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

Improving neural network optimizer convergence speed is a long-standing priority. Recently, there has been a focus on quasi-Newton optimization methods, whi...

Historically, the training of deep artificial neural networks has relied on parallel computing to achieve practical effectiveness. However, with the increas...

We present a Julia framework dedicated to partially-separable problems whose element function are detected automatically. This framework takes advantage of ...

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

This work proposes the integration of two new constraint-handling approaches into the blackbox constrained multiobjective optimization algorithm DMulti-MADS,...

Engineering design is often faced with uncertainties, making it difficult to determine an optimal design. In an unconstrained context, this amounts to choose...

This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with the Cross-Entropy (CE) method for non smooth constrained optimizat...

We present a modeling of bundle adjustment problems in Julia, as well as a solver for non-linear least square problems (including bundle adjustment problems)...

The context of this research is multiobjective optimization where conflicting objectives are present. In this work, these objectives are only available as th...

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