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Some Modified Matrix Eigenvalue Problems
This is the title of well-known numerical linear algebra survey article by Gene Golub published in 1973 [1]. The article covers a range of matrix eigenvalue problems which require some manipulations before the standard algorithms may be used. I am
using the same title to consider a new set of modified matrix eigenvalue problems. This includes constrained and bi-level optimizations arising from algorithms for fairness in machine learning, such as spectral clustering with group fairness [2] and fair
principal component analysis [3]. We also consider eigenvalue optimization via 2D eigenvalue problem with applications to the calculation of the distance to instability among others [4], and stationary values of a quadratic form subject to
non-homogeneous linear constraints for applications such as image segmentation with constraints [5]. I will discuss how to explore the underlying structures of these problems to turn them into our familiar eigenvalue problems and algorithms. This
talk is based on joint work with Ian Davidson, Aaron Davis, Ren-Cang Li, Ding Lu, Tianyi Lu, Junhui Shen, Yangfeng Su, Ji Wang, and Yunshen Zhou.
References
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[1] G. H. Golub, Some modified matrix eigenvalue problems, SIAM Review, 15(2), pp.318–334, 1973.
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[2] J. Wang, D. Lu, I. Davidson and Z. Bai, Scalable spectral clustering with group fairness constraints, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics
(AISTAT), PMLR 206:6613-6629, 2023.
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[3] J. Shen, A. Davis, D. Lu and Z. Bai Fair and efficient: hidden convexity of fair PCA and fast solution via eigenvalue optimization, submitted, 2024.
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[4] T. Lu, Y. Su and Z. Bai, Variational characterization and Rayleigh quotient iteration of 2D eigenvalue problem with Applications, SIAM J. Matrix. Anal. Appl. Vol.45, No.3, pp.1455-1468, 2024
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[5] Y. Zhou, Z. Bai and R.-C. Li, Linear constrained Rayleigh quotient optimization: theory and algorithms, CSIAM Trans. Appl. Math., 2(2), pp.195-262, 2021