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IFIP WG 5.7 International Conference on Advances in Production Management Systems (APMS) - Smart Manufacturing and Logistics Systems - Turning Ideas into Action

作者： Debuchi, Naoto Nishi, Tatsushi Liu, Ziang Okayama Univ Grad Sch Nat Sci & Technol Kita Ku 3-1-1 Tsushima Naka Okayama 7008530 Japan

ISBN: (纸本)9783031164118;9783031164101

With recent liberalization and enlarging of trade among companies, it is necessary to generate an optimal supply chain planning by cooperation and coordination of supply chain planning for multiple companies without sharing sensitive information such as costs and profit among competitive companies. A distributed optimization can solve the optimization problems with limited information. A distributed optimization method using subgradient and consensus control methods has been proposed to solve continuous optimization problems. However, conventional distributed optimization methods using subgradient and consensus control methods cannot be applied to the supply chain planning for multiple companies including 0-1 decision variables. In this paper, we propose a new distributed optimization method for solving the supply chain planning problem for multiple companies by subgradient method and consensus control. By branching the cases 0-1 variables, an optimal solution can be obtained by the enumeration. A method to reduce the computational effort has been developed in the proposed method. From numerical experiments, it is confirmed that we can obtain an optimal solution by the reduction of the computation.

关键词： distributed optimization Supply chain planning Subgradient method Consensus control

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distributed optimization for EVs integrated power system considering flexible ramping requirement

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 160卷

作者： Chen, Wenzhe Sun, Haishun Peng, Chaoyi Zhou, Huafeng Xu, Suyue Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan Peoples R China China Southern Power Grid Dispatching & Control Ct Guangzhou Peoples R China

More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs' dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively.

关键词： Flexible ramping product EV Cloud-edge collaboration distributed optimization

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A Fenchel dual gradient method enabling regularization for nonsmooth distributed optimization over time-varying networks

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optimization METHODS & SOFTWARE 2023年第4期38卷 813-836页

作者： Wu, Xuyang Sou, Kin Cheong Lu, Jie KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung Taiwan ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom AI Shanghai Peoples R China Room 1D-201B SIST Bldg393 Middle Huaxia Middle Rd Shanghai 201210 Peoples R China

In this paper, we develop a regularized Fenchel dual gradient method (RFDGM), which allows nodes in a time-varying undirected network to find a common decision, in a fully distributed fashion, for minimizing the sum of their local objective functions subject to their local constraints. Different from most existing distributed optimization algorithms that also cope with time-varying networks, RFDGM is able to handle problems with general convex objective functions and distinct local constraints, and still has non-asymptotic conver-gence results. Specifically, under a standard network connectivity condition, we show that RFDGM is guaranteed to reach e-accuracy in both optimality and feasibility within O(1/e(2) ln 1/e) iterations. Such iteration complexity can be improved to O(1/e ln 1/e) if the local objective functions are strongly convex but not necessarily differentiable. Finally, simulation results demonstrate the competence of RFDGM in practice.

关键词： distributed optimization decentralized optimization dual optimization method

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distributed optimization of Multiagent Systems Subject to Inequality Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第4期51卷 2232-2241页

作者： Tian, Fangzheng Yu, Wenwu Fu, Junjie Gu, Wei Gu, Juping Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Nantong Univ Dept Elect Engn Nantong 226019 Peoples R China

In this paper, we study a distributed convex optimization problem with inequality constraints. Each agent is associated with its cost function, and can only exchange information with its neighbors. It is assumed that each cost function is convex and the optimization variable is subject to an inequality constraint. The objective is to make all the agents reach consensus, and meanwhile converge to the minimum point of the sum of local cost functions. A distributed protocol is proposed to guarantee that all agents can reach consensus in finite time and converge to the optimal point within the inequality constraints. Based on the ideas of parameter projection, the protocol includes two decent directions. One makes the cost function decrease, and the other makes agents step forward to the constraint set. It is shown that the proposed protocol solves the problem under connected undirected graphs without using a Lagrange multiplier technique. Especially, all of the agents could reach the constraint sets in finite time and stay in there after. The method could also be used in the centralized optimization problems.

关键词： Multi-agent systems Cost function Protocols Convergence Convex functions Heuristic algorithms distributed optimization finite-time consensus inequality constraints multiagent systems parameter projection

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A Stochastic Second-Order Proximal Method for distributed optimization

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 1405-1410页

作者： Qiu, Chenyang Zhu, Shanying Ou, Zichong Lu, Jie Shanghai Tech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Shanghai Jiao Tong Univ Dept Automation Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China ShanghaiTech Univ Shanghai Engn Res Ctr Energy Efficient & Custom AI Shanghai 201210 Peoples R China

We propose a distributed stochastic second-order proximal (St-SoPro) method that enables agents in a network to cooperatively minimize the sum of their local loss functions without any centralized coordination. St-SoPro incorporates a decentralized second-order approximation into an augmented Lagrangian function, and randomly samples the local gradients and Hessian matrices to update, so that it is efficient in solving large-scale problems. We show that for restricted strongly convex and smooth problems, the agents linearly converge in expectation to a neighborhood of the optimum, and the neighborhood can be arbitrarily small under proper parameter settings. Simulations over real machine learning datasets demonstrate that St-SoPro outperforms several state-of-the-art methods in terms of convergence speed as well as computation and communication costs.

关键词： Convergence optimization Approximation algorithms Lagrangian functions Upper bound Stochastic processes Taylor series distributed optimization second-order method stochastic optimization

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Discrete Predefined-Time distributed optimization Algorithm for Resource Allocation Problem in Smart Grids 36

Discrete Predefined-Time Distributed Optimization Algorithm ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Su, Chuxiong Wang, Xiyuan Wang, Shaocong Dai, Hao Chang, Jing Xidian Univ Sch Aerosp Sci & Technol Xian Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The economic dispatch problem in smart grids is studied in this paper. A discrete predefined-time distributed optimization algorithm is proposed based on predefined-time convergence, which can make reasonable decisions on power generation for each generator within a predefined time. In the optimization algorithm designed in this paper, information exchanged between generators in the communication network is only the information of auxiliary variables, which avoids the problem of privacy data leakage. Finally, a set of numerical simulations is provided to verify the feasibility of the proposed algorithm.

关键词： Predefined-time distributed optimization Discrete algorithm Economic dispatch problem Smart grid

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Contractivity of distributed optimization and Nash Seeking Dynamics

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IEEE CONTROL SYSTEMS LETTERS 2023年 7卷 3896-3901页

作者： Gokhale, Anand Davydov, Alexander Bullo, Francesco Univ Calif Santa Barbara Ctr Control Dynam Syst & Computat Santa Barbara CA 93106 USA

In this letter, we study distributed optimization and Nash equilibrium-seeking dynamics from a contraction theoretic perspective. Our first result is a novel bound on the logarithmic norm of saddle matrices. Second, for distributed gradient flows based upon incidence and Laplacian constraints over arbitrary topologies, we establish strong contractivity over an appropriate invariant vector subspace. Third, we give sufficient conditions for strong contractivity in pseudogradient and best response games with complete information, show the equivalence of these conditions, and consider the special case of aggregative games.

关键词： Contraction theory distributed optimization game theory

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Node-based adaptive fractional distributed optimization algorithm 14

Node-based adaptive fractional distributed optimization algo...

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14th Asian Control Conference (ASCC)

作者： Peng, Huaijin Xu, Haoran Yue, Dongdong Wei, Yiheng Southeast Univ Sch Math Nanjing 211189 Peoples R China Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

Fractional distributed optimization can be applied in many fields and is attracting increasing research interest. This paper presents a node-based fractional distributed optimization algorithm, eliminating the need for a priori knowledge of the global Laplace matrix. It is proved the algorithm can converge to the minimum of the problem. In the end, the paper validates the performance of the algorithm through simulations.

关键词： distributed optimization Fractional calculus Nabla fractional system Node-based digraphs

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distributed optimization for nonlinear multi-agent systems with an upper-triangular structure

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INFORMATION SCIENCES 2023年第1期641卷

作者： Meng, Qingtan Ma, Qian Zhou, Guopeng Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Hubei Univ Sci & Technol Inst Engn & Technol Xianning 437100 Peoples R China Hubei Xiangcheng Inst Intelligent Mechatron Xianning 437100 Peoples R China

This paper proposes a new two-step distributed optimization framework with a low-gain parameter for a class of upper-triangular nonlinear multi-agent systems. Specifically, distributed optimal coordinators are first established to generate signals close to the optimal solution by using the real-time values of gradient functions. Then, reference-tracking controllers are designed to make system outputs converge to the generated signals by coordinators. Correspondingly, system outputs can converge to the optimal solution by choosing the appropriate low-gain parameter. Finally, simulation results illustrate the proposed control scheme.

关键词： Upper-triangular nonlinear systems distributed optimization Tracking control Multi-agent systems

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On the Optimal Communication Weights in distributed optimization Algorithms 26

On the Optimal Communication Weights in Distributed Optimiza...

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26th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Colla, Sebastien Hendrickx, Julien M. UCLouvain ICTEAM Inst B-1348 Louvain La Neuve Belgium

We establish that in distributed optimization, the prevalent strategy of minimizing the second-largest eigenvalue modulus (SLEM) of the averaging matrix for selecting communication weights, while optimal for existing theoretical worst-case performance bounds, is generally not optimal regarding the exact worst-case performance of the algorithms. This exact performance can be computed using the Performance Estimation Problem (PEP) approach. We thus rely on PEP to formulate an optimization problem that determines the optimal communication weights for a distributed optimization algorithm deployed on a specified undirected graph. Our results show that the optimal weights can outperform the weights minimizing the second-largest eigenvalue modulus (SLEM) of the averaging matrix. This suggests that the SLEM is not the best characterization of weighted network performance for decentralized optimization. Additionally, we explore and compare alternative heuristics for weight selection in distributed optimization. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： distributed optimization Averaging matrix Second-largest eigenvalue modulus Performance estimation problem Multi-agent systems

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