SparseCholesky

final def !=(arg0: Any): Boolean

final def ##(): Int

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def apply(A: CSCMatrix[Double]): CSCMatrix[Double]

calculate sparse Cholesky decompositon, return factor L for A = LL^T

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def incompleteSparseCholesky(A: CSCMatrix[Double], tolerance: Double = 1e-15): CSCMatrix[Double]

calculate the incomplete Cholesky factorization, useful as a preconditioner for CG (only approximate, as sparse as A, fast)

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def solve(A: CSCMatrix[Double], b: DenseVector[Double], fillInReduction: PermutationStrategy = PermutationStrategy.default): DenseVector[Double]

solve the linear system Ax = b for a sparse positive definite and symmetric matrix A using a Cholesky decomposition

def sparseCholesky(A: CSCMatrix[Double], tolerance: Double = 1e-15): CSCMatrix[Double]

Cholesky decomposition of a sparse, positive definite and symmetric matrix A

def substitutionSolver(choleskyFactor: CSCMatrix[Double], b: DenseVector[Double], P: Permutation): DenseVector[Double]

solves PLL.tP.t * x = b, for L sparse lower triangular matrix, P permutation

def substitutionSolver(choleskyFactor: CSCMatrix[Double], b: DenseVector[Double]): DenseVector[Double]

solves LL.t * x = b, for L sparse lower triangular matrix,

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