Create a block-native linear operator.

Usage

linear_operator(
  dim,
  apply,
  apply_adjoint = NULL,
  dtype = "double",
  structure = general(),
  name = NULL,
  metadata = list(),
  operator_id = NULL,
  revision = NULL,
  portable = FALSE
)

Arguments

dim: Integer vector of length two giving row and column dimensions.
apply: Function implementing block multiplication by the operator.
apply_adjoint: Optional function implementing block multiplication by the adjoint operator.
dtype: Scalar character type label, currently "double" or "complex".
structure: Eigencore structure descriptor such as general() or hermitian().
name: Optional operator label used in plans and diagnostics.
metadata: Optional list of implementation metadata. Certificates read two optional entries, trusted as given: two_norm, the exact spectral norm ||A||_2 (makes the certificate scale exact), and frobenius_norm, whose ||A||_F / sqrt(min(dim)) gives a structural lower bound on ||A||_2. Without them certificates use lower bounds from the computed vectors.
operator_id: Optional non-empty character identity for a logical callback-operator lineage. Supply together with revision.
revision: Optional non-empty character revision for the values and behavior implemented by the callback. Supply together with operator_id.
portable: Whether an explicitly identified callback operator may be restored and validated in another R session. Built-in matrix-backed operators derive portable deterministic identity automatically.

Value

An eigencore_operator list containing dimensions, apply callbacks, scalar type, structure metadata, a display name, and implementation metadata.

Examples

A <- diag(c(3, 2, 1))
op <- linear_operator(
  dim = dim(A),
  apply = function(X, alpha = 1, beta = 0, Y = NULL) {
    Z <- alpha * (A %*% X)
    if (is.null(Y) || beta == 0) Z else Z + beta * Y
  },
  structure = hermitian(),
  metadata = list(frobenius_norm = sqrt(sum(A^2)))
)
fit <- eig_partial(op, k = 1, target = largest())
values(fit)
#> [1] 3