3 research outputs found
Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization
This paper introduces a dual-regularized ADMM approach to distributed,
time-varying optimization. The proposed algorithm is designed in a
prediction-correction framework, in which the computing nodes predict the
future local costs based on past observations, and exploit this information to
solve the time-varying problem more effectively. In order to guarantee linear
convergence of the algorithm, a regularization is applied to the dual, yielding
a dual-regularized ADMM. We analyze the convergence properties of the
time-varying algorithm, as well as the regularization error of the
dual-regularized ADMM. Numerical results show that in time-varying settings,
despite the regularization error, the performance of the dual-regularized ADMM
can outperform inexact gradient-based methods, as well as exact dual
decomposition techniques, in terms of asymptotical error and consensus
constraint violation.Comment: Presented at Asilomar Conference on Signals, Systems, and Computers
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