Before revision: Stochastic zeroth-order optimization concerns problems where only noisy function evaluations are available. Such problems arises frequently in many important applications. In this paper, we consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in an Euclidean space, an important but less studied area, and propose four algorithms for solving this class of problems under different settings.
After revision: We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in an Euclidean space, an important but less studied area, and propose four algorithms for solving this class of problems under different settings.