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Changelog

Source: NEWS.md

eigencore 1.0.0

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.
  • Every result carries a numerical certificate: residuals for both singular relations, a backward-error bound, orthogonality loss, a labeled norm bound, and a single 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.
  • Operator algebra — center(), scale_cols(), compose(), crossprod_operator(), linear_operator() — solves centered, scaled, and composed problems without forming dense matrices.
  • Transparent method selection: 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.
  • RSpectra-compatible wrappers eigs(), eigs_sym(), and svds() accept the same which codes and additionally return certificates.
  • Benchmarked against ‘RSpectra’, ‘irlba’, and ‘PRIMME’; reproduce with Rscript inst/benchmarks/bench-readme.R.
  • Generalized eigen support: 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.
  • The exported API is stable as of 1.0.0; breaking changes from here follow semantic versioning.