Backward error analysis of the Lanczos bidiagonalization with reorthogonalization

The $k$-step Lanczos bidiagonalization reduces a matrix $A\in\mathbb{R}^{m\times n}$ into a bidiagonal form $Bk\in\mathbb{R}^{(k+1)\times k}$ while generates two orthonormal matrices $U{k+1}\in\mathbb{R}^{m\times (k+1)}$ and $V{k+1}\in\mathbb{R}^{n\times {(k+1)}}$. However, any practical implementation of the algorithm suffers from loss of orthogonality of $U{k+1}$ and $V{k+1}$ due to the presence of rounding errors, and several reorthogonalization strategies are proposed to maintain some level of orthogonality. In this paper, by writing various reorthogonalization strategies in a general form we make a backward error analysis of the Lanczos bidiagonalization with reorthogonalization (LBRO). Our results show that the computed $Bk$ by the $k$-step LBRO of $A$ with starting vector $b$ is the exact one generated by the $k$-step Lanczos bidiagonalization of $A+E$ with starting vector $b+\delta{b}$ (denoted by LB($A+E,b+\delta{b}$)), where the 2-norm of perturbation vector/matrix $\delta{b}$ and $E$ depend on the roundoff unit and orthogonality levels of $U{k+1}$ and $V{k+1}$. The results also show that the 2-norm of $U{k+1}-\bar{U}{k+1}$ and $V{k+1}-\bar{V}{k+1}$ are controlled by the orthogonality levels of $U{k+1}$ and $V{k+1}$, respectively, where $\bar{U}{k+1}$ and $\bar{V}{k+1}$ are the two orthonormal matrices generated by the $k$-step LB($A+E,b+\delta{b}$) in exact arithmetic. Thus the $k$-step LBRO is mixed forward-backward stable as long as the orthogonality of $U{k+1}$ and $V{k+1}$ are good enough. We use this result to investigate the backward stability of LBRO based SVD computation algorithm and LSQR algorithm. Numerical experiments are made to confirm our results.