June 11, 2024, 4:46 a.m. | Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05938v1 Announce Type: new
Abstract: Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with high precision. Existing methods either involve matrix decomposition or use the preconditioned conjugate gradient method. For relatively large instances, these methods cannot achieve the real-time requirement unless there is an effective precondition.
Recently, graph neural networks (GNNs) opened new possibilities for QP. Some promising empirical studies of applying GNNs for QP tasks …

abstract applications arxiv cs.lg gradient graph graph neural networks instances integer math.oc matrix mixed networks neural networks power precision programming real-time solutions type

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