Aug 17, 2019 · The approach involves solving two optimization problems for each off-diagonal entry of the matrix being estimated. The problems provide lower ...
Jun 25, 2019 · We give a novel approach that solves the cardinality constrained likelihood problem to certifiable optimality. The approach uses techniques from ...
The approach uses techniques from mixed-integer optimization and convex optimization, and provides a high-quality solution with a guarantee on its suboptimality ...
Jun 25, 2019 · The approach uses techniques from mixed-integer optimization and convex optimization, and provides a high-quality solution with a guarantee on ...
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What is sparse inverse covariance estimation?
What is MCD covariance estimation?
Aug 17, 2019 · The approach uses techniques from mixed-integer optimization and convex optimization, and provides a high-quality solution with a guarantee on ...
Certifiably Optimal Sparse Inverse Covariance Estimation · no code ... We consider the maximum likelihood estimation of sparse inverse covariance matrices.
We propose a tailored branch-and-bound algorithm, Optimal-SPCA, that enables us to solve SPCA to certifiable optimality in seconds for $$n = 100$$ n = 100 s, $$ ...
Missing: Inverse Estimation.
Article. Bertsimas, D, Lamperski, J and Pauphilet, J (2020) Certifiably optimal sparse inverse covariance estimation. Mathematical Programming, 184 (1-2). pp ...
Feb 12, 2015 · PDF | We prove optimal sparsity oracle inequalities for the estimation of covariance matrix under the Frobenius norm.
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices.