AAR-based decomposition algorithm for non-linear convex optimisation

Nima Rabiei () and Jose Muñoz ()

Computational Optimization and Applications, 2015, vol. 62, issue 3, 786 pages

Abstract: In this paper we present a method for decomposing a class of convex non-linear programmes which are frequently encountered in engineering plastic analysis. These problems have second-order conic memberships constraints and a single complicating variable in the objective function. The method is based on finding the distance between the feasible sets of the decomposed problems, and updating the global optimal value according to the value of this distance. The latter is found by exploiting the method of averaged alternating reflections, which is here adapted to the optimisation problem at hand. The method is specially suited for non-linear problems and as our numerical results show, its convergence is independent of the number of variables of each sub-domain. We have tested the method with an illustrative example and with problems that have more than 10,000 variables. Copyright Springer Science+Business Media New York 2015

Keywords: Decomposition; Convex optimisation; Non-linear optimisation; Second-order cone program (SOCP); Averaged alternating reflections (AAR) (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10589-015-9750-8

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