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Matrix completion under interval uncertainty

Author

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  • Mareček, Jakub
  • Richtárik, Peter
  • Takáč, Martin

Abstract

Matrix completion under interval uncertainty can be cast as a matrix completion problem with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes on a single personal computer.

Suggested Citation

  • Mareček, Jakub & Richtárik, Peter & Takáč, Martin, 2017. "Matrix completion under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 256(1), pages 35-43.
  • Handle: RePEc:eee:ejores:v:256:y:2017:i:1:p:35-43
    DOI: 10.1016/j.ejor.2016.07.014
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    References listed on IDEAS

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    1. NESTEROV, Yurii, 2012. "Efficiency of coordinate descent methods on huge-scale optimization problems," LIDAM Reprints CORE 2511, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    3. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    4. Jeyakumar, V. & Srisatkunarajah, S., 2009. "Geometric conditions for Kuhn-Tucker sufficiency of global optimality in mathematical programming," European Journal of Operational Research, Elsevier, vol. 194(2), pages 363-367, April.
    5. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    6. Guoyin Li & Alfred Ma & Ting Pong, 2014. "Robust least square semidefinite programming with applications," Computational Optimization and Applications, Springer, vol. 58(2), pages 347-379, June.
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