0, 4.0,
                         5.0, 6.0);
let mid = Matrix3::new(0.1, 0.2, 0.3,
                       0.5, 0.6, 0.7,
                       0.9, 1.0, 1.1);
let expected = rhs.transpose() * mid * rhs * 10.0 + mat * 5.0;

mat.quadform(10.0, &mid, &rhs, 5.0);
assert_relative_eq!(mat, expected);
Source§

impl<T: Scalar + Zero + One + ClosedMulAssign + ClosedAddAssign, D: DimName, S: Storage<T, D, D>> SquareMatrix<T, D, S>

§Append/prepend translation and scaling

Source

pub fn append_scaling(&self, scaling: T) -> OMatrix<T, D, D>
where D: DimNameSub<U1>, DefaultAllocator: Allocator<D, D>,

Computes the transformation equal to self followed by an uniform scaling factor.

Source

pub fn prepend_scaling(&self, scaling: T) -> OMatrix<T, D, D>
where D: DimNameSub<U1>, DefaultAllocator: Allocator<D, D>,

Computes the transformation equal to an uniform scaling factor followed by self.

Source

pub fn append_nonuniform_scaling<SB>( &self, scaling: &Vector<T, DimNameDiff<D, U1>, SB>, ) -> OMatrix<T, D, D>
where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<D, D>,

Computes the transformation equal to self followed by a non-uniform scaling factor.

Source

pub fn prepend_nonuniform_scaling<SB>( &self, scaling: &Vector<T, DimNameDiff<D, U1>, SB>, ) -> OMatrix<T, D, D>
where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<D, D>,

Computes the transformation equal to a non-uniform scaling factor followed by self.

Source

pub fn append_translation<SB>( &self, shift: &Vector<T, DimNameDiff<D, U1>, SB>, ) -> OMatrix<T, D, D>
where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<D, D>,

Computes the transformation equal to self followed by a translation.

Source

pub fn prepend_translation<SB>( &self, shift: &Vector<T, DimNameDiff<D, U1>, SB>, ) -> OMatrix<T, D, D>
where D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<D, D> + Allocator<DimNameDiff<D, U1>>,

Computes the transformation equal to a translation followed by self.

Source

pub fn append_scaling_mut(&mut self, scaling: T)
where S: StorageMut<T, D, D>, D: DimNameSub<U1>,

Computes in-place the transformation equal to self followed by an uniform scaling factor.

Source

pub fn prepend_scaling_mut(&mut self, scaling: T)
where S: StorageMut<T, D, D>, D: DimNameSub<U1>,

Computes in-place the transformation equal to an uniform scaling factor followed by self.

Source

pub fn append_nonuniform_scaling_mut<SB>( &mut self, scaling: &Vector<T, DimNameDiff<D, U1>, SB>, )
where S: StorageMut<T, D, D>, D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Computes in-place the transformation equal to self followed by a non-uniform scaling factor.

Source

pub fn prepend_nonuniform_scaling_mut<SB>( &mut self, scaling: &Vector<T, DimNameDiff<D, U1>, SB>, )
where S: StorageMut<T, D, D>, D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Computes in-place the transformation equal to a non-uniform scaling factor followed by self.

Source

pub fn append_translation_mut<SB>( &mut self, shift: &Vector<T, DimNameDiff<D, U1>, SB>, )
where S: StorageMut<T, D, D>, D: DimNameSub<U1>, SB: Storage<T, DimNameDiff<D, U1>>,

Computes the transformation equal to self followed by a translation.

Source

pub fn prepend_translation_mut<SB>( &mut self, shift: &Vector<T, DimNameDiff<D, U1>, SB>, )
where D: DimNameSub<U1>, S: StorageMut<T, D, D>, SB: Storage<T, DimNameDiff<D, U1>>, DefaultAllocator: Allocator<DimNameDiff<D, U1>>,

Computes the transformation equal to a translation followed by self.

Source§

impl<T: RealField, D: DimNameSub<U1>, S: Storage<T, D, D>> SquareMatrix<T, D, S>
where DefaultAllocator: Allocator<D, D> + Allocator<DimNameDiff<D, U1>> + Allocator<DimNameDiff<D, U1>, DimNameDiff<D, U1>>,

§Transformation of vectors and points

Source

pub fn transform_vector( &self, v: &OVector<T, DimNameDiff<D, U1>>, ) -> OVector<T, DimNameDiff<D, U1>>

Transforms the given vector, assuming the matrix self uses homogeneous coordinates.

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impl<T: RealField, S: Storage<T, Const<3>, Const<3>>> SquareMatrix<T, Const<3>, S>

Source

pub fn transform_point(&self, pt: &Point<T, 2>) -> Point<T, 2>

Transforms the given point, assuming the matrix self uses homogeneous coordinates.

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impl<T: RealField, S: Storage<T, Const<4>, Const<4>>> SquareMatrix<T, Const<4>, S>

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pub fn transform_point(&self, pt: &Point<T, 3>) -> Point<T, 3>

Transforms the given point, assuming the matrix self uses homogeneous coordinates.

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impl<T: Scalar, D: Dim, S: RawStorage<T, D, D>> SquareMatrix<T, D, S>

Source

pub fn diagonal(&self) -> OVector<T, D>
where DefaultAllocator: Allocator<D>,

The diagonal of this matrix.

Source

pub fn map_diagonal<T2: Scalar>(&self, f: impl FnMut(T) -> T2) -> OVector<T2, D>
where DefaultAllocator: Allocator<D>,

Apply the given function to this matrix’s diagonal and returns it.

This is a more efficient version of self.diagonal().map(f) since this allocates only once.

Source

pub fn trace(&self) -> T
where T: Scalar + Zero + ClosedAddAssign,

Computes a trace of a square matrix, i.e., the sum of its diagonal elements.

Source§

impl<T: SimdComplexField, D: Dim, S: Storage<T, D, D>> SquareMatrix<T, D, S>

Source

pub fn symmetric_part(&self) -> OMatrix<T, D, D>
where DefaultAllocator: Allocator<D, D>,

The symmetric part of self, i.e., 0.5 * (self + self.transpose()).

Source

pub fn hermitian_part(&self) -> OMatrix<T, D, D>
where DefaultAllocator: Allocator<D, D>,

The hermitian part of self, i.e., 0.5 * (self + self.adjoint()).

Source§

impl<T: RealField, D: Dim, S: Storage<T, D, D>> SquareMatrix<T, D, S>
where DefaultAllocator: Allocator<D, D>,

Source

pub fn is_special_orthogonal(&self, eps: T) -> bool
where D: DimMin<D, Output = D>, DefaultAllocator: Allocator<D>,

Checks that this matrix is orthogonal and has a determinant equal to 1.

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pub fn is_invertible(&self) -> bool

Returns true if this matrix is invertible.

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impl<T: ComplexField, D: DimMin<D, Output = D>, S: Storage<T, D, D>> SquareMatrix<T, D, S>

Source

pub fn determinant(&self) -> T
where DefaultAllocator: Allocator<D, D> + Allocator<D>,

Computes the matrix determinant.

If the matrix has a dimension larger than 3, an LU decomposition is used.

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impl<T: ComplexField, D: Dim, S: Storage<T, D, D>> SquareMatrix<T, D, S>

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pub fn try_inverse(self) -> Option<OMatrix<T, D, D>>
where DefaultAllocator: Allocator<D, D>,

Attempts to invert this square matrix.

§Panics

Panics if self isn’t a square matrix.

Source§

impl<T: ComplexField, D: Dim, S: StorageMut<T, D, D>> SquareMatrix<T, D, S>

Source

pub fn try_inverse_mut(&mut self) -> bool
where DefaultAllocator: Allocator<D, D>,

Attempts to invert this square matrix in-place. Returns false and leaves self untouched if inversion fails.

§Panics

Panics if self isn’t a square matrix.

Source§

impl<T: ComplexField, D, S: Storage<T, D, D>> SquareMatrix<T, D, S>
where D: DimSub<U1> + Dim, DefaultAllocator: Allocator<D, DimDiff<D, U1>> + Allocator<DimDiff<D, U1>> + Allocator<D, D> + Allocator<D>,

Source

pub fn eigenvalues(&self) -> Option<OVector<T, D>>

Computes the eigenvalues of this matrix.

Source

pub fn complex_eigenvalues(&self) -> OVector<NumComplex<T>, D>
where T: RealField, DefaultAllocator: Allocator<D>,

Computes the eigenvalues of this matrix.

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impl<T: ComplexField, D: Dim, S: Storage<T, D, D>> SquareMatrix<T, D, S>

Source

pub fn solve_lower_triangular<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> Option<OMatrix<T, R2, C2>>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn solve_upper_triangular<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> Option<OMatrix<T, R2, C2>>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn solve_lower_triangular_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 and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn solve_lower_triangular_with_diag_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, diag: T, ) -> bool
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self . x = b where x is the unknown and only the lower-triangular part of self is considered not-zero. The diagonal is never read as it is assumed to be equal to diag. Returns false and does not modify its inputs if diag is zero.

Source

pub fn solve_upper_triangular_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 and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_lower_triangular<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> Option<OMatrix<T, R2, C2>>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.transpose() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_upper_triangular<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> Option<OMatrix<T, R2, C2>>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.transpose() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_lower_triangular_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.transpose() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_upper_triangular_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.transpose() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn ad_solve_lower_triangular<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> Option<OMatrix<T, R2, C2>>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.adjoint() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn ad_solve_upper_triangular<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> Option<OMatrix<T, R2, C2>>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.adjoint() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn ad_solve_lower_triangular_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.adjoint() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn ad_solve_upper_triangular_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.adjoint() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source§

impl<T: SimdComplexField, D: Dim, S: Storage<T, D, D>> SquareMatrix<T, D, S>

Source

pub fn solve_lower_triangular_unchecked<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> OMatrix<T, R2, C2>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn solve_upper_triangular_unchecked<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> OMatrix<T, R2, C2>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn solve_lower_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn solve_lower_triangular_with_diag_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, diag: T, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self . x = b where x is the unknown and only the lower-triangular part of self is considered not-zero. The diagonal is never read as it is assumed to be equal to diag. Returns false and does not modify its inputs if diag is zero.

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pub fn solve_upper_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_lower_triangular_unchecked<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> OMatrix<T, R2, C2>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.transpose() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_upper_triangular_unchecked<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> OMatrix<T, R2, C2>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.transpose() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_lower_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self.transpose() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn tr_solve_upper_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self.transpose() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn ad_solve_lower_triangular_unchecked<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> OMatrix<T, R2, C2>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.adjoint() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

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pub fn ad_solve_upper_triangular_unchecked<R2: Dim, C2: Dim, S2>( &self, b: &Matrix<T, R2, C2, S2>, ) -> OMatrix<T, R2, C2>
where S2: Storage<T, R2, C2>, DefaultAllocator: Allocator<R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Computes the solution of the linear system self.adjoint() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

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pub fn ad_solve_lower_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self.adjoint() . x = b where x is the unknown and only the lower-triangular part of self (including the diagonal) is considered not-zero.

Source

pub fn ad_solve_upper_triangular_unchecked_mut<R2: Dim, C2: Dim, S2>( &self, b: &mut Matrix<T, R2, C2, S2>, )
where S2: StorageMut<T, R2, C2>, ShapeConstraint: SameNumberOfRows<R2, D>,

Solves the linear system self.adjoint() . x = b where x is the unknown and only the upper-triangular part of self (including the diagonal) is considered not-zero.

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impl<T: ComplexField, D: DimSub<U1>, S: Storage<T, D, D>> SquareMatrix<T, D, S>
where DefaultAllocator: Allocator<D, D> + Allocator<DimDiff<D, U1>> + Allocator<D>,

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pub fn symmetric_eigenvalues(&self) -> OVector<T::RealField, D>

Computes the eigenvalues of this symmetric matrix.

Only the lower-triangular part of the matrix is read.