# LinearAlgebra

Namespace: Deedle.Math

Linear algebra on frame using MathNet.Numerics library.

### Static members

 Static member Description LinearAlgebra.cholesky(df) Signature: df:Frame<'R,'C> -> Cholesky Type parameters: 'R, 'C Cholesky decomposition LinearAlgebra.condition(df) Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C Matrix condition LinearAlgebra.conjugate(df) Signature: df:Frame<'R,'C> -> Matrix Type parameters: 'R, 'C Conjugate LinearAlgebra.conjugateTranspose(df) Signature: df:Frame<'R,'C> -> Matrix Type parameters: 'R, 'C Conjugate tranpose LinearAlgebra.determinant(df) Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C Matrix determinant LinearAlgebra.eigen(df) Signature: df:Frame<'R,'C> -> Evd Type parameters: 'R, 'C Eigen values and eigen vectors of matrix LinearAlgebra.inverse(df) Signature: df:Frame<'R,'C> -> Matrix Type parameters: 'R, 'C Inverse LinearAlgebra.isHermitian(df) Signature: df:Frame<'R,'C> -> bool Type parameters: 'R, 'C Check whether it is Hermitian matrix LinearAlgebra.isSymmetric(df) Signature: df:Frame<'R,'C> -> bool Type parameters: 'R, 'C Check whether it is symmetric matrix LinearAlgebra.kernel(df) Signature: df:Frame<'R,'C> -> Vector [] Type parameters: 'R, 'C Matrix kernel LinearAlgebra.lu(df) Signature: df:Frame<'R,'C> -> LU Type parameters: 'R, 'C LU decomposition LinearAlgebra.norm(df) Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C Norm LinearAlgebra.normCols(df) Signature: df:Frame<'R,'C> -> Vector Type parameters: 'R, 'C Norm of columns LinearAlgebra.normRows(df) Signature: df:Frame<'R,'C> -> Vector Type parameters: 'R, 'C Norm of rows LinearAlgebra.nullity(df) Signature: df:Frame<'R,'C> -> int Type parameters: 'R, 'C Matrix nullity LinearAlgebra.pseudoInverse(df) Signature: df:Frame<'R,'C> -> Matrix Type parameters: 'R, 'C Pseudo-inverse of matrix LinearAlgebra.qr(df) Signature: df:Frame<'R,'C> -> QR Type parameters: 'R, 'C QR decomposition LinearAlgebra.range(df) Signature: df:Frame<'R,'C> -> Vector [] Type parameters: 'R, 'C Matrix range LinearAlgebra.rank(df) Signature: df:Frame<'R,'C> -> int Type parameters: 'R, 'C Matrix rank LinearAlgebra.svd(df) Signature: df:Frame<'R,'C> -> Svd Type parameters: 'R, 'C SVD decomposition LinearAlgebra.trace(df) Signature: df:Frame<'R,'C> -> float Type parameters: 'R, 'C Matrix trace LinearAlgebra.transpose(df) Signature: df:Frame<'R,'C> -> Frame<'C,'R> Type parameters: 'R, 'C Transpose. Performance is faster than generic Frame.transpose as it only applies to frame of float values