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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| _version_ | 1826303622543048704 |
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| author | Margellos, K Falsone, A Garatti, S Prandini, M |
| author_facet | Margellos, K Falsone, A Garatti, S Prandini, M |
| author_sort | Margellos, K |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T06:05:31Z |
| format | Conference item |
| id | oxford-uuid:edb48317-e54a-4e3a-9fcf-bca181e9e582 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:05:31Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:edb48317-e54a-4e3a-9fcf-bca181e9e5822022-03-27T11:27:02ZProximal minimization based distributed convex optimizationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:edb48317-e54a-4e3a-9fcf-bca181e9e582Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2016Margellos, KFalsone, AGaratti, SPrandini, MWe 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. |
| spellingShingle | Margellos, K Falsone, A Garatti, S Prandini, M Proximal minimization based distributed convex optimization |
| title | Proximal minimization based distributed convex optimization |
| title_full | Proximal minimization based distributed convex optimization |
| title_fullStr | Proximal minimization based distributed convex optimization |
| title_full_unstemmed | Proximal minimization based distributed convex optimization |
| title_short | Proximal minimization based distributed convex optimization |
| title_sort | proximal minimization based distributed convex optimization |
| work_keys_str_mv | AT margellosk proximalminimizationbaseddistributedconvexoptimization AT falsonea proximalminimizationbaseddistributedconvexoptimization AT garattis proximalminimizationbaseddistributedconvexoptimization AT prandinim proximalminimizationbaseddistributedconvexoptimization |