Solve a planned eigenproblem.

S3 method that runs the planned solver for an eigenproblem built by eigen_problem(). Most users call eig_partial(), which constructs the problem and dispatches here; call solve() directly when you want to build a problem once and reuse or inspect it. Returns a certified partial eigendecomposition.

Usage

# S3 method for class 'eigencore_eigen_problem'
solve(
  a,
  b,
  k,
  method = auto(),
  tol = 1e-08,
  maxit = NULL,
  vectors = TRUE,
  certify = TRUE,
  allow_dense_fallback = c("auto", "never", "always"),
  initial_subspace = NULL,
  ...
)

Arguments

a

Eigencore eigen problem object.

b

Unused second argument reserved by the base solve() generic.

k

Number of eigenpairs to compute.

method

Solver method descriptor.

tol

Convergence and certification tolerance.

maxit

Optional iteration limit.

vectors

Whether to compute vectors.

certify

Whether to compute certification diagnostics.

allow_dense_fallback

Dense fallback policy.

initial_subspace

Optional numeric matrix of starting directions (a warm start). Supported on standard real Hermitian Lanczos paths — native dense double / dgCMatrix, native matrix-free callbacks for lanczos(block > 1), and the scalar matrix-free reference path for lanczos(block = 1); supplying it on any other planned path is an error. The subspace is only a starting hint, never a source of reused convergence: every solve recomputes projected quantities, residuals, orthogonality, convergence, and a fresh current-operator certificate. An already-invariant supplied subspace is discarded to a cold start because residual certification alone cannot establish that it contains the requested extremal eigenpairs. NULL (the default) preserves the cold random start exactly.

...

Reserved for future solver options.

Value

An eigencore_eigen_result.