Computational advances in polynomial optimization: RAPOSa, a freely available global solver-Reference-Cited by-同舟云学术

Computational advances in polynomial optimization: RAPOSa, a freely available global solver

Published:2022-09-29 Issue:3 Volume:85 Page:541-568
ISSN:0925-5001
Container-title:Journal of Global Optimization
language:en
Short-container-title:J Glob Optim

Author:

González-Rodríguez Brais^ORCID,Ossorio-Castillo Joaquín,González-Díaz Julio,González-Rueda Ángel M.,Penas David R.,Rodríguez-Martínez Diego

Abstract

AbstractIn this paper we introduce , a global optimization solver specifically designed for (continuous) polynomial programming problems with box-constrained variables. Written entirely in , is based on the Reformulation-Linearization (Sherali and Tuncbilek in J Glob Optim 103:225–249, 1992). We present a description of the main characteristics of along with a thorough analysis of the impact on its performance of various enhancements discussed in the literature, such as bound tightening and SDP cuts. We also present a comparative study with three of the main state-of-the-art global optimization solvers: , and .

Funder

European Regional Development Fund

Ministerio de Ciencia y Tecnología

Ministerio de Educación, Cultura y Deporte

Xunta de Galicia

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Management Science and Operations Research,Control and Optimization,Computer Science Applications,Business, Management and Accounting (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10898-022-01229-w.pdf

Reference35 articles.

1. Achterberg, T., Koch, T., Martin, A.: Branching rules revisited. Oper. Res. Lett. 33, 42–54 (2005)

2. De Backer, B., Didier, F., Guére, E.: Glop: an open-source linear programming solver. In: 22nd International Symposium on Mathematical Programming (2015)

3. Balcan, M.-F., Dick, T., Sandholm, T., Vitercik, E.: Learning to branch. In: Dy, J., Krause, A. (Eds.) Proceedings of the 35th International Conference on Machine Learning, Stockholmsmässan, Stockholm Sweden: PMLR, Volume 80 of Proceedings of Machine Learning Research, pp. 344–353 (2018)

4. Belotti, P.: Bound reduction using pairs of linear inequalities. J. Glob. Optim. 56, 787–819 (2013)

5. Belotti, P., Cafieri, S., Lee, J., Liberti, L.: On the convergence of feasibility based bounds tightening (2012)

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