An Improved Incremental Singular Value Decomposition and New Error Bounds

The incremental singular value decomposition (SVD) updates a truncated SVD as new columns arrive, replacing a single large SVD with a sequence of small ones. In floating-point arithmetic, each update multiplies the running singular basis by a small orthogonal factor, and the accumulated product loses orthogonality unless the basis is reorthogonalized periodically. How often this reorthogonalization is needed has been an open question; we answer it by restructuring the algorithm so that rank-preserving updates are accumulated implicitly and applied in batches, reducing the number of large orthogonal multiplications from $n$, the stream length, to $r$, the numerical rank. We prove that this restructuring preserves the exact-arithmetic output of the original algorithm and establish two forward-error bounds. First, we sharpen the existing operator-norm truncation bound from $n\,\texttt{tol}$ to $\sqrt{n}\,\texttt{tol}$, and show the new rate is attained on a constructive example. Second, under a standard probabilistic rounding-error model, we prove that the loss of orthogonality of the computed left factor is independent of the stream length $n$ and depends on $m$, the length of each incoming column, only through a single $\sqrt{m}$ factor. Numerical experiments confirm both bounds and demonstrate that the proposed algorithm runs $4.5\times$ to $34\times$ faster than its closest competitors.