QUIC: quadratic approximation for sparse inverse covariance estimation. The ℓ 1 -regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel algorithm for solving the resulting optimization problem which is a regularized log-determinant program. In contrast to recent state-of-the-art methods that largely use first order gradient information, our algorithm is based on Newton’s method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem. We show that our method is superlinearly convergent, and present experimental results using synthetic and real-world application data that demonstrate the considerable improvements in performance of our method when compared to previous methods.

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  1. Choi, Young-Geun; Lee, Seunghwan; Yu, Donghyeon: An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units (2022)
  2. Nguyen, Viet Anh; Kuhn, Daniel; Esfahani, Peyman Mohajerin: Distributionally robust inverse covariance estimation: the Wasserstein shrinkage estimator (2022)
  3. Tran-Dinh, Quoc; Liang, Ling; Toh, Kim-Chuan: A new homotopy proximal variable-metric framework for composite convex minimization (2022)
  4. Bian, Fengmiao; Liang, Jingwei; Zhang, Xiaoqun: A stochastic alternating direction method of multipliers for non-smooth and non-convex optimization (2021)
  5. Krock, Mitchell; Kleiber, William; Becker, Stephen: Nonstationary modeling with sparsity for spatial data via the basis graphical Lasso (2021)
  6. Molstad, Aaron J.; Sun, Wei; Hsu, Li: A covariance-enhanced approach to multitissue joint eQTL mapping with application to transcriptome-wide association studies (2021)
  7. Park, Seongoh; Wang, Xinlei; Lim, Johan: Estimating high-dimensional covariance and precision matrices under general missing dependence (2021)
  8. Phan, Dzung T.; Menickelly, Matt: On the solution of (\ell_0)-constrained sparse inverse covariance estimation problems (2021)
  9. Bertsimas, Dimitris; Lamperski, Jourdain; Pauphilet, Jean: Certifiably optimal sparse inverse covariance estimation (2020)
  10. Ghose, Amur; Jaini, Priyank; Poupart, Pascal: Learning directed acyclic graph SPNs in sub-quadratic time (2020)
  11. Lin, Tiger W.; Chen, Yusi; Bukhari, Qasim; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.: Differential covariance: a new method to estimate functional connectivity in fMRI (2020)
  12. Li, Tianxi; Qian, Cheng; Levina, Elizaveta; Zhu, Ji: High-dimensional Gaussian graphical models on network-linked data (2020)
  13. Nakagaki, Takashi; Fukuda, Mituhiro; Kim, Sunyoung; Yamashita, Makoto: A dual spectral projected gradient method for log-determinant semidefinite problems (2020)
  14. Tajbakhsh, Sam Davanloo; Aybat, Necdet Serhat; Del Castillo, Enrique: On the theoretical guarantees for parameter estimation of Gaussian random field models: a sparse precision matrix approach (2020)
  15. Wang, Cheng; Jiang, Binyan: An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss (2020)
  16. Wu, Yichong; Li, Tiejun; Liu, Xiaoping; Chen, Luonan: Differential network inference via the fused D-trace loss with cross variables (2020)
  17. Yu, Hang; Wu, Songwei; Xin, Luyin; Dauwels, Justin: Fast Bayesian inference of sparse networks with automatic sparsity determination (2020)
  18. Zhang, Yangjing; Zhang, Ning; Sun, Defeng; Toh, Kim-Chuan: A proximal point dual Newton algorithm for solving group graphical Lasso problems (2020)
  19. Ağraz, Melih; Purutçuoğlu, Vilda: Extended Lasso-type MARS (LMARS) model in the description of biological network (2019)
  20. Bollhöfer, Matthias; Eftekhari, Aryan; Scheidegger, Simon; Schenk, Olaf: Large-scale sparse inverse covariance matrix estimation (2019)

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