This work introduces StoMADS, a stochastic variant of the mesh adaptive direct-search (MADS) algorithm originally developed for deterministic blackbox optim...

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning pr...

Computational speed and global optimality are a key need for pratical algorithms of the OPF problem. Recently, we proposed a tight-and-cheap conic relaxation...

In demand-response programs, aggregators balance the needs of generation companies and end-users. This work proposes a two-phase framework that shaves the ag...

Smart homes have the potential to achieve efficient energy consumption: households can profit from appropriately scheduled consumption. By 2020, 35% of all h...

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

The optimal reactive power dispatch (ORPD) problem is an alternating current optimal power flow (ACOPF) problem where discrete control devices for regulating...

Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. It is expected that 35% of households in North America an...

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

We consider the maximum $k$-cut problem that involves partitioning the vertex set of a graph into $k$ subsets such that the sum of the weights of the...

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...

The classical alternating current optimal power flow problem is highly nonconvex and generally hard to solve. Convex relaxations, in particular semidefinite,...

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...

Les problèmes d'optimisation de boîtes noires sont souvent contaminés par du bruit numérique, et les méthodes de recherche directe telles que l'algorithme de...

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 ...

An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usual...

Nous considérons le problème de la $k$-coupe maximale qui consiste à partitionner l'ensemble des sommets d'un graphe en $k$ sous-ensembles tels que l...