We consider the class of nonsmooth regularized optimization problems with equality constraints. Such problems arise in signal processing, machine learning,...

A generalized Nash equilibrium (GNE) framework is introduced for a graphon mean field game of controls (GMFGC). Each agent's drift, cost, and hard contro...

TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values. In h...

This paper addresses the problem of data-driven stabilization of continuous-time linear time-invariant systems. The objective is to design a dynamic output-...

This paper introduces a two-stage stochastic quadratic knapsack problem (TSSQKP) with uncertainty in both profits and weights. To overcome the computational ...

The field of Blackbox Optimization (BBO) lacks publicly available realistic engineering benchmark problems. This work proposes Micro-PRIAD, a collection of...

We introduce Envelopt, a globally convergent iterative framework for a broad class of structured optimization problems where a smooth objective is augmented ...

This work introduces ADS-PB, an extension of the Adaptive Direct Search (ADS) framework for solving constrained blackbox optimization problems. With ADS, it...

This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization. These problems c...

We study the solution of symmetric positive-definite linear systems by way of families of full- and limited-memory methods. Our contributions are threefol...

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...

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

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

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...

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...

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

This paper introduces IPNSDP, a MATLAB solver for general Nonlinear Semidefinite Programming (NSDP) based on a second-order interior point method. Unlike lin...