9 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, identifying Pareto optimal solutions that capture relevant trade-offs may not be straightforward. Popular choices include…
Why is soccer so popular: Understanding underdog achievement and randomness in team ball sports
Luis Nunes Vicente, Thaksheel Alleck, Tommaso Giovannelli +2
In this paper, we examine team ball sports to investigate how the likelihood of weaker teams winning against stronger ones, referred to as underdog achievement, is influenced by in…
The limitation of neural nets for approximation and optimization
Tommaso Giovannelli, Oumaima Sohab, Luis Nunes Vicente
We are interested in assessing the use of neural networks as surrogate models to approximate and minimize objective functions in optimization problems. While neural networks are wi…
Full-Low Evaluation Methods For Bound and Linearly Constrained Derivative-Free Optimization
Clément W. Royer, Oumaima Sohab, Luis Nunes Vicente
Derivative-free optimization (DFO) consists in finding the best value of an objective function without relying on derivatives. To tackle such problems, one may build approximate de…