[Home] Module FlyingFrog.LinearAlgebra.Float32

Linear algebra routines for matrices of single-precision floating point numbers

Value	Description
val cholesky : Matrix<float32> -> Matrix<float32>	Decompose a symmetric positive-definite matrix A into A = L L^T, returning the lower-triangular matrix L.
val determinant : Matrix<float32> -> float32	Compute the determinant of a matrix
val inverse : Matrix<float32> -> Matrix<float32>	Matrix inverse
val linear_solve : Matrix<float32> -> (Vector<float32> -> Vector<float32>)	Given a matrix, return a function that multiplies the given vector by the inverse of the matrix
val lu : Matrix<float32> -> Matrix<float32> * int array	Decompose the given matrix A into a lower-triangular matrix L and upper triangular matrix U such that A = LU, returning (L, U) and a pivot array.
val svd : Matrix<float32> -> Matrix<float32> * Matrix<float32> * Matrix<float32>	Compute the Singular Value Decomposition (SVD) of a matrix, representing it as the product of three matrices U, S and V^T where U and V are unitary and S is diagonal.
val symmetric_eigen : Matrix<float32> -> (float32 * Vector<float32>) array	Compute the real-valued eigenvalues and corresponding eigenvectors of a real symmetric matrix