Class CovarianceSelectionGLASSOFAST

  • All Implemented Interfaces:
    CovarianceSelectionSolver

    public class CovarianceSelectionGLASSOFAST
    extends Object
    implements CovarianceSelectionSolver
    GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem. The covariance selection problem is formulated as this: \[ \max_{X} \log(\det X) - Tr(\Sigma X)-\rho Card(X) \] in the variable of \(X \in S^n\), where \(\Sigma \in S^n\) is the sample covariance matrix, \(Card(X)\) the cardinality of \(X\), i.e., the number of non-zero coefficients in \(X\). \(\rho > 0\) is a parameter controlling the tradeoff between the likelihood and structure.
    See Also:
    • "Sustik, M.A. and Calderhead, B., "GLASSOFAST: An efficient GLASSO implementation," UTCS Technical Report TR-12-29, November 6, 2012."
    • "A. d'Aspremont, "Identifying Small Mean Reverting Portfolios", 2008."
    • "O. Banerjee, L. E. Ghaoui and A. d'Aspremont, "Model Selection Through Sparse Maximum Likelihood Estimation for multivariate Gaussian or Binary Data," Journal of Machine Learning Research, 9, pp. 485-516, March 2008."
    • Constructor Detail

      • CovarianceSelectionGLASSOFAST

        public CovarianceSelectionGLASSOFAST​(CovarianceSelectionProblem problem)
        Solves the maximum likelihood problem for covariance selection.
        Parameters:
        problem - the covariance selection problem
    • Method Detail

      • isConverged

        public boolean isConverged()
        Checks if the algorithm converges.
        Returns:
        true if the algorithm stops before maximum iteration is reached