Sparse inverse covariance selection¶
The following routines attempt to find a sparse inverse covariance matrix which could generate the given observations. This search is performed by attempting to solve the program
\[\text{min trace}(S Z) - \text{log}\;\text{det}\;Z +
\lambda \|\text{vec}(Z)\|_1\]
using the Alternating Direction Method of Multipliers.
The following functions were inspired by a simple ADMM solver due to Boyd et al. Elemental’s implementations make use of parallel (dense) linear algebra (including PMRRR for the symmetric tridiagonal eigensolver).
Python API¶
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SparseInvCov(D,lambd[,ctrl=None]
C++ API¶
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Int
SparseInvCov(const Matrix<F> &D, Base<F> lambda, Matrix<F> &Z, const SparseInvCovCtrl<Base<F>> &ctrl = SparseInvCovCtrl<Base<F>>())¶