Sébastien Le Digabel

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Solving optimization problems in which functions are blackboxes and variables involve different types poses significant theoretical and algorithmic challeng...

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Benchmarking new optimization methods on test problems is essential for assessing their performance and tuning their parameters. Yet, few problems are avail...

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Researchers around the globe attend the International Symposium on Mathematical Programming (ISMP) to share their latest results in mathematics, algorithms, ...

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Multiobjective blackbox optimization deals with problems where the objective and constraint functions are the outputs of a numerical simulation. In this cont...

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This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present differ...

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Heterogeneous datasets emerge in various machine learning or optimization applications that feature different data sources, various data types and complex re...

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This work introduces a novel multi-fidelity blackbox optimization algorithm designed to alleviate the resource-intensive task of evaluating infeasible points...

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A mathematical framework for modelling constrained mixed-variable optimization problems is presented in a blackbox optimization context. The framework intr...

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

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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...

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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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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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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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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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