We study distributed processing of subspace-constrained signals in multi-agent networks with sparse connectivity. We introduce the first optimization framework based on distributed subspace projections, aimed at minimizing a network cost function depending on the specific processing task, while imposing subspace constraints on the final solution. The proposed method hinges on (sub)gradient techniques while leveraging distributed projections as a mechanism to enforce subspace constraints in a cooperative and distributed fashion. Asymptotic convergence to optimal solutions of the problem is established under different assumptions (e.g., nondifferentiability, nonconvexity, etc.) on the objective function. Finally, numerical tests assess the performance of the proposed distributed strategy.

Distributed signal recovery based on in-network subspace projections / Di Lorenzo, P.; Barbarossa, S.; Sardellitti, S.. - 2019(2019), pp. 5242-5246. ((Intervento presentato al convegno 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 tenutosi a Brighton; United Kingdom [10.1109/ICASSP.2019.8682719].

Distributed signal recovery based on in-network subspace projections

Di Lorenzo P.;Barbarossa S.;Sardellitti S.
2019

Abstract

We study distributed processing of subspace-constrained signals in multi-agent networks with sparse connectivity. We introduce the first optimization framework based on distributed subspace projections, aimed at minimizing a network cost function depending on the specific processing task, while imposing subspace constraints on the final solution. The proposed method hinges on (sub)gradient techniques while leveraging distributed projections as a mechanism to enforce subspace constraints in a cooperative and distributed fashion. Asymptotic convergence to optimal solutions of the problem is established under different assumptions (e.g., nondifferentiability, nonconvexity, etc.) on the objective function. Finally, numerical tests assess the performance of the proposed distributed strategy.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1392820
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