Consensus Planning with Primal, Dual, and Proximal Agents

References

• Ahuja RK , Magnanti TL , Orlin JB (1993) Network Flows (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
• Boyd S , Parikh N , Chu E , Peleato B , Eckstein J (2011) Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends® in Machine Learning, vol. 3 (Now Publishers, Norwell, MA).Google Scholar
• Deng W , Yin W (2016) On the global and linear convergence of the generalized alternating direction method of multipliers. J. Sci. Comput. 66:889–916.Google Scholar
• Dicker L , Perrault-Joncas D , van Ryzin G (2022) Consensus planning protocol. Technical report, Amazon, Seattle.Google Scholar
• Eckstein J , Bertsekas DP (1992) On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Programming 55:293–318.Google Scholar
• Goldstein T , O’Donoghue B , Setzer S , Baraniuk R (2014) Fast alternating direction optimization methods. SIAM J. Imaging Sci. 7(3):1588–1623.Google Scholar
• He B , Yang H , Wang S (2000) Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities. J. Optim. Theory Appl. 106:337–356.Google Scholar
• He B , Liao LZ , Han D , Yang H (2002) A new inexact alternating directions method for monotone variational inequalities. Math. Programming 92:103–118.Google Scholar
• Hong M , Luo ZQ (2017) On the linear convergence of the alternating direction method of multipliers. Math. Programming 162(1–2):165–199.Google Scholar
• Kadkhodaie M , Christakopoulou K , Sanjabi M , Banerjee A (2015) Accelerated alternating direction method of multipliers. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 497–506.Google Scholar
• Lin Z , Li H , Fang C (2022) Alternating Direction Method of Multipliers for Machine Learning (Springer, New York).Google Scholar
• Lin Z , Liu R , Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. Adv. Neural Inform. Processing Systems , vol. 24 (MIT Press, Cambridge, MA).Google Scholar
• Luenberger DG , Ye Y (2021) Linear and Nonlinear Programming , International Series in Operations Research & Management Science, 5th ed. (Springer Nature, Cham, Switzerland).Google Scholar
• Nedic A , Ozdaglar A (2009) Distributed subgradient methods for multi-agent optimization. IEEE Trans. Automatic Control 54(1):48–61.Google Scholar
• Nesterov YE (1983) A method of solving a convex programming problem with convergence rate o ( 1 / k 2 ) . Doklady Akademii Nauk 269:543–547.Google Scholar
• Niu X , Wei E (2023) FedHybrid: A hybrid federated optimization method for heterogeneous clients. IEEE Trans. Signal Processing 71:150–163.Google Scholar
• O’Donoghue B , Candes E (2015) Adaptive restart for accelerated gradient schemes. Foundations of Computational Mathematics , vol. 15 (Springer-Verlag, Berlin, Heidelberg), 715–732.Google Scholar
• Ouyang H , He N , Tran L , Gray A (2013) Stochastic alternating direction method of multipliers. Dasgupta S, McAllester D, eds. Proc. 30th Internat. Conf. Machine Learn., vol. 28 (PMLR, New York), 80–88.Google Scholar
• Parikh N , Boyd S (2014) Proximal algorithms. Foundations and Trends® in Optimization , vol. 1 (Now Publishers Inc., Hanover, MA), 127–239.Google Scholar
• Ryu EK , Yin W (2022) Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators (Cambridge University Press, Cambridge, UK).Google Scholar
• Sun C , Ye M , Hu G (2020) Distributed optimization for two types of heterogeneous multiagent systems. IEEE Trans. Neural Networks Learn. Systems 32(3):1314–1324.Google Scholar
• Suzuki T (2013) Dual averaging and proximal gradient descent for online alternating direction multiplier method. Dasgupta S, McAllester D, eds. Proc. 30th Internat. Conf. Machine Learn. , vol. 28 (PMLR, New York), 392–400.Google Scholar
• van Ryzin G (2024) Consensus planning protocol (CPP): A market-based approach to coordination at Amazon. 25th ACM Conf. Econom. Comput. (ACM, New York).Google Scholar
• Wang H , Banerjee A (2014) Bregman alternating direction method of multipliers. Adv. Neural Inform. Processing Systems , vol. 27 (MIT Press, Cambridge, MA).Google Scholar
• Wang S , Roosta F , Xu P , Mahoney MW (2018) Giant: Globally improved approximate Newton method for distributed optimization. Inform. Processing Systems , vol. 31 (Curran Associates Inc., Red Hook, NY).Google Scholar
• Yang J , Yuan X (2013) Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization. Math. Comput. 82(281):301–329.Google Scholar
• Yang J , Zhang Y (2011) Alternating direction algorithms for l 1 -problems in compressive sensing. SIAM J. Sci. Comput. 33(1):250–278.Google Scholar
• Yuan K , Ling Q , Yin W (2016) On the convergence of decentralized gradient descent. SIAM J. Optim. 26(3):1835–1854.Google Scholar
• Zhang Y , Lin X (2015) DiSCO: Distributed optimization for self-concordant empirical loss. Proc. 32nd Internat. Conf. Machine Learn., vol. 37 (PMLR, New York), 362–370.Google Scholar
• Zipkin P (2000) Foundations of Inventory Management (McGraw-Hill, New York).Google Scholar