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arXiv · 1303.6680

Optimal scaling of the ADMM algorithm for distributed quadratic programming

André Teixeira·Euhanna Ghadimi·Iman Shames·Henrik Sandberg·Mikael Johansson
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Abstract

This paper presents optimal scaling of the alternating directions method of multipliers (ADMM) algorithm for a class of distributed quadratic programming problems. The scaling corresponds to the ADMM step-size and relaxation parameter, as well as the edge-weights of the underlying communication graph. We optimize these parameters to yield the smallest convergence factor of the algorithm. Explicit expressions are derived for the step-size and relaxation parameter, as well as for the corresponding convergence factor. Numerical simulations justify our results and highlight the benefits of optimally scaling the ADMM algorithm.

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André Teixeira, Euhanna Ghadimi, Iman Shames, Henrik Sandberg, Mikael Johansson. 2013-03-26. Optimal scaling of the ADMM algorithm for distributed quadratic programming. https://doi.org/10.1109/cdc.2013.6760977

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