Compute a partial eigendecomposition.

Usage

eig_partial(
  A,
  k,
  target = largest(),
  B = NULL,
  method = auto(),
  tol = 1e-08,
  maxit = NULL,
  vectors = TRUE,
  seed = NULL,
  certify = TRUE,
  allow_dense_fallback = c("auto", "never", "always")
)

Arguments

A: Matrix or eigencore operator.
k: Number of eigenpairs to compute.
target: Eigencore eigenvalue target descriptor.
B: Optional metric matrix or operator for generalized problems.
method: Solver method descriptor.
tol: Convergence and certification tolerance.
maxit: Optional iteration limit.
vectors: Whether to compute vectors.
seed: Optional random seed for stochastic solver components.
certify: Whether to compute certification diagnostics.
allow_dense_fallback: Dense fallback policy.

Value

An eigencore_eigen_result containing computed values, optional vectors, certificate diagnostics, method/plan metadata, and convergence diagnostics.

Examples

A <- diag(c(5, 4, 3, 2, 1))
A[1, 2] <- A[2, 1] <- 0.1
fit <- eig_partial(A, k = 2, target = largest())
values(fit)
#> [1] 5.009902 3.990098
certificate(fit)$passed
#> [1] TRUE

# Generalized SPD problem A x = lambda B x
B <- diag(c(2, 1, 1, 1, 1))
gfit <- eig_partial(A, B = B, k = 2, target = smallest())
values(gfit)
#> [1] 1 2