FINITE-DIFFERENCE STOCHASTIC SCHEMES FOR MINIMIZING A STRONGLY QUASICONVEX NON-DIFFERENTIABLE FUNCTION ON ℝn. THE NURMINSKII METHOD
DOI:
https://doi.org/10.46991/PYSUA.2026.60.1.023Keywords:
optimization, quasi convex function, subgradient, finite-difference 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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