We study nonconvex distributed optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex) function-the agents' sum-utility-plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to distribute the computation among the agents. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Numerical results show that the new method compares favorably to existing algorithms on both convex and nonconvex problems.

Distributed nonconvex optimization over networks / Di Lorenzo, P; Scutari, G.. - (2015), pp. 229-232. ((Intervento presentato al convegno IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing tenutosi a Cancun [doi:10.1109/CAMSAP.2015.7383778].

Distributed nonconvex optimization over networks

Di Lorenzo P;Scutari G.
2015

Abstract

We study nonconvex distributed optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex) function-the agents' sum-utility-plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to distribute the computation among the agents. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Numerical results show that the new method compares favorably to existing algorithms on both convex and nonconvex problems.
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Distributed optimization; nonconvex optimization; time-varying directed graphs
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Distributed nonconvex optimization over networks / Di Lorenzo, P; Scutari, G.. - (2015), pp. 229-232. ((Intervento presentato al convegno IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing tenutosi a Cancun [doi:10.1109/CAMSAP.2015.7383778].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1163496
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