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New method accelerates eigenvalue dataset generation using Chebyshev subspace filter

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Researchers have developed a new method called the Sorting Chebyshev Subspace Filter (SCSF) to accelerate the generation of eigenvalue datasets for training neural eigenvalue methods. This approach leverages similarities between operators, a factor previously overlooked, to reduce redundant computations. SCSF utilizes fast Fourier transform sorting and a Chebyshev subspace filter to reuse eigenpairs from solved problems, achieving up to a 3.5 times speedup compared to traditional numerical solvers. AI

IMPACT Accelerates data generation for neural eigenvalue methods, potentially reducing training costs and time.

RANK_REASON Academic paper detailing a novel method for accelerating data generation for neural eigenvalue methods.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method accelerates eigenvalue dataset generation using Chebyshev subspace filter

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Academic paper detailing a novel method for accelerating data generation for neural eigenvalue methods.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hong Wang, Jie Wang, Jian Luo, huanshuo dong, Yeqiu Chen, Runmin Jiang, Zhen huang ·

    Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter

    arXiv:2510.23215v2 Announce Type: replace Abstract: Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learning, neural eigenvalue methods have attracted significant attention as a forward pass …

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