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Space-filling curves for numerical approximation and visualization of solutions to systems of nonlinear inequalities with applications in robotics

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  • Lera, Daniela
  • Posypkin, Mikhail
  • Sergeyev, Yaroslav D.

Abstract

The problem of approximating and visualizing the solution set of systems of nonlinear inequalities can be frequently met in practice, in particular, when it is required to find the working space of some robots. In this paper, a method using Peano-Hilbert space-filling curves for the dimensionality reduction has been proposed for functions satisfying the Lipschitz condition. Theoretical properties of the introduced algorithm showing advantages of this reduction in the context of the present problem have been established and convergence properties of this method have been studied. A number of experiments executed on test functions and problems regarding finding workspace of robots confirm theoretical results and show a promising character of the new methodology.

Suggested Citation

  • Lera, Daniela & Posypkin, Mikhail & Sergeyev, Yaroslav D., 2021. "Space-filling curves for numerical approximation and visualization of solutions to systems of nonlinear inequalities with applications in robotics," Applied Mathematics and Computation, Elsevier, vol. 390(C).
    • Handle: RePEc:eee:apmaco:v:390:y:2021:i:c:s0096300320305762
    DOI: 10.1016/j.amc.2020.125660
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    References listed on IDEAS

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    1. Grishagin, Vladimir & Israfilov, Ruslan & Sergeyev, Yaroslav, 2018. "Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 270-280.
    2. Yuri Evtushenko & Mikhail Posypkin & Larisa Rybak & Andrei Turkin, 2018. "Approximating a solution set of nonlinear inequalities," Journal of Global Optimization, Springer, vol. 71(1), pages 129-145, May.
    3. Konstantin Barkalov & Roman Strongin, 2018. "Solving a set of global optimization problems by the parallel technique with uniform convergence," Journal of Global Optimization, Springer, vol. 71(1), pages 21-36, May.
    4. Daniela Lera & Yaroslav D. Sergeyev, 2018. "GOSH: derivative-free global optimization using multi-dimensional space-filling curves," Journal of Global Optimization, Springer, vol. 71(1), pages 193-211, May.
    5. Remigijus Paulavičius & Yaroslav Sergeyev & Dmitri Kvasov & Julius Žilinskas, 2014. "Globally-biased Disimpl algorithm for expensive global optimization," Journal of Global Optimization, Springer, vol. 59(2), pages 545-567, July.
    6. James M. Calvin & Yvonne Chen & Antanas Žilinskas, 2012. "An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate for Twice Continuously Differentiable Functions," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 628-636, November.
    7. Anatoly Zhigljavsky & Antanas Žilinskas, 2008. "Stochastic Global Optimization," Springer Optimization and Its Applications, Springer, number 978-0-387-74740-8, September.
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    Cited by:

    1. Mikhail Posypkin & Oleg Khamisov, 2021. "Automatic Convexity Deduction for Efficient Function’s Range Bounding," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
    2. Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Zeshan Aslam Khan & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Hierarchical Quasi-Fractional Gradient Descent Method for Parameter Estimation of Nonlinear ARX Systems Using Key Term Separation Principle," Mathematics, MDPI, vol. 9(24), pages 1-14, December.

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