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Mixed Integer Linear Programs (MILPs) are highly flexible and powerful tools for modeling and solving complex real-world combinatorial optimization problems. Recently, machine learning (ML)-guided approaches have demonstrated significant potential in
Externí odkaz:
http://arxiv.org/abs/2412.14409
Mixed-Integer Programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have shown potential to speed up MIP solving via offline training that then gui
Externí odkaz:
http://arxiv.org/abs/2412.14382
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method. Prior work has shown the existence of MILP backdoors, small sets of variables such that prioritizing branching o
Externí odkaz:
http://arxiv.org/abs/2401.10467
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned constraints. However, solving these f
Externí odkaz:
http://arxiv.org/abs/2401.03451
Autor:
Cai, Junyang, Nguyen, Khai-Nguyen, Shrestha, Nishant, Good, Aidan, Tu, Ruisen, Yu, Xin, Zhe, Shandian, Serra, Thiago
One surprising trait of neural networks is the extent to which their connections can be pruned with little to no effect on accuracy. But when we cross a critical level of parameter sparsity, pruning any further leads to a sudden drop in accuracy. Thi
Externí odkaz:
http://arxiv.org/abs/2301.07966
Akademický článek
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Autor:
Cai, Junyang, Zhou, Jian
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena November 2022 164
Publikováno v:
Proceedings of SPIE; April 2023, Vol. 12464 Issue: 1 p124641B-124641B-7, 1121777p
Akademický článek
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Autor:
Wang, Weiyan, Li, Shangjian, Qiang, Qian, Wu, Kui, Pan, Xiaoli, Su, Wentao, Cai, Junyang, Shen, Zhigang, Yang, Yunquan, Li, Changzhi, Zhang, Tao
Publikováno v:
Angewandte Chemie International Edition; 8/19/2024, Vol. 63 Issue 34, p1-1, 1p