期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 67, 期 1, 页码 413-420出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3057061
关键词
Couplings; Resource management; Distributed algorithms; Heuristic algorithms; Linear programming; Approximation algorithms; Task analysis; Constraint-coupled optimization; distributed optimization; mixed-integer linear programming
资金
- European Research Council under the European Union's Horizon 2020 Research and Innovation Programme [638992-OPT4SMART]
This article addresses the distributed mixed-integer linear programming setup in control applications, proposing a fully distributed algorithm that can provide accurate feasible solutions in finite time with low suboptimality bounds.
This article deals with a distributed Mixed-Integer Linear Programming (MILP) setup arising in several control applications. Agents of a network aim to minimize the sum of local linear cost functions subject to both individual constraints and a linear coupling constraint involving all the decision variables. A key, challenging feature of the considered setup is that some components of the decision variables must assume integer values. The addressed MILPs are NP-hard, nonconvex, and large-scale. Moreover, several additional challenges arise in a distributed framework due to the coupling constraint, so that feasible solutions with guaranteed suboptimality bounds are of interest. We propose a fully distributed algorithm based on a primal decomposition approach and an appropriate tightening of the coupling constraint. The algorithm is guaranteed to provide feasible solutions in finite time. Moreover, asymptotic and finite-time suboptimality bounds are established for the computed solution. Monte Carlo simulations highlight the extremely low suboptimality bounds achieved by the algorithm.
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