# Sébastien Le Digabel

Back## Cahiers du GERAD

### 56 results — page 1 of 3

In blackbox optimization, evaluation of the objective and constraint functions is time consuming. In some situations, constraint values may be evaluated in...

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This work is in the context of blackbox optimization where the functions defining the problem are expensive to evaluate and where no derivatives are availabl...

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NOMAD is software for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic...

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Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in the development of any new application. This consuming p...

BibTeX referenceConstrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates

This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MAD...

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This work reviews blackbox optimization applications over the last twenty years, addressed using direct search optimization methods. Emphasis is placed on...

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The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destinati...

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

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Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

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This work considers the graph partitioning problem known as maximum *k*-cut. It focuses on investigating features of a branch-and-bound method to efficiently...

In derivative-free and blackbox optimization, the objective function is often evaluated through the execution of a computer program seen as a blackbox. It ...

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

BibTeX referenceHyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search

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...

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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...

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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...

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

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

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

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

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

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