7 papers
Non-monotone direct-search methods for deterministic and stochastic derivative-free optimization
Anjie Ding, Trang H. Tran, Luis Nunes Vicente
In derivative-free optimization (DFO), one minimizes functions for which the gradient is unavailable or expensive to compute. In many applications, objective function values and gr…
Stochastic set-valued optimization and its application to robust learning
Tommaso Giovannelli, Jingfu Tan, Luis Nunes Vicente
In this paper, we develop a stochastic set-valued optimization (SVO) framework tailored for robust machine learning. In the SVO setting, each decision variable is mapped to a set o…
Sequential test sampling for stochastic derivative-free optimization
Anjie Ding, Francesco Rinaldi, Luis Nunes Vicente
In many derivative-free optimization algorithms, a sufficient decrease condition decides whether to accept a trial step in each iteration. This condition typically requires that th…
Non-smooth stochastic gradient descent using smoothing functions
Tommaso Giovannelli, Jingfu Tan, Luis Nunes Vicente
In this paper, we address stochastic optimization problems involving a composition of a non-smooth outer function and a smooth inner function, a formulation frequently encountered…
A stochastic gradient method for trilevel optimization
Tommaso Giovannelli, Griffin Dean Kent, Luis Nunes Vicente
With the success that the field of bilevel optimization has seen in recent years, similar methodologies have started being applied to solving more difficult applications that arise…
Pareto sensitivity, most-changing sub-fronts, and knee solutions
Tommaso Giovannelli, Marcos Medeiros Raimundo, Luis Nunes Vicente
When dealing with a multi-objective optimization problem, obtaining a comprehensive representation of the set of Pareto optimal solutions can be computationally expensive. However,…