We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when incr...

The present work is in a context of derivative-free optimization involving direct search algorithms guided by surrogate models of the original problem. The...

This is a two-part work. In Part I, low-cost and representative reduced-fidelity models of two versions of the HYDROTEL hydrological model are constructed, u...

Dealing with computationally-intensive calibration processes is still common in distributed hydrological modelling despite the computing power growth. Comput...

In the recent years, the development of new algorithms for multiobjective optimization has considerably grown. A large number of performance indicators has...

The parallel space decomposition of the Mesh Adaptive Direct Search algorithm (PSD-MADS proposed in 2008) is an asynchronous parallel method for constrained ...

Derivative-free optimization (DFO) is the mathematical study of the optimization algorithms that do not use derivatives. One branch of DFO focuses on model-...

The mesh adaptive direct search (MADS) algorithm is designed for blackbox optimization problems for which the functions defining the objective and the constr...

We investigate surrogate-assisted strategies for global derivative-free optimization using the mesh adaptive direct search MADS blackbox optimization algorit...

Despite the lack of theoretical and practical convergence support, the Nelder-Mead (NM) algorithm is widely used to solve unconstrained optimization proble...

The calibration of hydrological models is here formulated as a Blackbox optimization problem where the only information available to the optimization algorit...

The Runge-Kutta class of iterative methods is designed to approximate solutions of a system of ordinary differential equations (ODE). The second-order cla...

Locally weighted regression combines the advantages of polynomial regression and kernel smoothing. We present three ideas for appropriate and effective use...

The Mesh Adaptive Direct Search algorithm (MADS) is an iterative method for constrained blackbox optimization problems. One of the optional MADS features i...

Blackbox optimization problems are often contaminated with numerical noise, and direct search methods such as the Mesh Adaptive Direct Search (MADS) algorit...

The authors investigate the complexity needed in the structure of the scenario trees to maximize energy production in a rolling-horizon framework. Three comp...

We present a new derivative-free trust-region (DFTR) algorithm to solve general nonlinear constrained problems with the use of an augmented Lagrangian m...

We study derivative-free constrained optimization problems and propose a trust-region method that builds linear or quadratic models around the best feasible ...

This paper presents an optimization method to solve the short-term unit commitment and loading problem with uncertain inflows. A scenario tree is built base...

The subdifferential of a function is a generalization for nonsmooth functions of the concept of gradient. It is frequently used in variational analysis, part...