Article
Engineering, Electrical & Electronic
Feng Lin, Weiyu Li, Qing Ling
Summary: This paper addresses the problem of distributed learning under Byzantine attacks and proposes a Byzantine-robust stochastic ADMM method. The effectiveness of the proposed method is demonstrated through theoretical analysis and numerical experiments.
Article
Automation & Control Systems
Ting Bai, Shaoyuan Li, Yuanyuan Zou
Summary: This paper proposes a novel distributed model predictive control scheme based on ADMM to address the control problem of linear systems with changeable network topology. The scheme enables quick response when modifying network topology while maintaining satisfactory dynamic performance.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Energy & Fuels
Yang Hu, Meng Zhang, Kaiyan Wang, DeYi Wang
Summary: This paper proposes a method for solving the optimization problem of wind power and large-scale EVs synergistic access to the power grid based on a distributed framework. By establishing a rolling real-time optimization model and employing an improved ADMM approach, the parallel optimization of each agent is achieved. The effectiveness and feasibility of the method are verified through example simulation.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Electrical & Electronic
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho Sow, Qing Ling
Summary: This paper investigates the problem of minimizing a sum of convex composite functions over a decentralized network. The proposed penalty ADMM method shows sublinear convergence for convex private functions and linear convergence when the smooth parts are strongly convex. Numerical results confirm the theoretical analyses and demonstrate the advantages of PAD over existing state-of-the-art algorithms.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Geochemistry & Geophysics
Yaming Yang, Xingyao Yin, Kun Li, Yongjian Zeng
Summary: In this paper, a robust prestack seismic inversion method for VTI media using quadratic PP-reflectivity approximation is proposed. By deriving quadratic approximations for phase velocity and polarization, the advantage of the second-order terms at a higher contrast interface is analyzed. Then, a quadratic PP-reflectivity approximation is derived from the exact equation based on perturbation theory, significantly improving accuracy even with enhanced anisotropy. Finally, an improved alternating direction method of multipliers (ADMM) algorithm is developed to handle the nonlinearity of the quadratic equations, and the feasibility and stability of the proposed method are tested on two datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Jia Hu, Tiande Guo, Tong Zhao
Summary: This paper introduces a faster stochastic alternating direction method for solving large scale convex composite problems, incorporating a randomization scheme to reduce computational time. The method is shown to be effective in numerical experiments and has unified the stochastic ADMM for solving general convex and strongly convex composite problems.
APPLIED INTELLIGENCE
(2022)
Article
Mathematics, Applied
Fengmiao Bian, Jingwei Liang, Xiaoqun Zhang
Summary: The paper proposes combining ADMM with a class of variance-reduced stochastic gradient estimators for solving large-scale non-convex and non-smooth optimization problems. Global convergence is established under the additional assumption that the object function satisfies Kurdyka-Lojasiewicz property, and numerical experiments are conducted to demonstrate the performance of the proposed methods.
Article
Engineering, Civil
Jun Ma, Zilong Cheng, Xiaoxue Zhang, Masayoshi Tomizuka, Tong Heng Lee
Summary: In this paper, a method using iterative linear quadratic regulator (iLQR) and alternating direction method of multipliers (ADMM) for motion planning in the context of autonomous driving is proposed. The method achieves high computation efficiency under various constraints and enables real-time computation and implementation, providing additional safety to on-road driving tasks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Xuyang Wu, Jie Lu
Summary: This article investigates solving convex composite optimization on an undirected network, where each node is required to minimize the sum of all the component functions throughout the network. A general approximate method of multipliers (AMM) is developed to address this problem by approximating the method of multipliers using a surrogate function. Different distributed realizations of AMM are obtained by designing the surrogate function in various ways. The convergence rates of AMM provide new or stronger convergence results compared to many prior methods.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Chemistry, Physical
Xiaodong Wei, Chao Sun, Qiang Ren, Feikun Zhou, Weiwei Huo, Fengchun Sun
Summary: This paper proposes a method based on the ADMM algorithm to solve the energy management problem for fuel cell vehicles, demonstrating faster computation speed and higher accuracy through simulation results.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Operations Research & Management Science
Sedi Bartz, Ruben Campoy, Hung M. Phan
Summary: This paper proposes and studies an adaptive version of ADMM for the case where the objective function is the sum of a strongly convex function and a weakly convex function. By combining generalized notions of convexity and penalty parameters with the convexity constants of the functions, we prove convergence of the algorithm under natural assumptions.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Dongxia Wang, Yongmei Lei, Jianhui Zhou
Summary: This paper proposes an asynchronous distributed ADMM based on a hybrid parallel model, which combines OpenMP for parallelization within nodes and MPI for message passing between nodes, to improve the scalability of distributed ADMM and reduce system time without compromising convergence or increasing communication cost and memory consumption. Furthermore, efficient parallelized algorithms are designed for solving sub-problems in different applications, resulting in higher scalability and reduced system time compared to state-of-the-art distributed ADMM.
Article
Energy & Fuels
Feixiong Chen, Hongjie Deng, Zhenguo Shao
Summary: This paper proposes a distributed robust synergistic scheduling method for multi-energy hub-based integrated energy systems, aiming to minimize operating costs while taking into account uncertainties in renewable energy generation and multi-energy load demands. The method reformulates the robust scheduling model into a MISOCP form, develops a light robust model to address uncertainties from energy hubs, and utilizes a consensus-based ADMM approach to tackle the scheduling model with limited information exchange. Simulation results demonstrate the effectiveness of the proposed method for optimal synergy of multiple energy hubs with uncertainties.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Mathematics, Applied
Jianchao Bai, Yuxue Ma, Hao Sun, Miao Zhang
Summary: This paper investigates a convex optimization problem with multi-block variables and separable structures. A partial LQP-based ADMM algorithm is proposed, and the convergence and convergence rate are analyzed using a prediction-correction approach.
APPLIED NUMERICAL MATHEMATICS
(2021)
Article
Mathematics
Zhangquan Wang, Shanshan Huo, Xinlong Xiong, Ke Wang, Banteng Liu
Summary: This paper proposes an adaptive parameter selection method based on the ADMM, which decomposes a convex model-fitting problem into a set of sub-problems that can be executed in parallel. The effectiveness of the algorithm is verified through experiments on eight classification datasets, showing improved speed of data processing and increased parallelism.
Article
Engineering, Electrical & Electronic
Shiqian Ma, Necdet Serhat Aybat
PROCEEDINGS OF THE IEEE
(2018)
Article
Automation & Control Systems
Necdet Serhat Aybat, Hesam Ahmadi, Uday V. Shanbhag
Summary: In this article, we discuss a first-order inexact augmented Lagrangian scheme for simultaneously learning the parameter theta* and computing the optimal solution x* in a misspecified optimization problem. Numerical results suggest that this scheme performs well in practice.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Bugra Can, Saeed Soori, Necdet Serhat Aybat, Maryam Mehri Dehnavi, Mert Gurbuzbalaban
Summary: This article focuses on the effective resistance (ER) between nodes in a weighted undirected graph and its application in designing randomized gossiping methods. The research demonstrates that using ER weights can improve the computation and communication efficiency in certain graph structures.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Automation & Control Systems
Erfan Yazdandoost Hamedani, Necdet Serhat Aybat
Summary: In this study, we propose decentralized primal-dual methods for cooperative multiagent consensus optimization problems. It applies to both static and time-varying communication networks and allows only local communication. The study examines convergence rates under the assumption of strong convexity and investigates the impact of underlying network topology on the convergence rates.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Multidisciplinary
Mahmoud Ashour, Jingyao Wang, Necdet Serhat Aybat, Constantino Lagoa, Hao Che
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2019)