Please use this identifier to cite or link to this item: https://doi.org/10.1137/090772514
Title: Solving log-determinant optimization problems by a Newton-CG primal proximal point algorithm
Authors: Wang, C.
Sun, D. 
Toh, K.-C. 
Keywords: Log-determinant optimization problem
Newton's method
Proximal point algorithm
Sparse inverse covariance selection
Issue Date: 2010
Citation: Wang, C., Sun, D., Toh, K.-C. (2010). Solving log-determinant optimization problems by a Newton-CG primal proximal point algorithm. SIAM Journal on Optimization 20 (6) : 2994-3013. ScholarBank@NUS Repository. https://doi.org/10.1137/090772514
Abstract: We propose a Newton-CG primal proximal point algorithm (PPA) for solving large scale log-determinant optimization problems. Our algorithm employs the essential ideas of PPA, the Newton method, and the preconditioned CG solver. When applying the Newton method to solve the inner subproblem, we find that the log-determinant term plays the role of a smoothing term as in the traditional smoothing Newton technique. Focusing on the problem of maximum likelihood sparse estimation of a Gaussian graphical model, we demonstrate that our algorithm performs favorably compared to existing state-of-the-art algorithms and is much preferred when a high quality solution is required for problems with many equality constraints. © 2010 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/104148
ISSN: 10526234
DOI: 10.1137/090772514
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