Distributed constrained optimization and consensus in uncertain networks via proximal minimization
We provide a unifying framework for distributed convex optimization over time-varying networks, in the presence of constraints and uncertainty, features that are typically treated separately in the literature. We adopt a proximal minimization perspective and show that this set-up allows us to bypass...
| Main Authors: | , , , |
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| Format: | Journal article |
| Published: |
IEEE
2017
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