Zobrazeno 1 - 10
of 1 181
pro vyhledávání: '"Blackbox optimization"'
Bistable mechanical systems exhibit two stable configurations where the elastic energy is locally minimized. To realize such systems, origami techniques have been proposed as a versatile platform to design deployable structures with both compact and
Externí odkaz:
http://arxiv.org/abs/2408.15147
Multi-Objective Optimization (MOO) is an important problem in real-world applications. However, for a non-trivial problem, no single solution exists that can optimize all the objectives simultaneously. In a typical MOO problem, the goal is to find a
Externí odkaz:
http://arxiv.org/abs/2408.09976
Autor:
Andrés-Thió, Nicolau, Audet, Charles, Diago, Miguel, Gheribi, Aimen E., Digabel, Sébastien Le, Lebeuf, Xavier, Garneau, Mathieu Lemyre, Tribes, Christophe
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.
Externí odkaz:
http://arxiv.org/abs/2406.00140
Akademický článek
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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 evaluat
Externí odkaz:
http://arxiv.org/abs/2312.13128
Akademický článek
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This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to unknown uncertainties. To handle mixed aleatory/epistemic uncertainties, the problem is tran
Externí odkaz:
http://arxiv.org/abs/2310.11380
This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic opt
Externí odkaz:
http://arxiv.org/abs/2305.19450
PyXAB -- A Python Library for $\mathcal{X}$-Armed Bandit and Online Blackbox Optimization Algorithms
We introduce a Python open-source library for $\mathcal{X}$-armed bandit and online blackbox optimization named PyXAB. PyXAB contains the implementations for more than 10 $\mathcal{X}$-armed bandit algorithms, such as HOO, StoSOO, HCT, and the most r
Externí odkaz:
http://arxiv.org/abs/2303.04030
Publikováno v:
AIMS Mathematics, Vol 8, Iss 11, Pp 25922-25956 (2023)
This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic opt
Externí odkaz:
https://doaj.org/article/117750536a634907927c03bb83eca901
