Abstract
The paper studies approaches to numerical solving huge-scale quasiseparable optimization problems. The main idea is based on using gradient methods with simple iteration structure instead more intelligent techniques, which is widely used for solving traditional, small-sized problems. The results of numerical experiments for a number of test quasiseparable optimization problems with dimensions up to 1010 variables are presented.
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References
Yu. Nesterov, Introductory Lectures on Convex Optimization (Springer US, Boston, 2004).
A. S. Anikin et al., “Effective numerical methods for huge-scale linear systems with double-sparsity and applications to Page Rank,” Proc. MIPT 7 (4), 74–94 (2015).
B. T. Polyak, “Minimization of unsmooth functionals,” USSR Comput. Math. Math. Phys. 9 (3), 14–29 (1969).
J. Barzilai and J. M. Borwein, “Two-point step size gradient methods,” IMA J. Numer. Anal., No. 8, 141–148 (1988).
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Submitted by L. N. Shchur
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Andrianov, A.N., Anikin, A.S., Bychkov, I.V. et al. Numerical solution of huge-scale quasiseparable optimization problems. Lobachevskii J Math 38, 870–873 (2017). https://doi.org/10.1134/S1995080217050031
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DOI: https://doi.org/10.1134/S1995080217050031