Definition
Black-Box Optimization refers to methods that optimize an objective function without access to its internal structure or gradients — treating the system purely as an input-output oracle. This is especially relevant when targeting proprietary models exposed only through APIs, where the underlying weights and computations are completely inaccessible, forcing reliance on techniques such as evolutionary search, random perturbation, or zeroth-order methods that infer progress solely from output signals.