JLCF32LinearAlgebra

jla32.spad line 393 [edit on github]

Linear Algebra functions computed using Julia and its algorithms. 32 bits version.

conditionNumber: (JLComplexF32Matrix, JLFloat32) -> JLFloat32

conditionNumber(m) computes the p-condition number of m.

conditionNumber: JLComplexF32Matrix -> JLFloat32

conditionNumber(m) computes the condition number of m.

condSkeel: JLComplexF32Matrix -> JLFloat32

condSkeel(m) computes the Skeel condition number of m.

eigen!: JLComplexF32Matrix -> Record(values: JLComplexF32Vector, vectors: JLComplexF32Matrix)

eigen!(m) computes the spectral decomposition of m but overwrites m to save memory space.

eigen: JLComplexF32Matrix -> Record(values: JLComplexF32Vector, vectors: JLComplexF32Matrix)

eigen(m) computes the spectral decomposition of m.

eigenSystem!: JLComplexF32Matrix -> Record(values: JLComplexF32Vector, leftVectors: JLComplexF32Matrix, rightVectors: JLComplexF32Matrix)

eigenSystem!(m) computes the spectral decomposition of m but overwrites m to save memory space.

eigenSystem: JLComplexF32Matrix -> Record(values: JLComplexF32Vector, leftVectors: JLComplexF32Matrix, rightVectors: JLComplexF32Matrix)

eigenSystem(m) computes the spectral decomposition of m.

eigvals!: JLComplexF32Matrix -> JLComplexF32Vector

eigvals!(m) returns the eigen values of m but overwrites m to save memory space.

eigvals: JLComplexF32Matrix -> JLComplexF32Vector

eigvals(m) returns the eigen values of m.

eigvecs: JLComplexF32Matrix -> JLComplexF32Matrix

eigvecs(m) returns the eigen vectors of m.

exp: JLComplexF32Matrix -> JLComplexF32Matrix

exp(m) returns the matrix exponential of m.

log: JLComplexF32Matrix -> JLComplexF32Matrix

log(m) tries to compute the principal matrix logarithm of m. Otherwise, returns a non pricipal matrix logarithm of m if possible.

mpInverse: JLComplexF32Matrix -> JLComplexF32Matrix

mpInverse(m) returns the Moore-Penrose pseudo inverse of m.

norm: (JLComplexF32Matrix, JLFloat32) -> JLFloat32

norm(m,p) computes the p-norm of m.

norm: (JLComplexF32Vector, JLFloat32) -> JLFloat32

norm(v,p) computes th p-norm of v.

norm: JLComplexF32Matrix -> JLFloat32

norm(m) computes the 2-norm of m, also known as the Frobenius norm.

norm: JLComplexF32Vector -> JLFloat32

norm(v) computes the 2-norm of v.

normalize!: JLComplexF32Matrix -> JLComplexF32Matrix

normalize!(m) destructively normalize m such that its norm equals to 1.

normalize!: JLComplexF32Vector -> JLComplexF32Vector

normalize!(v) destructively normalize v such that norm(v) equals to 1.

normalize: JLComplexF32Matrix -> JLComplexF32Matrix

normalize(m) returns normalized m such that its norm equals to 1.

normalize: JLComplexF32Vector -> JLComplexF32Vector

normalize(v) returns normalized v such that its norm equals to 1.

operatorNorm: (JLComplexF32Matrix, JLFloat32) -> JLFloat32

operatorNorm(m,p) computes the operator norm of m induced by the vector p-norm.

operatorNorm: JLComplexF32Matrix -> JLFloat32

operatorNorm(m) computes the operator norm of m induced by the vector 2-norm.

rank!: (JLComplexF32Matrix, JLFloat32) -> NonNegativeInteger

rank!(m, tol) computes rank of m. Counts singular value with magnitude greater than tol but overwrites m to save memory space.

rank: (JLComplexF32Matrix, JLFloat32) -> NonNegativeInteger

rank(m, tol) computes rank of m. Counts singular value with magnitude greater than tol.

solve!: (JLComplexF32Matrix, JLComplexF32Matrix) -> JLComplexF32Matrix

solve!(A,B) solves the matrix equation A*X=B. Overwrites B with matrix X and returns X.

solve: (JLComplexF32Matrix, JLComplexF32Matrix) -> JLComplexF32Matrix

solve(A,B) solves the matrix equation A*X=B, and returns X.

sqrt: JLComplexF32Matrix -> JLComplexF32Matrix

sqrt(m) returns the principal square root of m.

svd!: JLComplexF32Matrix -> Record(U: JLComplexF32Matrix, sv: JLFloat32Vector, Vt: JLComplexF32Matrix)

svd!(m) is the same as svd(m) but overwites a to save memory space.

svd: JLComplexF32Matrix -> Record(U: JLComplexF32Matrix, sv: JLFloat32Vector, Vt: JLComplexF32Matrix)

svd(m) computes the singular value decomposition SVD of m such that SVD.U * diagonalMatrix(SVD.sv) * SVD.Vt = m.

svdvals!: JLComplexF32Matrix -> JLFloat32Vector

svdvals!(m) returns the singular values of m but overwrites m to save memory space.

svdvals: JLComplexF32Matrix -> JLFloat32Vector

svdvals(m) returns the singular values of m.