We propose a novel parallel essentially cyclic asynchronous algorithm for the minimization of the sum of a smooth (nonconvex) function and a convex (nonsmooth) regularizer. The framework hinges on Successive Convex Approximation (SCA) techniques and on a new global model that describes many asynchronous environments in a more faithful and exhaustive way with respect to state-of-the-art models. A key feature of the model is the update of (block) variables according to the essential cyclic rule: an integer B exists such that, every B iterations, all (block) variables are updated at least once. The algorithm is theoretically guaranteed to achieve a sublinear convergence rate and near linear speedup with respect to the number of cores. Asymptotic convergence to stationary solutions is also proved. Numerical results show that our scheme compares favorably to existing asynchronous methods.

Essentially cyclic asynchronous nonconvex large-scale optimization / Cannelli, L.; Facchinei, F.; Kungurtsev, V.; Scutari, G.. - STAMPA. - (2017), pp. 1-5. (Intervento presentato al convegno 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 tenutosi a Sapporo, Japan nel 2017) [10.1109/SPAWC.2017.8227767].

Essentially cyclic asynchronous nonconvex large-scale optimization

Facchinei, F.
;
2017

Abstract

We propose a novel parallel essentially cyclic asynchronous algorithm for the minimization of the sum of a smooth (nonconvex) function and a convex (nonsmooth) regularizer. The framework hinges on Successive Convex Approximation (SCA) techniques and on a new global model that describes many asynchronous environments in a more faithful and exhaustive way with respect to state-of-the-art models. A key feature of the model is the update of (block) variables according to the essential cyclic rule: an integer B exists such that, every B iterations, all (block) variables are updated at least once. The algorithm is theoretically guaranteed to achieve a sublinear convergence rate and near linear speedup with respect to the number of cores. Asymptotic convergence to stationary solutions is also proved. Numerical results show that our scheme compares favorably to existing asynchronous methods.
2017
18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Asynchronous algorithms; Big-data; Inconsistent read; Linear speedup; Nonconvex constrained optimization
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Essentially cyclic asynchronous nonconvex large-scale optimization / Cannelli, L.; Facchinei, F.; Kungurtsev, V.; Scutari, G.. - STAMPA. - (2017), pp. 1-5. (Intervento presentato al convegno 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 tenutosi a Sapporo, Japan nel 2017) [10.1109/SPAWC.2017.8227767].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1100979
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