Proximal minimization based distributed convex optimization
We provide a novel iterative algorithm for distributed convex optimization over time-varying multi-agent networks, in the presence of heterogeneous agent constraints. We adopt a proximal minimization perspective and show that this set-up allows us to bypass the difficulties of existing algorithms wh...
| Main Authors: | , , , |
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2016
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| Summary: | We provide a novel iterative algorithm for distributed convex optimization over time-varying multi-agent networks, in the presence of heterogeneous agent constraints. We adopt a proximal minimization perspective and show that this set-up allows us to bypass the difficulties of existing algorithms while simplifying the underlying mathematical analysis. At every iteration each agent makes a tentative decision by solving a local optimization program, and then communicates this decision with neighboring agents. We show that following this scheme agents reach consensus on a common decision vector, and in particular that this vector is an optimizer of the centralized problem. |
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