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Article
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Summary: This paper presents a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes. The algorithm consists of two components: a dynamic gradient tracking scheme for finding local solution estimates and a recursive least squares scheme for estimating unknown parameters. The algorithm exhibits a bounded regret under suitable assumptions. A numerical example is provided to corroborate the theoretical analysis.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
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Summary: This article discusses a time-varying optimization problem in a network of systems, where each system is shared by and affects a group of individuals. The objective is to minimize cost functions associated with individual preferences, which are unknown, subject to time-varying constraints that represent physical or operational limits of the network. The article presents a distributed online optimization algorithm that learns cost functions based on users' feedback and uses measurements from the network to bypass the need for sensing unknown inputs. The performance of the algorithm is analyzed in terms of dynamic network regret and constraint violation, with numerical examples provided in the context of real-time optimization of distributed energy resources.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
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Article
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Summary: 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.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
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Article
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Summary: This paper focuses on an aggregative optimization problem under the communication bottleneck. The goal is to solve the problem through distributed computation and local efficient communication without a central coordinator. A novel distributed quantized algorithm, called D-QAGT, is developed to track the optimal variables with finite bits communication. The algorithm achieves the exact optimal solution with linear convergence rate.
Article
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Summary: This article investigates distributed online convex optimization over a multiagent network without any global/central coordinators, where an aggregative variable is present. The proposed online distributed gradient tracking algorithm (O-DGT) provides upper bounds for the dynamic regret and is also analyzed with stochastic/noisy gradients. This article is the first to study the presence of an aggregative variable in convex optimization and includes a numerical experiment to validate the theoretical results.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Automation & Control Systems
Giuseppe Belgioioso et al.
Summary: The algorithm is the first fully distributed one designed to seek generalized Nash equilibrium in aggregative games on a time-varying communication network under partial-decision information. It integrates dynamic tracking into a projected pseudo-gradient algorithm, and the convergence analysis relies on the framework of monotone operator splitting and the Krasnosel'skii-Mann fixed-point iteration with errors.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
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Summary: This paper presents an online algorithm for solving an optimization problem with time-varying costs and unknown functions, utilizing Gaussian processes to learn the unknown cost function and design time-varying optimization tools. The algorithm aims to track the optimal trajectory while learning user satisfaction, with consideration for limited computational budgets or real-time implementation.
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