Skip to main content

Advertisement

Log in

Approximating a solution set of nonlinear inequalities

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

In this paper we propose a method for solving systems of nonlinear inequalities with predefined accuracy based on nonuniform covering concept formerly adopted for global optimization. The method generates inner and outer approximations of the solution set. We describe the general concept and three ways of numerical implementation of the method. The first one is applicable only in a few cases when a minimum and a maximum of the constraints convolution function can be found analytically. The second implementation uses a global optimization method to find extrema of the constraints convolution function numerically. The third one is based on extrema approximation with Lipschitz under- and overestimations. We obtain theoretical bounds on the complexity and the accuracy of the generated approximations as well as compare proposed approaches theoretically and experimentally.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Bulatov, V.P., Khamisov, O.V.: Cutting methods in \({E}^{n+1}\) for global optimization of a class of functions. Comput. Math. Math. Phys. 47(11), 1756–1767 (2007)

    Article  MathSciNet  Google Scholar 

  2. Evtushenko, Y., Posypkin, M., Turkin, A., Rybak, L.: The non-uniform covering approach to manipulator workspace assessment. In: Young Researchers in Electrical and Electronic Engineering (EIConRus), 2017 IEEE Conference of Russian, pp. 386–389. IEEE (2017)

  3. Evtushenko, Y.G.: Numerical methods for finding global extrema (case of a non-uniform mesh). USSR Comput. Math. Math. Phys. 11(6), 38–54 (1971)

    Article  MATH  Google Scholar 

  4. Evtushenko, Y.G., Posypkin, M.: Effective hull of a set and its approximation. In: Doklady Mathematics, vol. 90, pp. 791–794. Springer (2014)

  5. Evtushenko, Y.G., Posypkin, M.A.: Nonuniform covering method as applied to multicriteria optimization problems with guaranteed accuracy. Comput. Math. Math. Phys. 53(2), 144–157 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  6. Evtushenko, Y.G., Posypkin, M.A., Rybak, L.A., Turkin, A.V.: Numerical method for approximating the solution set of a system of non-linear inequalities. Int. J. Open Inf. Technol. 4(12), 1–6 (2016)

    Google Scholar 

  7. Fletcher, R., Leyffer, S.: Nonlinear programming without a penalty function. Math. Program. 91(2), 239–269 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Garanzha, V.A., Kudryavtseva, L.: Generation of three-dimensional delaunay meshes from weakly structured and inconsistent data. Comput. Math. Math. Phys. 52(3), 427–447 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Gosselin, C.M., Jean, M.: Determination of the workspace of planar parallel manipulators with joint limits. Robot. Auton. Syst. 17(3), 129–138 (1996). doi:10.1016/0921-8890(95)00039-9

    Article  Google Scholar 

  10. Gu, C., Zhu, D.: A filter algorithm for nonlinear systems of equalities and inequalities. Appl. Math. Comput. 218(20), 10289–10298 (2012)

    MathSciNet  MATH  Google Scholar 

  11. Hansen, E., Walster, G.W.: Global Optimization Using Interval Analysis: Revised and Expanded, vol. 264. Springer, Berlin (2003)

    Google Scholar 

  12. He, C., Ma, C.: A smoothing self-adaptive Levenberg–Marquardt algorithm for solving system of nonlinear inequalities. Appl. Math. Comput. 216(10), 3056–3063 (2010)

    MathSciNet  MATH  Google Scholar 

  13. Jaulin, L., Kieffer, M., Didrit, O., Walter, E.: Applied Interval Analysis. Springer, London (1993)

    MATH  Google Scholar 

  14. Jaulin, L., Walter, E.: Set inversion via interval analysis for nonlinear bounded-error estimation. Automatica 29(4), 1053–1064 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kamenev, G.: Efficiency of the estimate refinement method for polyhedral approximation of multidimensional balls. Comput. Math. Math. Phys. 56(5), 744–755 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kamenev, G.K.: Method for polyhedral approximation of a ball with an optimal order of growth of the facet structure cardinality. Comput. Math. Math. Phys. 54(8), 1201–1213 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kvasov, D., Sergeyev, Y.D.: Deterministic approaches for solving practical black-box global optimization problems. Adv. Eng. Softw. 80(2), 58–66 (2015)

    Article  Google Scholar 

  18. Lera, D., Sergeyev, Y.D.: Deterministic global optimization using space-filling curves and multiple estimates of Lipschitz and Holder constants. Commun. Nonlinear Sci. Numer. Simul. 23(1–3), 328–342 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  19. Lotov, A.V., Pospelov, A.I.: The modified method of refined bounds for polyhedral approximation of convex polytopes. Comput. Math. Math. Phys. 48(6), 933–941 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Merlet, J.P.: Parallel Robots, vol. 74. Springer, Berlin (2012)

    MATH  Google Scholar 

  21. Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to Interval Analysis. SIAM, Philadelphia (2009)

    Book  MATH  Google Scholar 

  22. Neumaier, A.: Interval Methods for Systems of Equations, vol. 37. Cambridge University Press, Cambridge (1990)

    MATH  Google Scholar 

  23. Paulavicius, R., Zilinskas, J.: Analysis of different norms and corresponding Lipschitz constants for global optimization. Technol. Econ. Dev. Econ. 12(4), 301–306 (2006)

    Google Scholar 

  24. Paulavičius, R., Žilinskas, J.: Simplicial Global Optimization. Springer, Berlin (2014)

    Book  MATH  Google Scholar 

  25. Pinter, J.D.: Global optimization in action: continuous and lipschitz optimization—algorithms, implementation and applications. Kluwer Academic Publishers, Dordrecht (1996)

  26. Sergeyev, Y.D., Kvasov, D.E.: Global search based on efficient diagonal partitions and a set of Lipschitz constants. SIAM J. Optim. 16(2), 910–937 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  27. Strongin, R.G., Sergeyev, Y.D.: Global Optimization with Non-convex Constraints: Sequential and Parallel Algorithms, vol. 45. Springer, Berlin (2013)

    MATH  Google Scholar 

  28. Zhang, Y., Huang, Z.H.: A nonmonotone smoothing-type algorithm for solving a system of equalities and inequalities. J. Comput. Appl. Math. 233(9), 2312–2321 (2010)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mikhail Posypkin.

Additional information

The work was supported by the Russian Science Fund, project 16-19-00148.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Evtushenko, Y., Posypkin, M., Rybak, L. et al. Approximating a solution set of nonlinear inequalities. J Glob Optim 71, 129–145 (2018). https://doi.org/10.1007/s10898-017-0576-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-017-0576-z

Keywords

Navigation