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pro vyhledávání: '"Klamkin, Michael"'
This paper introduces Dual Interior Point Learning (DIPL) and Dual Supergradient Learning (DSL) to learn dual feasible solutions to parametric linear programs with bounded variables, which are pervasive across many industries. DIPL mimics a novel dua
Externí odkaz:
http://arxiv.org/abs/2402.02596
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their
Externí odkaz:
http://arxiv.org/abs/2208.07497
Autor:
Barry, Neil, Chatzos, Minas, Chen, Wenbo, Han, Dahye, Huang, Chaofan, Joseph, Roshan, Klamkin, Michael, Park, Seonho, Tanneau, Mathieu, Van Hentenryck, Pascal, Wang, Shangkun, Zhang, Hanyu, Zhao, Haoruo
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations. Indeed, the increased stochasticity in load and the volatility of
Externí odkaz:
http://arxiv.org/abs/2204.00950
Publikováno v:
In Electric Power Systems Research October 2024 235
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17b72a196a97a5ec215cfeaa7c7cbce6
http://arxiv.org/abs/2208.07497
http://arxiv.org/abs/2208.07497