Jean Bigeon
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Risk averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties
This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to...
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Improving neural network optimizer convergence speed is a long-standing priority. Recently, there has been a focus on quasi-Newton optimization methods, whi...
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Historically, the training of deep artificial neural networks has relied on parallel computing to achieve practical effectiveness. However, with the increas...
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We present a Julia framework dedicated to partially-separable problems whose element function are detected automatically. This framework takes advantage of ...
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This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise...
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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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Engineering design is often faced with uncertainties, making it difficult to determine an optimal design. In an unconstrained context, this amounts to choose...
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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...
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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)...
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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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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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