Abstract
This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems (RAP) where the objective functions are generally convex. With the help of projection operators, a primal-dual framework, and Nesterov’s accelerated method, we first design a distributed accelerated primal-dual projection neurodynamic approach (DAPDP), and its convergence rate of the primal-dual gap is \(O\left( {{1 \over {{t^2}}}} \right)\) by selecting appropriate parameters and initial values. Then, when the local closed convex sets are convex inequalities which have no closed-form solutions of their projection operators, we further propose a distributed accelerated penalty primal-dual neurodynamic approach (DAPPD) on the strength of the penalty method, primal-dual framework, and Nesterov’s accelerated method. Based on the above analysis, we prove that DAPPD also has a convergence rate \(O\left( {{1 \over {{t^2}}}} \right)\) of the primal-dual gap. Compared with the distributed dynamical approaches based on the classical primal-dual framework, our proposed distributed accelerated neurodynamic approaches have faster convergence rates. Numerical simulations demonstrate that our proposed neurodynamic approaches are feasible and effective.
Similar content being viewed by others
References
Boyd S. Distributed optimization and statistical learning via the alternating direction method of multipliers. FNT Machine Learn, 2010, 3: 1–122
Li C, Dong Z, Chen G, et al. Data-driven planning of electric vehicle charging infrastructure: A case study of Sydney, Australia. IEEE Trans Smart Grid, 2021, 12: 3289–3304
Mateos G, Giannakis G B. Distributed recursive least-squares: Stability and performance analysis. IEEE Trans Signal Process, 2012, 60: 3740–3754
Liu C B, Ma Y H, Yin H, et al. Human resource allocation for multiple scientific research projects via improved pigeon-inspired optimization algorithm. Sci China Tech Sci, 2021, 64: 139–147
Fu Z, Yu W W, Lu J H, et al. A distributed normalized Nash equilibrium seeking algorithm for power allocation among micro-grids. Sci China Tech Sci, 2021, 64: 341–352
Chen G, Guo Z. Initialization-free distributed fixed-time convergent algorithms for optimal resource allocation. IEEE Trans Syst Man Cybern Syst, 2022, 52: 845–854
Jia W, Liu N, Qin S. An adaptive continuous-time algorithm for nonsmooth convex resource allocation optimization. IEEE Trans Automat Contr, 2022, 67: 6038–6044
Li C, Yu X, Yu W, et al. Distributed event-triggered scheme for economic dispatch in smart grids. IEEE Trans Ind Inf, 2016, 12: 1775–1785
Li C, Yu X, Yu W, et al. Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid, 2017, 8: 250–261
Xu J, He X, Han X, et al. A two-layer distributed algorithm using neurodynamic system for solving L1-minimization. IEEE Trans Circuits Syst II, 2022, 69: 3490–3494
Shi Y, Li L, Yang J, et al. Center-based Transfer Feature Learning With Classifier Adaptation for surface defect recognition. Mech Syst Signal Processing, 2023, 188: 110001
Yalcinoz T, Short M J. Neural networks approach for solving economic dispatch problem with transmission capacity constraints. IEEE Trans Power Syst, 1998, 13: 307–313
Wang G, Bian Q, Xin H, et al. A robust reserve scheduling method considering asymmetrical wind power distribution. IEEE CAA J Autom Sin, 2018, 5: 961–967
Beck A, Nedic A, Ozdaglar A, et al. An \(O\left( {{1 \over k}} \right)\) gradient method for network resource allocation problems. IEEE Trans Control Netw Syst, 2014, 1: 64–73
Wang K, Fu Z, Xu Q, et al. Distributed fixed step-size algorithm for dynamic economic dispatch with power flow limits. Sci China Inf Sci, 2021, 64: 112202
Yang T, Lu J, Wu D, et al. A distributed algorithm for economic dispatch over time-varying directed networks with delays. IEEE Trans Ind Electron, 2017, 64: 5095–5106
Chen W, Li T. Distributed economic dispatch for energy internet based on multiagent consensus control. IEEE Trans Automat Contr, 2021, 66: 137–152
He X, Zhao Y, Huang T. Optimizing the dynamic economic dispatch problem by the distributed consensus-based ADMM approach. IEEE Trans Ind Inf, 2020, 16: 3210–3221
Yu W, Li C, Yu X, et al. Economic power dispatch in smart grids: a framework for distributed optimization and consensus dynamics. Sci China Inf Sci, 2018, 61: 1–16
Yao Y Y, Tian F Z, Mei F, et al. Dynamical economic dispatch using distributed barrier function-based optimization algorithm. Sci China Tech Sci, 2019, 62: 2104–2112
Chen R J, Yang T, Chai T Y. Distributed accelerated optimization algorithms: Insights from an ODE. Sci China Tech Sci, 2020, 63: 1647–1655
Li C, Yu X, Huang T, et al. Distributed optimal consensus over resource allocation network and its application to dynamical economic dispatch. IEEE Trans Neural Netw Learn Syst, 2018, 29: 2407–2418
He X, Ho D W C, Huang T, et al. Second-order continuous-time algorithms for economic power dispatch in smart grids. IEEE Trans Syst Man Cybern Syst, 2018, 48: 1482–1492
Deng Z, Liang S, Yu W. Distributed optimal resource allocation of second-order multiagent systems. Int J Robust Nonlinear Control, 2018, 28: 4246–4260
Wang D, Wang Z, Wen C, et al. Second-order continuous-time algorithm for optimal resource allocation in power systems. IEEE Trans Ind Inf, 2019, 15: 626–637
Zeng X, Yi P, Hong Y, et al. Distributed continuous-time algorithms for nonsmooth extended monotropic optimization problems. SIAM J Control Optim, 2018, 56: 3973–3993
Le X, Chen S, Li F, et al. Distributed neurodynamic optimization for energy internet management. IEEE Trans Syst Man Cybern Syst, 2019, 49: 1624–1633
Li K, Liu Q, Yang S, et al. Cooperative optimization of dual multiagent system for optimal resource allocation. IEEE Trans Syst Man Cybern Syst, 2020, 50: 4676–4687
Liang S, Zeng X, Chen G, et al. Distributed sub-optimal resource allocation via a projected form of singular perturbation. Automatica, 2020, 121: 109180
Xu Y, Luo D, Duan H, Distributed planar formation maneuvering of leader-follower networked systems via a barycentric coordinate-based approach.
Pei Y Q, Gu H B, Liu K X, et al. An overview on the designs of distributed observers in LTI multi-agent systems. Sci China Tech Sci, 2021, 64: 2337–2346
Huang C D, Cao J D. Comparative study on bifurcation control methods in a fractional-order delayed predator-prey system. Sci China Tech Sci, 2019, 62: 298–307
Pshenichnyi B N. One method of solving the convex programming problem. Cybernetics, 1981, 16: 510–520
Nesterov Y. Introductory Lectures on Convex Optimization: A Basic Course. New York: Springer Science & Business Media, 2003
Su W, Boyd S, Candes E. A differential equation for modeling Nesterov’s accelerated gradient method: Theory and insights. Adv Neural Inf Process Syst, 2014, 27: 2510–2518
Wibisono A, Wilson A C, Jordan M I. A variational perspective on accelerated methods in optimization. Proc Natl Acad Sci USA, 2016, 113: E7351–E7358
Kolarijani A S, Esfahani P M, Keviczky T. Continuous-time accelerated methods via a hybrid control lens. IEEE Trans Automat Contr, 2020, 65: 3425–3440
Attouch H, Chbani Z, Peypouquet J, et al. Fast convergence of inertial dynamics and algorithms with asymptotic vanishing viscosity. Math Program, 2018, 168: 123–175
Zeng X, Lei J, Chen J. Dynamical primal-dual nesterov accelerated method and its application to network optimization. IEEE Trans Automat Contr, 2023, 68: 1760–1767
Jiang X, Qin S, Xue X, et al. A second-order accelerated neurodynamic approach for distributed convex optimization. Neural Networks, 2022, 146: 161–173
Boţ R I, Csetnek E R. Second order forward-backward dynamical systems for monotone inclusion problems. SIAM J Control Optim, 2016, 54: 1423–1443
He X, Hu R, Fang Y P. Convergence rates of inertial primal-dual dynamical methods for separable convex optimization problems. SIAM J Control Optim, 2021, 59: 3278–3301
Boţ R I, Nguyen D K. Improved convergence rates and trajectory convergence for primal-dual dynamical systems with vanishing damping. J Differ Equ, 2021, 303: 369–406
Parikh N. Proximal algorithms. FNT Optim, 2014, 1: 127–239
Zhao Y, Liao X, He X, et al. Centralized and collective neurodynamic optimization approaches for sparse signal reconstruction via L1-minimization. IEEE Trans Neural Netw Learn Syst, 2022, 33: 7488–7501
Kia S S. Distributed optimal resource allocation over networked systems and use of an e-exact penalty function. IFAC-PapersOnLine, 2016, 49: 13–18
Pinar M Ç, Zenios S A. On smoothing exact penalty functions for convex constrained optimization. SIAM J Optim, 1994, 4: 486–511
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Natural Science Foundation of China (Grant No. 62176218), and the Fundamental Research Funds for the Central Universities (Grant No. XDJK2020TY003).
Rights and permissions
About this article
Cite this article
Zhao, Y., He, X., Yu, J. et al. Distributed accelerated primal-dual neurodynamic approaches for resource allocation problem. Sci. China Technol. Sci. 66, 3639–3650 (2023). https://doi.org/10.1007/s11431-022-2161-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-022-2161-4