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An interior-point Lagrangian decomposition method for separable convex optimization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Necoara, Ion |
| Copyright Year | 2009 |
| 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.optimization-online.org/DB_FILE/2009/02/2217.pdf |
| Alternate Webpage(s) | https://www.researchgate.net/profile/Johan_Suykens/publication/225472829_Interior-Point_Lagrangian_Decomposition_Method_for_Separable_Convex_Optimization/links/00463520b8bb55f520000000.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Augmented Lagrangian method Computation Convergence (action) Convex optimization Distributed algorithm Dual Gradient Lagrange multiplier Lagrangian relaxation Local convergence Mathematical optimization Merge sort Newton Newton's method Polynomial Rate of convergence Self-concordant function Shape optimization Time complexity |
| Content Type | Text |
| Resource Type | Article |
