A developed randomized algorithm with noise level tuning for large-scale noisy unconstrained DFO problems and its real-life applications

In this paper, a new randomized solver (called VRDFON) for noisy unconstrained derivative-free optimization problems is discussed. Complexity results 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 … Read more