Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 67, Issue 6, Pages 3165-3171Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3095456
Keywords
Optimization; Linear programming; Games; Target tracking; Standards; Nash equilibrium; Sensors; Aggregative optimization; distributed algorithm; linear convergence rate; multi-agent networks; strongly convex function
Funding
- Ministry of Education, Singapore [AcRF TIER 1-2019-T1-001-088 (RG72/19)]
- National Natural Science Foundation of China [62003243]
- Shanghai Municipal Commission of Science and Technology [19511132101]
- Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100]
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This article proposes a new framework called distributed aggregative optimization, which allows local objective functions to be dependent on the sum of functions of decision variables of all the agents. To solve this problem, a distributed algorithm called distributed aggregative gradient tracking is proposed and analyzed. It is shown that the algorithm can converge at a linear rate to the optimal variable. A numerical example is provided to validate the theoretical result.
This article proposes a new framework for distributed optimization, called distributed aggregative optimization, which allows local objective functions to be dependent not only on their own decision variables, but also on the sum of functions of decision variables of all the agents. To handle this problem, a distributed algorithm, called distributed aggregative gradient tracking, is proposed and analyzed, where the global objective function is strongly convex, and the communication graph is balanced and strongly connected. It is shown that the algorithm can converge to the optimal variable at a linear rate. A numerical example is provided to corroborate the theoretical result.
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