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

This work studies constrained blackbox optimization problems that cannot be solved in reasonable time due to prohibitive computational costs. This challenge...

Benchmarking new optimization methods on test problems is essential for assessing their performance and tuning their parameters. Yet, few problems are avail...

This paper presents a transmission expansion planning framework that couples Benders decomposition with an operational layer based on a semidefinite programm...

This work presents a case study where four well-known derivative-free solvers are benchmarked on several instances based on the \(\textsf{solar}\) suite of...

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

The present work studies the problem of sorting using comparisons involving three elements at a time. Each comparison only identifies the smallest, middle, ...

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

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

Researchers around the globe attend the International Symposium on Mathematical Programming (ISMP) to share their latest results in mathematics, algorithms, ...

Bistable mechanical systems exhibit two stable configurations where the elastic energy is locally minimized. To realize such systems, origami techniques ha...

This work introduces a _partitioned optimization framework_ (POf) to ease the solving process for optimization problems for which fixing some variables to a...

This paper introduces a new step to the Direct Search Method (DSM) to strengthen its convergence analysis. By design, this so-called covering step may e...

This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present differ...

Heterogeneous datasets emerge in various machine learning or optimization applications that feature different data sources, various data types and complex re...

The cosine measure was introduced in 2003 to quantify the richness of a finite positive spanning sets of directions in the context of derivative-free direc...

This work introduces a novel multi-fidelity blackbox optimization algorithm designed to alleviate the resource-intensive task of evaluating infeasible points...

This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to...

This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise...