Efficient implementations of the modified Gram-Schmidt orthogonalization with a non-standard inner product
This work addresses a computational bottleneck in QR factorization for non-standard inner products, offering practical efficiency gains for numerical linear algebra applications.
The paper proposes two efficient implementations (HA and HP types) of modified Gram-Schmidt orthogonalization for non-standard inner products, reducing matrix-vector multiplications from 2n to n while maintaining competitive accuracy.
The modified Gram-Schmidt (MGS) orthogonalization is one of the most well-used algorithms for computing the thin QR factorization. MGS can be straightforwardly extended to a non-standard inner product with respect to a symmetric positive definite matrix $A$. For the thin QR factorization of an $m \times n$ matrix with the non-standard inner product, a naive implementation of MGS requires $2n$ matrix-vector multiplications (MV) with respect to $A$. In this paper, we propose $n$-MV implementations: a high accuracy (HA) type and a high performance (HP) type, of MGS. We also provide error bounds of the HA-type implementation. Numerical experiments and analysis indicate that the proposed implementations have competitive advantages over the naive implementation in terms of both computational cost and accuracy.