A low-rank spectral method for learning Markov models-Reference-Cited by-同舟云学术

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A low-rank spectral method for learning Markov models

Published:2022-05-05 Issue:1 Volume:17 Page:143-162
ISSN:1862-4472
Container-title:Optimization Letters
language:en
Short-container-title:Optim Lett

Author:

Bi Shujun,Yin Zhen,Weng Yihong

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research of Guangzhou

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Business, Management and Accounting (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s11590-022-01882-w.pdf

Reference28 articles.

1. Azé, D.: A survey on error bounds for lower semicontinuous functions. Esaim Proc. 13, 1–17 (2003)

2. Benczúr, A.A., Csalogány, K., Sarlós, T.: On the feasibility of low-rank approximation for personalized pagerank. In: Special Interest Tracks and Posters of the 14th international conference on World Wide Web, pp. 972–973 (2005)

3. Benson, A.R., Gleich, D.F., Lim, L.-H.: The spacey random walk: a stochastic process for higher-order data. SIAM Rev. 59, 321–345 (2017)

4. Bi, S.J., Pan, S.H.: Error bounds for rank constrained optimization problems and applications. Oper. Res. Lett. 44, 336–341 (2016)

5. Burke, J.V., Ferris, M.C.: Weak sharp minima in mathematical programming. SIAM J. Control Optim. 31, 1340–1359 (1993)

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