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First CRAN release.
svd_partial() and eig_partial() compute
the top-k singular triplets or eigenpairs of large dense,
sparse (CSC), diagonal, banded/tridiagonal, and matrix-free operators
through native C++ kernels.passed flag. Bounds that
can only be estimated (for example stochastic norm estimates on centered
sparse operators) are reported as estimates and never produce an
unqualified passed.center(), scale_cols(),
compose(), crossprod_operator(),
linear_operator() — solves centered, scaled, and composed
problems without forming dense matrices.plan_solver() reports the
chosen kernel before a solve, and fit$method names the path
that actually ran. Problem classes without a production kernel carry
explicit reference labels.eigs(),
eigs_sym(), and svds() accept the same
which codes and additionally return certificates.Rscript inst/benchmarks/bench-readme.R.eig_full() for dense SPD and
general pencils, generalized_schur() and
generalized_svd() for dense QZ/GSVD, partial sparse general
pencils with nonsingular diagonal B via transformed native
Arnoldi, left eigenvectors and conditioning diagnostics on supported
dense paths, and pencil_norm_scaled alpha/beta
classification. Sparse SPD partial paths remain under
eig_partial() / LOBPCG / B-orthogonal Lanczos; general
sparse QZ and non-diagonal sparse B are explicit
unsupported boundaries. The real dense GSVD path currently requires a
linked LAPACK that provides the deprecated dggsvd
routine.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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