We present a GPU implementation of Algorithm NCL, an augmented Lagrangian method for solving large-scale and degenerate nonlinear programs. Although interio...

Two families of directional direct search methods have emerged in derivative-free and blackbox optimization (DFO and BBO), each based on distinct principles:...

In this work, we propose improved task mapping strategies for real-time electric power system simulations on heterogeneous computing clusters, considering bo...

Modern power distribution networks (DNs) increasingly incorporate active distribution network technologies, such as distributed energy resources (DERs) and r...

Surrogate modeling with insufficient data can lead to high prediction uncertainty and errors. A promising remedy to address this issue is the use of transfer...

Large-scale distributed wireless networks provide infrastructure-free and cost-effective connectivity, supporting applications from disaster recovery to glob...

Solving optimization problems in which functions are blackboxes and variables involve different types poses significant theoretical and algorithmic challeng...

The growing reliance on power electronics introduces new challenges requiring detailed time-domain analyses with fast and accurate circuit simulation tools...

Benchmarking is essential for assessing the effectiveness of optimization algorithms. This is especially true in derivative-free optimization, where target ...

Algorithm NCL was devised to solve a class of large nonlinearly constrained optimization problems whose constraints do not satisfy LICQ at a solution. It ...

Local search methods start from a feasible solution and improve it by successive minor modifications until a solution that cannot be further improved is enco...

We propose a multi-precision extension of the Quadratic Regularization (R2) algorithm that enables it to take advantage of low-precision computations, and by...

In this paper, we address the problem of online estimation of spherical features in the field of camera for robotics. Specifically, we consider a mobile robo...

Integrating Optical Transport Networks (OTNs) into multilayer Elastic Optical Networks (EONs) enhances data transmission efficiency but introduces significan...

For sequences of networks embedded in the unit cube \([0, 1]^m\), (weak) measure limits of sequences of empirical measures of vertex densities (vertexon...

We explore a scaled spectral preconditioner for the efficient solution of sequences of symmetric and positive-definite linear systems. We design the scaled...

Penalty methods are a well known class of algorithms for constrained optimization. They transform a constrained problem into a sequence of unconstrained _pe...

The solution of the graphon mean field game equation (GMFG) is characterized by an HJB equation and an FPK equation coupled via a graphon mean field. An a...

This paper introduces new model parameterizations for learning dynamical systems from data via the Koopman operator, and studies their properties. Whereas mo...

The modelling of linear quadratic Gaussian optimal control problems on large complex networks is intractable computationally. Graphon theory provides an appr...