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arXiv · 2007.16024

Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case

Francisco Facchinei·Vyacheskav Kungurtsevb·Lorenzo Lampariello·Gesualdo Scutari
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Abstract

In this paper we first extend the diminishing stepsize method for nonconvex constrained problems presented in [4] to deal with equality constraints and a nonsmooth objective function of composite type. We then consider the particular case in which the constraints are convex and satisfy a standard constraint qualification and show that in this setting the algorithm can be considerably simplified, reducing the computational burden of each iteration.

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Francisco Facchinei, Vyacheskav Kungurtsevb, Lorenzo Lampariello, Gesualdo Scutari. 2020-07-30. Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case. https://arxiv.org/abs/2007.16024

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