Machine learning and optimization – Optimization Online

Robust Out-of-Distribution Stochastic Optimization with Heterogeneous Inclusive and Exclusive Distribution Clusters

Published: 2026/09/10, Updated: 2026/09/18

Biao Han

Decision scenarios far from rare in practice often place data-driven decision-making in an awkward position, where the decision maker may have access to neither the target distribution itself nor any empirical samples drawn from it, especially when decisions must be made in novel or highly uncertain environments. In response, robust out-of-distribution stochastic optimization (RooDSO) has … Read more

Stable and Unstable Singularities in Navier-Stokes ?

Published: 2026/09/06

Orson Mengara

Nonlinear Optimization, Robust Optimization euler equations, Machine learning and optimization, Navier-Stokes equation

This document provides an extended and rigorous framework dedicated to the geometric analysis of the 3D incompressible Navier-Stokes equations \cite{ESS2003}. We comprehensively develop geometric proofs related to decay estimates, blow-up profiles, and the foundational partial regularity theory of Caffarelli, Kohn, and Nirenberg (CKN). We examine in detail the Hausdorff dimension of potential singular sets, local … Read more

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers

Published: 2026/04/12

I. Esra Buyuktahtakin Toy

Data Science Algorithms, Optimization in Data Science, Stochastic Programming decision making under uncertainty, Decision-focused learning, deep learning, learning-to-optimize, Machine learning and optimization, markov decision processes, multi-stage stochastic programming, operations research, optimization under uncertainty, reinforcement learning, sequential decision making, stochastic optimization

Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management sciences (OR/MS), which have long offered conceptual and methodological foundations for sequential decision-making under uncertainty. At the same time, recent advances in deep learning, including feedforward neural … Read more