| Autor: |
Alimisis, Foivos, Vary, Simon, Vandereycken, Bart |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
We develop an accelerated gradient descent algorithm on the Grassmann manifold to compute the subspace spanned by a number of leading eigenvectors of a symmetric positive semi-definite matrix. This has a constant cost per iteration and a provable iteration complexity of $\tilde{\mathcal{O}}(1/\sqrt{\delta})$, where $\delta$ is the spectral gap and $\tilde{\mathcal{O}}$ hides logarithmic factors. This improves over the $\tilde{\mathcal{O}}(1/\delta)$ complexity achieved by subspace iteration and standard gradient descent, in cases that the spectral gap is tiny. It also matches the iteration complexity of the Lanczos method that has however a growing cost per iteration. On the theoretical part, we rely on the formulation of Riemannian accelerated gradient descent by [26] and new characterizations of the geodesic convexity of the symmetric eigenvalue problem by [8]. On the empirical part, we test our algorithm in synthetic and real matrices and compare with other popular methods. |
| Databáze: |
arXiv |
| Externí odkaz: |
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