25 citations · 33 across the 27 of their papers we have counts for
34 papers
Lipschitzian SLLNs for random functions
Lai Tian, Johannes O. Royset
We prove strong laws of large numbers for random functions in the Lipschitz pseudometric. Our results hold under either a topological or a model-theoretic condition, with the latte…
Perturbation Duality for Robust and Distributionally Robust Optimization: Short and General Proofs
Louis L. Chen, Jake Roth, Johannes O. Royset
Duality is a foundational tool in robust and distributionally robust optimization (RO/DRO), underpinning both analytical insights and tractable reformulations. Whereas RO/DRO duali…
Composite Optimization using Local Models and Global Approximations
Welington de Oliveira, Johannes O. Royset
This work presents a unified framework that combines global approximations with locally built models to handle challenging nonconvex and nonsmooth composite optimization problems,…
Optimistic Bilevel Optimization with Composite Lower-Level Problem
Mattia Solla, Johannes O. Royset
This paper introduces a novel double regularization scheme for bilevel optimization problems whose lower-level problem is composite and convex, but not necessarily strongly convex,…
Failure of uniform laws of large numbers for subdifferentials and beyond
Lai Tian, Johannes O. Royset
We provide counterexamples showing that uniform laws of large numbers do not hold for subdifferentials under natural assumptions. Our constructions are univariate random Lipschitz…
Approximating Rockafellians Mitigate Distributional Perturbations: Discontinuous Integrands and Chance-Constrained Applications
Lai Tian, Johannes O. Royset
In this paper, we show how approximating Rockafellians serve as a principled and effective alternative for improving the stability of stochastic programs under distributional chang…
Epi-Consistent Approximation of Stochastic Dynamic Programs
Dominic S. T. Keehan, Johannes O. Royset
We study the consistency of stochastic dynamic programs under converging probability distributions and other approximations. Utilizing results on the epi-convergence of expectation…
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…
Multi-Agent Search for a Moving and Camouflaging Target
Miguel Lejeune, Johannes O. Royset, Wenbo Ma
In multi-agent search planning for a randomly moving and camouflaging target, we examine heterogeneous searchers that differ in terms of their endurance level, travel speed, and de…
Risk-Adaptive Local Decision Rules
Johannes O. Royset, Miguel A. Lejeune
For parameterized mixed-binary optimization problems, we construct local decision rules that prescribe near-optimal courses of action across a set of parameter values. The decision…
Membership Privacy Risks of Sharpness Aware Minimization
Young In Kim, Andrea Agiollo, Pratiksha Agrawal +2
Optimization algorithms that seek flatter minima, such as Sharpness-Aware Minimization (SAM), are credited with improved generalization and robustness to noise. We ask whether such…
A variational approach to a cumulative distribution function estimation problem under stochastic ambiguity
Julio Deride, Johannes O. Royset, Fernanda Urrea
We propose a method for finding a cumulative distribution function (cdf) that minimizes the distance to a given cdf, while belonging to an ambiguity set constructed relative to ano…
Risk-Adaptive Approaches to Stochastic Optimization: A Survey
Johannes O. Royset
Uncertainty is prevalent in engineering design, data-driven problems, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to…
Optimizing Surveillance Satellites for the Synthetic Theater Operations Research Model
Steven M. Warner, Johannes O. Royset
The Synthetic Theater Operations Research Model (STORM) simulates theater-level conflict and requires inputs about utilization of surveillance satellites to search large geographic…
S-BORM: Reliability-based optimization of general systems using buffered optimization and reliability method
Ji-Eun Byun, Welington de Oliveira, Johannes O. Royset
Reliability-based optimization (RBO) is crucial for identifying optimal risk-informed decisions for designing and operating engineering systems. However, its computation remains ch…
Epi-Convergence of Expectation Functions under Varying Measures and Integrands
Eugene A. Feinberg, Pavlo O. Kasyanov, Johannes O. Royset
For expectation functions on metric spaces, we provide sufficient conditions for epi-convergence under varying probability measures and integrands, and examine applications in the…
On Robustness in Nonconvex Optimization with Application to Defense Planning
Johannes O. Royset
In the context of structured nonconvex optimization, we estimate the increase in minimum value for a decision that is robust to parameter perturbations as compared to the value of…
Rockafellian Relaxation and Stochastic Optimization under Perturbations
Johannes O. Royset, Louis L. Chen, Eric Eckstrand
In practice, optimization models are often prone to unavoidable inaccuracies due to dubious assumptions and corrupted data. Traditionally, this placed special emphasis on risk-base…
Consistent Approximations in Composite Optimization
Johannes O. Royset
Approximations of optimization problems arise in computational procedures and sensitivity analysis. The resulting effect on solutions can be significant, with even small approximat…