A note on the convergence of deterministic gradient sampling in nonsmooth optimization

Bennet Gebken ()
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Bennet Gebken: Paderborn University

Computational Optimization and Applications, 2024, vol. 88, issue 1, No 5, 165 pages

Abstract: Abstract Approximation of subdifferentials is one of the main tasks when computing descent directions for nonsmooth optimization problems. In this article, we propose a bisection method for weakly lower semismooth functions which is able to compute new subgradients that improve a given approximation in case a direction with insufficient descent was computed. Combined with a recently proposed deterministic gradient sampling approach, this yields a deterministic and provably convergent way to approximate subdifferentials for computing descent directions.

Keywords: Nonsmooth optimization; Nonsmooth analysis; Nonconvex optimization; Gradient sampling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10589-024-00552-0

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