Interval many-objective optimization problems (IMaOPs) involve more than three conflicting
objectives with interval parameters. Various real-world applications under uncertainty can …

This work highlights an approach for incorporating realistic uncertainties into scientific
computing workflows based on finite elements, focusing on prevalent applications in …

We consider minimizing finite-sum and expectation objective functions via Hessian-
averaging based subsampled Newton methods. These methods allow for gradient …

The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for
finite element discretizations. MFEM supports numerous types of finite element methods and …

In this paper, we present a novel derivative-free optimization framework for solving
unconstrained stochastic optimization problems. Many problems in fields ranging from …

In this work, we consider solving optimization problems with a stochastic objective and
deterministic equality constraints. We propose a Trust-Region Sequential Quadratic …

This paper explores the application of Retrieval-Augmented Generation (RAG) in
engineering design, examining its potential to revolutionize the design process through …

In this paper we focus on nonconvex optimization problems with expectation constraints. To
address the challenges posed by possibly nonconvex constraints and the stochastic nature …

In this paper, we consider a family of linearly constrained convex minimization problems
whose objective function is not necessarily smooth. A basic doubleproximal augmented …