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Optimal Scaling of a Gradient Method for Distributed Resource Allocation

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  • L. Xiao

    (California Institute of Technology)

  • S. Boyd

    (Stanford University)

Abstract

We consider a class of weighted gradient methods for distributed resource allocation over a network. Each node of the network is associated with a local variable and a convex cost function; the sum of the variables (resources) across the network is fixed. Starting with a feasible allocation, each node updates its local variable in proportion to the differences between the marginal costs of itself and its neighbors. We focus on how to choose the proportional weights on the edges (scaling factors for the gradient method) to make this distributed algorithm converge and on how to make the convergence as fast as possible. We give sufficient conditions on the edge weights for the algorithm to converge monotonically to the optimal solution; these conditions have the form of a linear matrix inequality. We give some simple, explicit methods to choose the weights that satisfy these conditions. We derive a guaranteed convergence rate for the algorithm and find the weights that minimize this rate by solving a semidefinite program. Finally, we extend the main results to problems with general equality constraints and problems with block separable objective function.

Suggested Citation

  • L. Xiao & S. Boyd, 2006. "Optimal Scaling of a Gradient Method for Distributed Resource Allocation," Journal of Optimization Theory and Applications, Springer, vol. 129(3), pages 469-488, June.
    • Handle: RePEc:spr:joptap:v:129:y:2006:i:3:d:10.1007_s10957-006-9080-1
    DOI: 10.1007/s10957-006-9080-1
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    References listed on IDEAS

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    1. G. M. Heal, 1969. "Planning without Prices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 36(3), pages 347-362.
    2. Hurwicz, Leonid, 1973. "The Design of Mechanisms for Resource Allocation," American Economic Review, American Economic Association, vol. 63(2), pages 1-30, May.
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    Cited by:

    1. Flåm, Sjur Didrik, 2015. "Bilateral exchange and competitive equilibrium," Working Papers in Economics 05/15, University of Bergen, Department of Economics.
    2. Sjur Didrik Flåm, 2019. "Blocks of coordinates, stochastic programming, and markets," Computational Management Science, Springer, vol. 16(1), pages 3-16, February.
    3. Ion Necoara & Yurii Nesterov & François Glineur, 2017. "Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks," Journal of Optimization Theory and Applications, Springer, vol. 173(1), pages 227-254, April.
    4. William La Cruz, 2022. "A genetic algorithm with a self-reproduction operator to solve systems of nonlinear equations," Journal of Global Optimization, Springer, vol. 84(4), pages 1005-1032, December.
    5. Huang, Lei & Sun, Wei & Li, Qiyue & Mu, Daoming & Li, Weitao, 2024. "A two-layer energy management for islanded microgrid based on inverse reinforcement learning and distributed ADMM," Energy, Elsevier, vol. 301(C).
    6. Ion Necoara & Andrei Patrascu, 2014. "A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints," Computational Optimization and Applications, Springer, vol. 57(2), pages 307-337, March.
    7. Huang, Lei & Sun, Wei & Li, Qiyue & Li, Weitao, 2023. "Distributed real-time economic dispatch for islanded microgrids with dynamic power demand," Applied Energy, Elsevier, vol. 342(C).
    8. Hua Han & Lang Li & Lina Wang & Mei Su & Yue Zhao & Josep M. Guerrero, 2017. "A Novel Decentralized Economic Operation in Islanded AC Microgrids," Energies, MDPI, vol. 10(6), pages 1-18, June.
    9. Andrea Simonetto & Hadi Jamali-Rad, 2016. "Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 172-197, January.
    10. Yajie Jiang & Siyuan Cheng & Haoze Wang, 2023. "Distributed Integral Convex Optimization-Based Current Control for Power Loss Optimization in Direct Current Microgrids," Energies, MDPI, vol. 16(24), pages 1-17, December.
    11. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.
    12. Wu, Kunming & Li, Qiang & Chen, Ziyu & Lin, Jiayang & Yi, Yongli & Chen, Minyou, 2021. "Distributed optimization method with weighted gradients for economic dispatch problem of multi-microgrid systems," Energy, Elsevier, vol. 222(C).
    13. Sjur Didrik Flåm, 2016. "Noncooperative games, coupling constraints, and partial efficiency," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 4(2), pages 213-229, October.
    14. Hongsheng Liu & Shu Lu, 2019. "Convergence of the augmented decomposition algorithm," Computational Optimization and Applications, Springer, vol. 72(1), pages 179-213, January.

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