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30-Jan-2020 18:52

(And denotes the identity matrix.) Note that if you are solving a conventional least-squares problem, you will need to transpose your matrix before calling this function, and therefore it will be somewhat more efficient to construct your matrix in CSR format (so that its transpose will be in CSC format).factor it (i.e., it performs a “symbolic Cholesky decomposition”).Instead of loading all of A into memory, one can load in ‘strips’ of columns and pass them to this method one at a time.Note that no fill-reduction analysis is done; whatever permutation was chosen by the initial call to Note This method uses an efficient implementation that extracts the diagonal D directly from CHOLMOD’s internal representation.In the special case where C is the 1-by-1 unit matrix, this identity reduces to the Sherman–Morrison formula.In the special case when C is the identity matrix I, the matrix B where the matrix B can be approximated by a low-rank matrix UCV, for example using the singular value decomposition.The En KF has increasingly become an essential tool for data assimilation of numerical models.It is an attractive assimilation method because it can evolve the model covariance matrix for a non-linear model, through the use of an ensemble of model states, and it is easy to implement for any numerical model.

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This function ignores the actual contents of the matrix A.

All it cares about are (1) which entries are non-zero, and (2) whether A has real or complex type.

This module provides efficient implementations of all the basic linear algebra operations for sparse, symmetric, positive-definite matrices (as, for instance, commonly arise in least squares problems).

Specifically, it exposes most of the capabilities of the CHOLMOD package, including: is any real scalar (usually 0 or 1).

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This function ignores the actual contents of the matrix A.

All it cares about are (1) which entries are non-zero, and (2) whether A has real or complex type.

This module provides efficient implementations of all the basic linear algebra operations for sparse, symmetric, positive-definite matrices (as, for instance, commonly arise in least squares problems).

Specifically, it exposes most of the capabilities of the CHOLMOD package, including: is any real scalar (usually 0 or 1).

This can be derived using blockwise matrix inversion.