The Exclusion-Inclusion Gap for Evaluating Finite Pareto Approximations in Multiobjective Stochastic Optimization

We introduce the exclusion–inclusion gap for evaluating finite Pareto approximations constructed from stochastic objective estimates. The gap measures the maximum of two objective-space errors associated with excluding Pareto points from the returned set’s true upper image and including dominated points in the returned set’s true lower image. The terms in its approximation–estimation error decomposition admit … Read more

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 … Read more