25 citations · 33 across the 27 of their papers we have counts for
6 papers · 1 filter
Variational Analysis of a Nonconvex and Nonsmooth Optimization Problem: An Introduction
Johannes O. Royset
Variational analysis provides the theoretical foundations and practical tools for constructing optimization algorithms without being restricted to smooth or convex problems. We sur…
On Stability in Optimistic Bilevel Optimization
Johannes O. Royset
Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we construct a lifted formulation that exhibits…
An implementable proximal-type method for computing critical points to minimization problems with a nonsmooth and nonconvex constraint
Gregorio M. Sempere, Welington de Oliveira, Johannes O. Royset
This work proposes an implementable proximal-type method for a broad class of optimization problems involving nonsmooth and nonconvex objective and constraint functions. In contras…
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation
Louis L. Chen, Bobbie Chern, Eric Eckstrand +2
Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling, noisy labeling, and weak labeling (i.e., image classification). Although neural ne…
Rockafellian Relaxation for PDE-Constrained Optimization with Distributional Uncertainty
Harbir Antil, Sean P. Carney, Hugo Díaz +1
Stochastic optimization problems are generally known to be ill-conditioned to the form of the underlying uncertainty. A framework is introduced for optimal control problems with pa…
Approximations of Rockafellians, Lagrangians, and Dual Functions
Julio Deride, Johannes O. Royset
Solutions of an optimization problem are sensitive to changes caused by approximations or parametric perturbations, especially in the nonconvex setting. This paper shows that solut…