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- W3013932788 abstract "Common approaches in distributed optimization build upon the consensus framework to enforce cooperation and consistency among the nodes. In many applications however, the inter-node relationships are better modeled via antagonistic or dissensual constraints. These relationships can generally be incorporated via non-convex constraints or penalty functions, and the resulting formulations are flexible enough to subsume a wide variety of classification and discrimination problems. This work develops a general-purpose ADMM algorithm for distributed optimization with dissensus constraints. The formulation is generalized to incorporate both consensus and dissensus relationships. The non-convex constraints are handled via appropriate first-order approximations. The proposed algorithm is tested on the discriminative dictionary learning problem, where the goal is to learn class-specific dictionaries usable for both reconstruction and discrimination tasks. Extensive tests over human activity recognition dataset demonstrate the efficacy of the proposed approach." @default.
- W3013932788 created "2020-04-03" @default.
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- W3013932788 date "2019-11-01" @default.
- W3013932788 modified "2023-09-27" @default.
- W3013932788 title "Network Dissensus via Distributed ADMM" @default.
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- W3013932788 doi "https://doi.org/10.1109/ieeeconf44664.2019.9048905" @default.
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