Inexact and Stochastic Generalized Conditional Gradient with Augmented Lagrangian and Proximal Step

11 May 2020Antonio Silveti-FallsCesare MolinariJalal Fadili

In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in the authors' previous paper, which we denote ICGALP, that allows for errors in the computation of several important quantities. In particular this allows one to compute some gradients, proximal terms, and/or linear minimization oracles in an inexact fashion that facilitates the practical application of the algorithm to computationally intensive settings, e.g. in high (or possibly infinite) dimensional Hilbert spaces commonly found in machine learning problems... (read more)

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