I am trying to estimate a covariance matrix from the Hessian of a posterior mode. However, this Hessian is indefinite (possibly because of numerical/roundoff issues), and thus, the Cholesky decomposition does not exist. So, I want to use a modified Cholesky algorithm to estimate a Cholesky of a pseudovariance that is reasonably close to the original matrix. I know that there are R packages that contain code for Gill-Murray and Schnabel-Eskow algorithms for standard, dense, base-R matrices. But my Matrix is large (k=30000), and sparse (block-arrow structure, stored as a dsCMatrix class from the Matrix package).
Is anyone aware of existing code (or perhaps an algorithm that is easy to adapt) that would perform a modified Cholesky decomposition on a large, sparse indefinite matrix, preferably working on sparseMatrix classes? Alternatively, is there a way I could compute a sparse LDL' decomposition from an existing R function, and quickly modify the output? Thanks, Michael ------------------------------------------- Michael Braun Associate Professor of Management Science MIT Sloan School of Management 100 Main St.., E62-535 Cambridge, MA 02139 bra...@mit.edu 617-253-3436
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