Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithms on Smooth Functions

Rank-based zeroth-order (ZO) optimization -- which relies only on the ordering of function evaluations -- offers strong robustness to noise and monotone transformations, and underlies many successful algorithms such as CMA-ES, natural evolution strategies, and rank-based genetic algorithms. Despite its widespread use, the theoretical understanding of rank-based ZO methods remains limited: existing analyses provide only asymptotic insights and do not yield explicit convergence rates for algorithms selecting the top-k directions. This work closes this gap by analyzing a simple rank-based ZO algorithm and establishing the first explicit, and non-asymptotic query complexities. For a d-dimension problem, if the function is L-smooth and $μ$-strongly convex, the algorithm achieves O\!(dLμlog\!dL/μδlog\!1/ε) to find an ε-suboptimal solution, and for smooth nonconvex objectives it reaches O\!(dL/εlog\!1/ε). Notation (·) hides constant terms and O(·) hides extra loglog1/ε term. These query complexities hold with a probability at least $1-δ$ with $0<δ<1$. The analysis in this paper is novel and avoids classical drift and information-geometric techniques. Our analysis offers new insight into why rank-based heuristics lead to efficient ZO optimization.