Random projections for quadratic programs

Author:

D’Ambrosio Claudia,Liberti LeoORCID,Poirion Pierre-Louis,Vu Ky

Funder

H2020 Marie Sklodowska-Curie Actions

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics,Software

Reference28 articles.

1. Achlioptas, D.: Database-friendly random projections: Johnson–Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66, 671–687 (2003)

2. Ailon, N., Chazelle, B.: Approximate nearest neighbors and fast Johnson–Lindenstrauss lemma. In: Proceedings of the Symposium on the Theory Of Computing, Volume ’06 of STOC. ACM, Seattle (2006)

3. Boutsidis, C., Zouzias, A., Drineas, P.: Random projections for $$k$$-means clustering. In: Advances in Neural Information Processing Systems, NIPS. NIPS Foundation, La Jolla, pp. 298–306 (2010)

4. Dasgupta, S.: Experiments with random projection. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 143–151. Morgan Kaufman, San Francisco (2000)

5. Dorn, W.: Duality in quadratic programming. Q. Appl. Math. 18(2), 155–162 (1960)

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