Accelerated anti-lopsided algorithm for nonnegative least squares

Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

Bro, R., De Jong, S.: A fast non-negativity-constrained least squares algorithm. J. Chemom. 11(5), 393–401 (1997)

Caramanis, L., Jo, S.J.: Ee 381v: large scale optimization fall 2012. http://users.ece.utexas.edu/~cmcaram/EE381V_2012F/Lecture_4_Scribe_Notes.final.pdf (2012)

Cevher, V., Becker, S., Schmidt, M.: Convex optimization for big data: scalable, randomized, and parallel algorithms for big data analytics. Sig. Process. Mag. IEEE 31(5), 32–43 (2014)

Chen, D., Plemmons, R.J.: Nonnegativity constraints in numerical analysis. In: Symposium on the Birth of Numerical Analysis, pp. 109–140 (2009)

Dax, A.: On computational aspects of bounded linear least squares problems. ACM Trans. Math. Softw. (TOMS) 17(1), 64–73 (1991)

Franc, V., Hlaváč, V., Navara, M.: Sequential coordinate-wise algorithm for the non-negative least squares problem. In: Gagalowicz, A., Philips, W (eds.) Proceedings of the 11th International Conference, CAIP 2005, Versailles, France, September 5-8, 2005. Computer Analysis of Images and Patterns, pp. 407–414. Springer, Berlin (2005)

Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. 1981. Academic, London (1987)

Gillis, N., Glineur, F.: Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization. Neural Comput. 24(4), 1085–1105 (2012)

Guan, N., Tao, D., Luo, Z., Yuan, B.: NeNMF: an optimal gradient method for nonnegative matrix factorization. IEEE Trans. Sig. Process. 60(6), 2882–2898 (2012)

Hsieh, C.J., Dhillon, I.S.: Fast coordinate descent methods with variable selection for non-negative matrix factorization. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1064–1072. ACM (2011)

Kim, D., Sra, S., Dhillon, I.S.: A new projected quasi-Newton approach for the nonnegative least squares problem. Computer Science Department, University of Texas at Austin (2006)

Kim, D., Sra, S., Dhillon, I.S.: A non-monotonic method for large-scale non-negative least squares. Optim. Methods Softw. 28(5), 1012–1039 (2013)

Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)

Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems, vol. 161. SIAM, Philadelphia (1974)

Nesterov, Y.: A method of solving a convex programming problem with convergence rate $$o (1/k^2)$$ o ( 1 / k 2 ) . Sov. Math. Dokl. 27, 372–376 (1983)

Nesterov, Y.: Efficiency of coordinate descent methods on huge-scale optimization problems. Core Discussion Papers 2010002, Université Catholique de Louvain. Center for Operations Research and Econometrics (CORE) (2010)

Parikh, N., Boyd, S.: Proximal algorithms. Found. Trends Optim. 1(3), 123–231 (2013)

Potluru, V.K.: Frugal coordinate descent for large-scale NNLS. In: AAAI (2012)

Schmidt, M., Friedlander, M.: Coordinate descent converges faster with the Gauss–Southwell rule than random selection. In: NIPS OPT-ML Workshop (2014)

Van Benthem, M.H., Keenan, M.R.: Fast algorithm for the solution of large-scale non-negativity-constrained least squares problems. J. Chemom. 18(10), 441–450 (2004)

Zhang, Z.-Y.: Nonnegative matrix factorization: models, algorithms and applications. In: Holmes, D.E., Jain, L.C. (eds.) Data Mining: Foundations and Intelligent Paradigms, vol. 2. Springer, Berlin (2012)

Zhou, G., Cichocki, A., Zhao, Q., Xie, S.: Nonnegative matrix and tensor factorizations: an algorithmic perspective. Sig. Process. Mag. IEEE 31(3), 54–65 (2014)