VRDFON — line search in noisy unconstrained derivative-free optimization

In this paper, a new randomized solver (called VRDFON) for noisy unconstrained derivative-free optimization problems is discussed. Complexity bounds in the presence of noise for nonconvex, convex, and strongly convex functions are studied. Two effective ingredients of VRDFON are an improved derivative-free line search algorithm with many heuristic enhancements and quadratic models in adaptively determined subspaces. VRDFON is more robust and efficient than other state-of-the-art solvers, especially for medium and high dimensions.

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