Efficiently computing Schur complement I would like to compute the Schur complement $A-B^TC^{-1}B$, where $C = I+VV^T$ (diagonal plus low rank). The matrix $A$ has $10^3$ rows/columns, while $C$ has $10^6$ rows/columns. The Woodbury formula yields the following expression for the Schur complement:
$$A-B^TB+B^TV(I+V^TV)^{-1}V^TB.$$
This expression can be evaluated without storing large matrices, but the result suffers from numerical inaccuracies. Is there a numerically more stable way of computing the Schur complement, without high memory requirements?

Let us decompose the matrix $VV^T$ into its eigenvectors. Thus we have, 
$VV^T = \Sigma u_iu_i^T$ where $u_i$ is defined where $ u_i = \sqrt[2]\lambda_i \alpha_i$ where $\lambda_i$ is an eigenvalue of $VV^T$ and $\alpha_i$ is the corresponding eigenvector. Since the matrix $VV^T$ is positive definite its eigenvectors would be orthogonal meaning that $u_i^Tu_j= \sqrt[2]\lambda_i\alpha_i^T\alpha_j\sqrt[2]\lambda_j = 0$ when $i \neq j$.Applying Sherman-Morrison formula, for a $u_1$ we get - $(I+u_1u_1^T)^{-1} = I^{-1} - \frac{u_1u_1^T}{1 + u_1^Tu_1}$. This expression simplifies to $(I+u_1u_1^T)^{-1} = I - \frac{u_1u_1^T}{1 + \lambda_1}$ where $\lambda_1$ is the eigenvector corresponding to $u_1$. Now consider $(I^{-1}+ u_1u_1^T +u_2u_2^T) = (I+u_1u_1^T)^{-1} + \frac{(I - \frac{u_1u_1^T}{1 + \lambda_1})u_2u_2^T(I - \frac{u_1u_1^T}{1 + \lambda_1})}{1 + u_2^Tu_2}$. Since $u_1^Tu_2 =0$, $u_2u_2^Tu_1u_1^T = 0$. Hence the numerator simplifies as $u_2u_2^T$. Thus, $(I + u_1u_1^T + u_2u_2^T)^{-1} = I - \frac{u_1u_1^T}{1+\lambda_1} - \frac{u_2u_2^T}{1+\lambda_2}$. By induction it can be proven that $(I + \Sigma u_iu_i^T)^{-1} = I - \Sigma \frac{u_iu_i^T}{(1 + \lambda_i)}$. This can be used to compute the inverse of $(I + VV^T)$ with sufficient numerical stability. Regarding the final computation I think multiplication is inevitable. The time complexity of inversion by the given method is still $O(n^3)$ where $VV^T$ has dimensions $nxn$. So this would be the time complexity of the overall operation as well. But the memory required is $O(n^2)$. Also the net inaccuracy would be proportional to $\Sigma \Delta \lambda_i$ where $\Delta \lambda_i$ is the error in computing the eigenvalues.
