The almost supermartingale convergence theorem of Robbins and Siegmund (1971) is a fundamental tool for establishing the convergence of various stochastic it...

Optical transport networks (OTNs) carry Ethernet traffic over lightpaths that can be provisioned using elastic optical networks (EONs) with flexible spectrum...

This paper investigates mean field games and control on sparse networks. In the case of large expander graphs, the limit topologies are analyzed using the gr...

Accurate estimation of metal accumulation in electric arc furnaces (EAFs) is critical for maintaining stable ferrosilicon (FeSi) production and ensuring effe...

The design of tactical wireless networks reflects a complex interplay among structural constraints, technological choices, and underlying modeling assumption...

Tactical wireless networks play a vital role in ensuring reliable connectivity in scenarios where conventional telecommunications infrastructure is unavailab...

Bilevel optimization involves an upper-level and a lower-level decision maker. The lower-level optimization problem is nested within the constraints of the ...

In this paper, we study a finitely repeated Prisoner's Dilemma in which players are asymmetric in both their temptation to deviate from cooperation and the...

Wind power generation represents one of the main resources of modern renewable power systems. Its deployment is governed by numerous factors, e.g., weather p...

We study the solution of large symmetric positive-definite linear systems in a matrix-free setting with a limited iteration budget. We focus on the precondi...

The \(k\)-means algorithm is one of the most widely used methods for solving the minimum sum-of-squares clustering (MSSC) problem, but it is well known to ...

Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there ha...

In simulation-based engineering, design choices are often obtained following the optimization of complex blackbox models. These models frequently involve mi...

The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called cold start problem by using source data to aid in th...

We consider the security-constrained optimal power flow (SCOPF) problem in a linearized form where thermal line limits are enforced as soft constraints to re...

Predicting the state of the weather, from tracking hurricanes and thunderstorms to assessing daily temperatures, is of crucial importance. Over the past deca...

As fast-charging demand grows, ensuring the operational resilience of electric vehicle (EV) infrastructure under congestion becomes a key challenge in large-...

It is well known that physical activity plays a key role in maintaining good health. Over the past few decades, there has been a significant increase in both...

Dynamic games are considered with large subpopulations distributed over large sparse graphs, where each agent has mean field coupling with all agents withi...

Semi-supervised clustering is a learning approach that primarily relies on unlabeled data but incorporates some prior information to improve the clustering r...