Randomized Constraints Consensus for Distributed Robust Mixed-Integer Programming
| العنوان: | Randomized Constraints Consensus for Distributed Robust Mixed-Integer Programming |
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| المؤلفون: | Mohammadreza Chamanbaz, Giuseppe Notarstefano, Roland Bouffanais, Francesco Sasso |
| المساهمون: | Chamanbaz M., Notarstefano G., Sasso F., Bouffanais R. |
| المصدر: | IEEE Transactions on Control of Network Systems |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, 0209 industrial biotechnology, Mathematical optimization, Control and Optimization, Optimization problem, Linear programming, Computer Networks and Communications, Computer science, robust optimization, Systems and Control (eess.SY), 02 engineering and technology, Electrical Engineering and Systems Science - Systems and Control, randomized algorithm, Distributed optimization, 020901 industrial engineering & automation, Robustness (computer science), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Mathematics - Optimization and Control, Integer programming, mixed-integer programming, Node (networking), 020206 networking & telecommunications, Computer Science - Distributed, Parallel, and Cluster Computing, Optimization and Control (math.OC), Control and Systems Engineering, Distributed algorithm, Asynchronous communication, Signal Processing, Distributed, Parallel, and Cluster Computing (cs.DC), Wireless sensor network |
| الوصف: | In this paper, we consider a network of processors aiming at cooperatively solving mixed-integer convex programs subject to uncertainty. Each node only knows a common cost function and its local uncertain constraint set. We propose a randomized, distributed algorithm working under asynchronous, unreliable and directed communication. The algorithm is based on a local computation and communication paradigm. At each communication round, nodes perform two updates: (i) a verification in which they check---in a randomized fashion---the robust feasibility of a candidate optimal point, and (ii) an optimization step in which they exchange their candidate basis (the minimal set of constraints defining a solution) with neighbors and locally solve an optimization problem. As main result, we show that processors can stop the algorithm after a finite number of communication rounds (either because verification has been successful for a sufficient number of rounds or because a given threshold has been reached), so that candidate optimal solutions are consensual. The common solution is proven to be---with high confidence---feasible and hence optimal for the entire set of uncertainty except a subset having an arbitrary small probability measure. We show the effectiveness of the proposed distributed algorithm using two examples: a random, uncertain mixed-integer linear program and a distributed localization in wireless sensor networks. The distributed algorithm is implemented on a multi-core platform in which the nodes communicate asynchronously. Comment: Submitted for publication. arXiv admin note: text overlap with arXiv:1706.00488 |
| وصف الملف: | STAMPA |
| تدمد: | 2372-2533 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63112d26051af71dcc520284900e6303 https://doi.org/10.1109/tcns.2020.3024483 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....63112d26051af71dcc520284900e6303 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23722533 |
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