Hi Douglas, thanks for your answer.
My question indeed arises from a sparse matrix context: 'A' is sparse symmetric, and 'C' must also be sparse since it would otherwise fill.
It comes from a Bayes regression with a very large number of parameters; a loop over blocks will do the job in my specific case. Yet I wondered about this since similar need for "covariance updating" may arise from linear filtering or kriging.
Douglas Bates wrote
The CHOLMOD library provides sparse matrix methods, especially the Cholesky decomposition and modifications to that decomposition, which is where the name comes from. Do you expect to work with sparse matrices? I haven't seem too much code for update/downdate operations on the Cholesky decomposition for dense matrices. There were rank-1 update/downdate methods in Linpack but they didn't make it through to Lapack.
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