must have the same pattern of non-zeros as the matrix used to create this factor originally. The usual use for this is to factor AA’ when A has a large number of columns, or those columns become available incrementally.
For the sparse case, I believe that CHOLMOD does provide these capabilities.factor it (i.e., it performs a “symbolic Cholesky decomposition”).This function ignores the actual contents of the matrix A.The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor.
The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution rates (operations/second) that are close to the peak rates for dense matrix computations. We propose the implementation of efficient schemes for updating the LL(T) or LDL(T) factors computed in DSCPACK-S to meet the computational requirements of their project.I need to use rank 1 Cholesky updates for an adaptive algorithm I am working on, to bring down complexity from O(n^3) to O(n^2), and was a bit unclear from this open issue if there is some preliminary yet operational support (either built-in or by invoking QRupdate)?I would also love being able to update a QR factorization by adding or deleting columns.All it cares about are (1) which entries are non-zero, and (2) whether A has real or complex type.