The alternating direction multiplier method (ADMM) was originally devised as an iterative method for solving convex minimization problems by means of parallelization, and was recently used for distributed processing. This letter proposes a modification of state-of-the-art ADMM formulations in order to obtain a scalable version, well suited for a wide range of applications such as cooperative localization and smart grid optimizations. The resulting algorithm is distributed and scalable, it assures fast convergence speed and robustness to errors. Its performance is tested with an application example based upon cooperative localization.
A Distributed and Scalable Processing Method Based Upon ADMM
ERSEGHE, TOMASO
2012
Abstract
The alternating direction multiplier method (ADMM) was originally devised as an iterative method for solving convex minimization problems by means of parallelization, and was recently used for distributed processing. This letter proposes a modification of state-of-the-art ADMM formulations in order to obtain a scalable version, well suited for a wide range of applications such as cooperative localization and smart grid optimizations. The resulting algorithm is distributed and scalable, it assures fast convergence speed and robustness to errors. Its performance is tested with an application example based upon cooperative localization.
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