Interior-Point Lagrangian Decomposition Method for Separable Convex Optimization

I. Necoara () and J. A. K. Suykens
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I. Necoara: University Politehnica Bucharest
J. A. K. Suykens: Katholieke Universiteit Leuven

Journal of Optimization Theory and Applications, 2009, vol. 143, issue 3, No 8, 567-588

Abstract: Abstract In this paper, we propose a distributed algorithm for solving large-scale separable convex problems using Lagrangian dual decomposition and the interior-point framework. By adding self-concordant barrier terms to the ordinary Lagrangian, we prove under mild assumptions that the corresponding family of augmented dual functions is self-concordant. This makes it possible to efficiently use the Newton method for tracing the central path. We show that the new algorithm is globally convergent and highly parallelizable and thus it is suitable for solving large-scale separable convex problems.

Keywords: Separable convex optimization; Self-concordant functions; Interior-point methods; Augmented Lagrangian decomposition; Parallel computations (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10957-009-9566-8

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