Sébastien Le Digabel

solar: A solar thermal power plant simulator for blackbox optimization benchmarking

  • Sébastien Le Digabel
  • Charles Audet
  • Xavier Lebeuf
  • Miguel Diago
  • Aïmen E. Gheribi
  • Christophe Tribes
    • Categories Constrained Nonlinear Optimization, Energy, Optimization Software Benchmark Tags , , ,

    This work introduces solar, a collection of  ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including hidden constraints)  differ across instances. Some are deterministic, others are stochastic … Read more

    A graph-structured distance for mixed-variable domains with meta variables

  • Edward Hallé-Hannan
  • Charles Audet
  • Youssef Diouane
  • Sébastien Le Digabel
  • Paul Saves
    • Categories Data Science Theory, Optimization in Data Science Tags , , ,

    Heterogeneous datasets emerge in various machine learning and optimization applications that feature different input sources, types or formats. Most models or methods do not natively tackle heterogeneity. Hence, such datasets are often partitioned into smaller and simpler ones, which may limit the generalizability or performance, especially if data is limited. The first main contribution of … Read more

    Fidelity and interruption control for expensive constrained multi-fidelity blackbox optimization

  • Xavier Lebeuf
  • Sébastien Le Digabel
  • Charles Audet
  • Miguel Diago
  • Stéphane Alarie
    • Categories Constrained Nonlinear Optimization, Energy, Optimization of Simulated Systems Tags , , , , ,

    This work introduces a novel blackbox optimization algorithm for computationally expensive constrained multi-fidelity problems. When applying a direct search method to such problems, the scarcity of feasible points may lead to numerous costly evaluations spent on infeasible points. Our proposed fidelity and interruption controlled optimization algorithm addresses this issue by leveraging multi-fidelity information, allowing for … Read more

    A general mathematical framework for constrained mixed-variable blackbox optimization problems with meta and categorical variables

  • Charles Audet
  • Edward Hallé-Hannan
  • Sébastien Le Digabel
    • Categories Nonlinear Optimization Tags , , , ,

    A mathematical framework for modelling constrained mixed-variable optimization problems is presented in a blackbox optimization context. The framework introduces a new notation and allows solution strategies. The notation framework allows meta and categorical variables to be explicitly and efficiently modelled, which facilitates the solution of such problems. The new term meta variables is used to … Read more

    Handling of constraints in multiobjective blackbox optimization

  • Jean Bigeon
  • Sébastien Le Digabel
  • Ludovic Salomon
    • Categories Multi-Criteria Optimization, Nonlinear Optimization Tags , , , ,

    This work proposes the integration of two new constraint-handling approaches into the blackbox constrained multiobjective optimization algorithm DMulti-MADS, an extension of the Mesh Adaptive Direct Search (MADS) algorithm for single-objective constrained optimization. The constraints are aggregated into a single constraint violation function which is used either in a two-phase approach, where research of a feasible … Read more

    Hierarchically constrained blackbox optimization

  • Stéphane Alarie
  • Charles Audet
  • Paulin Jacquot
  • Sébastien Le Digabel
    • Categories Nonlinear Optimization Tags , ,

    In blackbox optimization, evaluation of the objective and constraint functions is time consuming. In some situations, constraint values may be evaluated independently or sequentially. The present work proposes and compares two strategies to define a hierarchical ordering of the constraints and to interrupt the evaluation process at a trial point when it is detected that … Read more

    Quantifying uncertainty with ensembles of surrogates for blackbox optimization

  • Charles Audet
  • Sébastien Le Digabel
  • Renaud Saltet
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , , , ,

    This work is in the context of blackbox optimization where the functions defining the problem are expensive to evaluate and where no derivatives are available. A tried and tested technique is to build surrogates of the objective and the constraints in order to conduct the optimization at a cheaper computational cost. This work proposes different … Read more

    NOMAD version 4: Nonlinear optimization with the MADS algorithm

  • Charles Audet
  • Sébastien Le Digabel
  • Viviane Rochon Montplaisir
  • Christophe Tribes
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems Tags , , ,

    NOMAD is software for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic features published in scientific publications. These features are motivated by real applications encountered by industrial partners. The latest major release of NOMAD, version 3, dates from 2008. Minor releases are produced as new features … Read more

    Use of static surrogates in hyperparameter optimization

  • Dounia Lakhmiri
  • Sébastien Le Digabel
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems Tags , ,

    Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in the development of any new application. This consuming process can benefit from the elaboration of strategies designed to quickly discard low quality configurations and focus on more promising candidates. This work aims at enhancing HyperNOMAD, a library that adapts … Read more

    Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates

  • Kwassi Joseph Dzahini
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Approaches Tags , , ,

    This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally developed for deterministic blackbox optimization under general constraints. The values of the objective and constraint functions are provided by a noisy blackbox, i.e., they can only be computed with random noise whose … Read more