Cholesky factorization A = L·Lᵀ with the active backend (koblas). Uplo.FULL checks that both triangles agree, while Uplo.LOWER or Uplo.UPPER names the authoritative triangle without checking the other. A non-positive pivot throws NotPositiveDefinite unless policy regularizes.

column

fun F64DenseMatrix.column(j: Int): F64DenseVector

Column j as a fresh vector, copied rather than viewed.

ger

fun F64DenseMatrix.ger(alpha: Double, x: F64VectorLike, y: F64VectorLike)

Rank-one update A = A + alpha * x * yT (BLAS dger) in place. Subtract by passing alpha = -1.0.

ldl

fun F64DenseMatrix.ldl(workspace: Workspace? = null, uplo: Uplo = Uplo.FULL): F64LdlDecomposition

Symmetric indefinite factorization A = L·D·Lᵀ with the active backend. Uplo.FULL checks that both triangles agree; Uplo.LOWER or Uplo.UPPER selects one triangle without checking the other.

fun F64DenseMatrix.lu(): F64LuDecomposition

LU-factorize this square matrix with the active backend (koblas); see F64Decompositions.factor.

minus

operator fun F64DenseMatrix.minus(other: F64DenseMatrix): F64DenseMatrix

A - B, allocating. axpy with alpha = -1.0 accumulates into an existing operand.

norm1

fun F64DenseMatrix.norm1(): Double

Matrix 1-norm, the maximum absolute column sum (LAPACK dlange with norm 1). This is the anorm rcond expects, computed before the matrix is factored.

normFro

fun F64DenseMatrix.normFro(): Double

Frobenius norm (LAPACK dlange with norm F). Rescales like norm2 against overflow and underflow.

normInf

fun F64DenseMatrix.normInf(workspace: Workspace? = null): Double

Matrix infinity-norm, the maximum absolute row sum (LAPACK dlange with norm I).

plus

operator fun F64DenseMatrix.plus(other: F64DenseMatrix): F64DenseMatrix

A + B, allocating. axpy accumulates into an existing operand.

fun F64DenseMatrix.qr(workspace: Workspace? = null): F64QrDecomposition

QR factorization A = Q·R with the active backend; see F64Decompositions.qr.

qrPivoted

fun F64DenseMatrix.qrPivoted(tolerance: Double = AUTOMATIC_RANK_TOLERANCE, workspace: Workspace? = null): F64PivotedQrDecomposition

QR with column pivoting, A·P = Q·R, with the active backend; see F64Decompositions.qrPivoted.

row

fun F64DenseMatrix.row(i: Int): F64DenseVector

Row i as a fresh vector, gathered across the backing. Prefer column where the algorithm allows.

scaleColumns

fun F64DenseMatrix.scaleColumns(d: DoubleArray)

Scale column j by d(j) in place, the product A * D for the diagonal D with entries d(j).

scaleRows

fun F64DenseMatrix.scaleRows(d: DoubleArray)

Scale row i by d(i) in place, the product D * A for the diagonal D with entries d(i).

syr

fun F64DenseMatrix.syr(alpha: Double, x: F64VectorLike, uplo: Uplo = Uplo.FULL)

Symmetric rank-1 update A += alpha * x * xT (BLAS dsyr) in place. See F64Blas.syr.

syr2

fun F64DenseMatrix.syr2(alpha: Double, x: F64VectorLike, y: F64VectorLike, uplo: Uplo = Uplo.FULL)

Symmetric rank-2 update A += alpha * (x * yT + y * xT) (BLAS dsyr2) in place. See F64Blas.syr2.

times

operator fun F64DenseMatrix.times(other: F64DenseMatrix): F64DenseMatrix

A * B (BLAS dgemm), allocating. gemm accumulates into an existing C instead.

operator fun F64DenseMatrix.times(alpha: Double): F64DenseMatrix

alpha * A, allocating. scale multiplies in place.

operator fun F64MatrixLike.times(x: F64VectorLike): F64DenseVector

Matrix-vector product into a fresh dense result for any F64MatrixLike against any F64VectorLike. gemv provide transpose and destination-buffer variants.

transpose

fun F64DenseMatrix.transpose(): F64DenseMatrix

Fresh transposed matrix. For products, prefer the transpose flags on gemv and gemm, which read the original storage without copying.

unaryMinus