Noisy Pairwise-Comparison Random Search for Smooth Nonconvex Optimization

We consider minimizing high-dimensional smooth nonconvex objectives using only noisy pairwise comparisons. Unlike classical zeroth-order methods limited by the ambient dimension $d$, we propose Noisy-Comparison Random Search (NCRS), a direct-search method that exploits random line search to adapt to the intrinsic dimension $k\le d$. We establish novel non-convex convergence guarantees for approximate stationarity: under a uniform-margin oracle, NCRS attains $\epsilon$-stationarity with complexity $\mathcal{O}(k\mathrm{/}(p^2{\epsilon }^2))$, explicitly replacing ambient dependence with the intrinsic dimension. Furthermore, we introduce a general tie-aware noise model where comparison quality degrades near ties; for this setting, we prove that a majority-vote variant of NCRS achieves $\epsilon$-stationarity with complexity $\mathcal{O}(k^2\mathrm{/}{\epsilon }^4)$.