Nothing
Code
writeLines(vapply(exports, api_signature, character(1L)))
Output
adjoint(x, ...)
alpha_beta(x, ...)
as_operator(x, ...)
auto()
backward_error(x, ...)
both_ends(k_low, k_high)
center(A, rows = FALSE, columns = TRUE, row_means = NULL, col_means = NULL, name = NULL)
certificate(x, ...)
check_adjoint(A, trials = 20, tol = 1e-12, seed = NULL)
compose(A, B, name = NULL)
crossprod_operator(A, name = NULL)
diagnostics(x, ...)
eig_full(A, B = NULL, structure = NULL, vectors = TRUE, tol = 1e-08, allow_dense_fallback = c("auto", "never", "always"), ...)
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"))
eigen_problem(A, metric = NULL, structure = NULL, target = largest(), transform = NULL)
eigs(A, k, which = "LM", opts = list(), ...)
eigs_sym(A, k, which = "LA", opts = list(), ...)
euclidean(dim, dtype = "double")
general()
generalized_schur(A, B, sort = NULL, vectors = TRUE, ...)
generalized_svd(A, B, tol = 1e-08, ...)
golub_kahan(max_subspace = NULL, reorthogonalize = TRUE)
hermitian()
lanczos(max_subspace = NULL, max_restarts = NULL, block = 1L, reorthogonalize = TRUE)
largest()
largest_imaginary()
largest_magnitude()
largest_real()
left_vectors(x, ...)
linear_operator(dim, apply, apply_adjoint = NULL, dtype = "double", structure = general(), name = NULL, metadata = list())
lobpcg(maxit = 200L, preconditioner = NULL, constraints = NULL)
nearest(sigma)
plan_solver(problem, ...)
randomized(oversample = 10, n_iter = 2, block = NULL, normalizer = c("qr", "lu", "none"), refine = TRUE)
right_vectors(x, ...)
scale_cols(A, weights, name = NULL)
scale_rows(A, weights, name = NULL)
shift_invert(sigma, solve = NULL, factorization = NULL)
shifted_cholesky_preconditioner(A, shift = 0)
shifted_diagonal_preconditioner(A, shift = 0)
shifted_tridiagonal_preconditioner(A, shift = 0)
smallest()
smallest_imaginary()
smallest_magnitude()
smallest_real()
svd_partial(A, rank, target = largest(), method = auto(), tol = 1e-08, vectors = c("both", "left", "right", "none"), seed = NULL, certify = TRUE, allow_dense_fallback = c("auto", "never", "always"))
svd_problem(A, domain = NULL, codomain = NULL, target = largest())
svds(A, k, nu = k, nv = k, opts = list(), ...)
symmetric_operator(A, validate = TRUE, tol = 1e-10)
values(x, ...)
vectors(x, ...)
Code
writeLines(regs)
Output
adjoint.eigencore_operator
as_operator.default
as_operator.eigencore_operator
as_operator.matrix
plan_solver.eigencore_eigen_problem
plan_solver.eigencore_svd_problem
print.eigencore_benchmark
print.eigencore_certificate
print.eigencore_eigen_result
print.eigencore_gsvd_result
print.eigencore_operator
print.eigencore_plan
print.eigencore_svd_result
print.eigencore_validation
residuals.eigencore_certificate
residuals.eigencore_eigen_result
residuals.eigencore_svd_result
solve.eigencore_eigen_problem
solve.eigencore_svd_problem
Code
cat("eigen result:\n")
Output
eigen result:
Code
writeLines(sort(names(efit)))
Output
backward_error
certificate
iterations
matvecs
method
nconv
orthogonality
plan
requested
residuals
restart
target
values
vectors
warnings
Code
cat("svd result:\n")
Output
svd result:
Code
writeLines(sort(names(sfit)))
Output
backward_error
certificate
d
iterations
matvecs
method
nconv
orthogonality
plan
requested
residuals
stage_seconds
target
u
v
values
warnings
Code
cat("certificate:\n")
Output
certificate:
Code
writeLines(sort(names(efit$certificate)))
Output
backward_error
certificate_type
converged
failed_indices
max_backward_error
max_orthogonality_loss
max_residual
norm_bound_type
notes
orthogonality
orthogonality_passed
orthogonality_required
orthogonality_tolerance
passed
residuals
scale
scale_is_estimate
tolerance
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