Orthogonal Constrained Neural Networks for Solving Structured Inverse Eigenvalue Problems
<|reference_start|>Orthogonal Constrained Neural Networks for Solving Structured Inverse Eigenvalue Problems: This paper introduces a novel neural network for efficiently solving Structured Inverse Eigenvalue Problems (SIEPs). The main contributions lie in two aspects: firstly, a unified framework is proposed that can handle various SIEPs instances. Particularly, an innovative method for handling nonnegativity constraints is devised using the ReLU function. Secondly, a novel neural network based on multilayer perceptrons, utilizing the Stiefel layer, is designed to efficiently solve SIEP. By incorporating the Stiefel layer through matrix orthogonal decomposition, the orthogonality of similarity transformations is ensured, leading to accurate solutions for SIEPs. Hence, we name this new network Stiefel Multilayer Perceptron (SMLP). Furthermore, SMLP is an unsupervised learning approach with a lightweight structure that is easy to train. Several numerical tests from literature and engineering domains demonstrate the efficiency of SMLP.<|reference_end|>
@article{zhang2024orthogonal,
    title={Orthogonal Constrained Neural Networks for Solving Structured Inverse
  Eigenvalue Problems},
    author={Shuai Zhang, Xuelian Jiang, Hao Qian, Yingxiang Xu},
    journal={arXiv preprint arXiv:2406.19981},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19981},
    primaryClass={math.NA cs.NA}
}