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1. Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions.

Author

Davis, Damek and Drusvyatskiy, Dmitriy

Subjects
*ALGORITHMS
Abstract

We introduce a geometrically transparent strict saddle property for nonsmooth functions. This property guarantees that simple proximal algorithms on weakly convex problems converge only to local minimizers, when randomly initialized. We argue that the strict saddle property may be a realistic assumption in applications, since it provably holds for generic semi-algebraic optimization problems. [ABSTRACT FROM AUTHOR]

Published
2022
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