Dual Dynamic Programing with cut selection: Convergence proof and numerical experiments

Vincent Guigues

European Journal of Operational Research, 2017, vol. 258, issue 1, 47-57

Abstract: We consider convex optimization problems formulated using dynamic programing equations. Such problems can be solved using the Dual Dynamic Programing algorithm combined with the Level 1 cut selection strategy or the Territory algorithm to select the most relevant Benders cuts. We propose a limited memory variant of Level 1 and show the convergence of DDP combined with the Territory algorithm, Level 1 or its variant for nonlinear optimization problems. In the special case of linear programs, we show convergence in a finite number of iterations. Numerical simulations illustrate the interest of our variant and show that it can be much quicker than a simplex algorithm on some large instances of portfolio selection and inventory problems.

Keywords: Dynamic Programing; Nonlinear programing; Decomposition algorithms; Dual Dynamic Programing; Pruning methods (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:258:y:2017:i:1:p:47-57

DOI: 10.1016/j.ejor.2016.10.047

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