On how to solve large-scale log-determinant optimization problems

Chengjing Wang ()
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Chengjing Wang: Southwest Jiaotong University

Computational Optimization and Applications, 2016, vol. 64, issue 2, No 7, 489-511

Abstract: Abstract We propose a proximal augmented Lagrangian method and a hybrid method, i.e., employing the proximal augmented Lagrangian method to generate a good initial point and then employing the Newton-CG augmented Lagrangian method to get a highly accurate solution, to solve large-scale nonlinear semidefinite programming problems whose objective functions are a sum of a convex quadratic function and a log-determinant term. We demonstrate that the algorithms can supply a high quality solution efficiently even for some ill-conditioned problems.

Keywords: Quadratic programming; Log-determinant optimization problem; Proximal augmented Lagrangian method; Augmented Lagrangian method; Newton-CG method (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10589-015-9812-y

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