Changelog for lapack-0.4
Change log for the lapack
package
0.4
-
Unified
Matrix
type that provides the same type parameters across all special types. This reduces the use of type functions and improves type inference. -
Unified
transpose
andadjoint
functions enabled by the newMatrix
type. -
Unpacked
format: We now support data type and according functions for unpacked triangular, symmetric and Hermitian matrices. Enables declaration e.g. of Hessenberg matrices. -
There are now two types of square matrices:
-
Square
: height and width shapes match exactly -
LiberalSquare
: only the sizes of height and width match
-
-
Square.eigensystem
: Use liberal square as transformation matrix, such that the eigenvalue array hasShapeInt
shape. The dimension of the input square matrix does not make sense as shape for the eigenvalue array. -
Square.fromGeneral
->fromFull
-
Orthogonal.affineKernelFromSpan
->affineFiberFromFrame
,Orthogonal.affineSpanFromKernel
->affineFrameFromFiber
0.3.2
-
Orthogonal
:project
,affineKernelFromSpan
,affineSpanFromKernel
,leastSquaresConstraint
,gaussMarkovLinearModel
-
Symmetric.fromHermitian
,Hermitian.fromSymmetric
-
instance Monoid Matrix
, especiallymempty
for matrices with static shapes. -
Extent.Dimensions
: turn from type family to data family -
Start using
doctest-extract
for simple tests
0.3.1
Matrix.Symmetric
: You can now import many functions for symmetric matrices from this module. This is more natural than importing them fromTriangular
.
0.3
-
Matrix data family
-
Matrix
:ZeroInt
->ShapeInt
,zeroInt
->shapeInt
-
Hermitian
,BandedHermitian
:covariance
->gramian
-
Square.eigensystem
: Return left eigenvectors as rows of the last matrix. This is adjoint with respect to the definition inlapack-0.2
but it is consistent with the other eigenvalue and singular value decompositions.