FINITE-DIFFRENCE STOCHASTIC SCHEMES FOR MINIMIZING A STRONGLY QUASICONVEX NON-DIFFERENTIABLE FUNCTION ON $\mathbb{R}^n$: THE NURMINSKII METHOD
DOI:
https://doi.org/10.46991/PYSUA.2026.60.1.023Keywords:
optimization, quasi convex function, subgradient, fnite-diference scheme, convergence rateAbstract
In this work, stochastic approximation methods based on finite differences are investigated for the problem of minimizing quasiconvex functions. The main result of this work is the derivation of convergence rate estimates for stochastic finite-difference methods in the case of quasiconvex functions. The obtained results significantly extend the applicability of stochastic finite-difference methods to nonsmooth quasiconvex optimization problems and provide a rigorous justification for their use in black-box settings where the oracle returns only function values.
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