An improved randomized algorithm with noise level tuning for large-scale noisy unconstrained DFO problems

In this paper, a new randomized solver (called VRDFON) for noisy unconstrained derivative-free optimization (DFO) problems is discussed. Complexity result in the presence of noise for nonconvex functions is 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. Numerical results show that, on the large scale unconstrained CUTEst test problems contaminated by the absolute uniform noise, VRDFON is competitive with state-of-the-art DFO solvers.