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arXiv · 2508.00495

A linesearch-based derivative-free method for noisy black-box problems

Alberto De Santis·Giampaolo Liuzzi·Stefano Lucidi
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Abstract

In this work we consider unconstrained optimization problems. The objective function is known through a zeroth order stochastic oracle that gives an estimate of the true objective function. To solve these problems, we propose a derivative-free algorithm based on extrapolation techniques. Under reasonable assumptions we are able to prove convergence properties for the proposed algorithms. Furthermore, we also give a worst-case complexity result stating that the total number of iterations where the expected value of the norm of the objective function gradient is above a prefixed $\epsilon>0$ is ${\cal O}(n^2\epsilon^{-2}/\beta^2)$ in the worst case.

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Alberto De Santis, Giampaolo Liuzzi, Stefano Lucidi. 2025-08-01. A linesearch-based derivative-free method for noisy black-box problems. https://arxiv.org/abs/2508.00495

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