We consider a contextual stochastic optimization (CSO) problem, where one has observations of the uncertain parameters together with concurrent observations of covariates, and the goal is to choose decisions that minimize expected cost conditioned on new covariate observations. The empirical residuals-based sample average approximation (ER-SAA) of the CSO problem constructs scenarios of uncertainty by combining … Read more
Our project investigates replacing the classical adversarial discriminator in GAN training with a kernel-based distance metric, namely Kernel Stein Discrepancy (KSD). We assess whether a kernelized objective can improve training stability and efficiency without compromising sample quality, and we evaluate accelerated Nystrom approximations with Renyi landmark selection on CIFAR-10. ArticleDownload View PDF
Primal heuristics for finding high-quality feasible solutions are an important component in mixed-integer optimization (MIO) solvers. Recent advances in GPU-accelerated optimization algorithms show the potential of GPU acceleration for continuous optimization. In this paper, we introduce the Parallel Node Generation and Exploration Algorithm (PaNGEA), a GPU-friendly MIO primal heuristic. PaNGEA explores restricted subproblems by combining … Read more
We study passenger routing in an aviation network that blends hydrogen‐ and kerosene‐powered aircraft. Under our assumptions, hydrogen enables carbon‐free short‐ and medium‐haul flights but requires capital‐intensive supply facilities, which lead to varying prices and availabilities of hydrogen at specific airports, creating strong interdependencies between routing, technology choice, and infrastructure availability. To capture these trade‐offs, … Read more
We present a polynomial-time algorithm for optimally coloring perfect graphs that is based entirely on graph-theoretic operations. At its core, the algorithm decides whether a perfect graph contains a clique of a given size by iteratively counting walks in the graph with certain weights assigned to its edges and nonedges. These weights are initialized according … Read more
Tracking targets in undersea warfare requires successful detection by an active search asset. Maximizing detection likelihood requires strategic placement and routing of the search assets in the search region over the planning horizon. We develop a two-stage stochastic integer programming model that maximizes the expected total reward for target detections under uncertainty in target motion … Read more
This research introduces the Stochastic Sequential Queens Elimination Problem, where on the \(n\)-queens board, each activated queen simultaneously attempts to eliminate all queens in her unblocked neighborhood, each independently succeeding with probability \(p\). The objective is to minimize the expected cumulative conflict count over the trajectory. This research proposes a Markov decision process for this … Read more
This paper develops an adaptive scenario partitioning approach for stochastic bilevel linear programs. The method extends the Adaptive Partitioning Method, originally designed for two-stage stochastic programs, to settings in which a leader makes a first-stage decision while anticipating scenario dependent optimal responses from a follower. The proposed approach solves a sequence of aggregated master problems … Read more
This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization. These problems can inherently imply high computational costs due to the possible large number of variables and multi-modality of the search space. In addition, the potential time-intensive nature and time heterogeneity of the blackboxes defining the … Read more
In this paper, we develop a direction adaptation evolution strategy (DAES)—a new MAES-type method—for noisy derivative-free optimization, designed to reconcile the population-based search mechanisms of evolution strategies with rigorous complexity analysis. Unlike standard MAES schemes, DAES fixes the adaptation matrix to the identity and replaces matrix adaptation with a structured direction-generation mechanism based on symmetric … Read more