Please use this identifier to cite or link to this item:
|Title:||An inexact interior point method for L1-regularized sparse covariance selection|
|Keywords:||Inexact interior point method|
Inexact search direction
Log-determinant semidefinite programming
Sparse inverse covariance selection
|Source:||Li, L.,Toh, K.-C. (2010). An inexact interior point method for L1-regularized sparse covariance selection. Mathematical Programming Computation 2 (3-4) : 291-315. ScholarBank@NUS Repository. https://doi.org/10.1007/s12532-010-0020-6|
|Abstract:||Sparse covariance selection problems can be formulated as logdeterminant (log-det) semidefinite programming (SDP) problems with large numbers of linear constraints. Standard primal-dual interior-point methods that are based on solving the Schur complement equation would encounter severe computational bottlenecks if they are applied to solve these SDPs. In this paper, we consider a customized inexact primal-dual path-following interior-point algorithm for solving large scale log-det SDP problems arising from sparse covariance selection problems. Our inexact algorithm solves the large and ill-conditioned linear system of equations in each iteration by a preconditioned iterative solver. By exploiting the structures in sparse covariance selection problems, we are able to design highly effective preconditioners to efficiently solve the large and ill-conditioned linear systems. Numerical experiments on both synthetic and real covariance selection problems show that our algorithm is highly efficient and outperforms other existing algorithms. © Springer and Mathematical Optimization Society 2010.|
|Source Title:||Mathematical Programming Computation|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 17, 2018
checked on Jan 14, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.