# Sébastien Le Digabel

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### 55 results — page 1 of 3

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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...

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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...

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

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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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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...

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