Skip to main content
Log in

Linear interval parametric approach to testing pseudoconvexity

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

Abstract

The recent paper (DOI: 10.1007/s10898-017-0537-6) suggests various practical tests (sufficient conditions) for checking pseudoconvexity of a twice differentiable function on an interval domain. The tests were implemented using interval extensions of the gradient and the Hessian of the function considered. In this paper, we present an alternative approach which is based on the use of more accurate affine form enclosures and affine arithmetic. We modify the tests to work with linear interval parametric enclosures of the gradients and the Hessians. We also present computational complexity results, showing that performing some tests exactly is NP-hard. It is shown by numerical experiments on random and benchmark data that the new approach results in more efficient tests for checking pseudoconvexity, however, at the expense of higher computation time.

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.

Similar content being viewed by others

Notes

  1. engine.h is a C/C++ header file for MATLAB engine programs, it contains function prototypes for engine routines.

References

  1. Ahmadi, A.A., Olshevsky, A., Parrilo, P.A., Tsitsiklis, J.N.: NP-hardness of deciding convexity of quartic polynomials and related problems. Math. Program. 137(1–2), 453–476 (2013)

    Article  MathSciNet  Google Scholar 

  2. Avriel, M., Schaible, S.: Second order characterizations of pseudoconvex functions. Math. Program. 14(1), 170–185 (1978)

    Article  MathSciNet  Google Scholar 

  3. Crouzeix, J.: On second order conditions for quasiconvexity. Math. Program. 18(1), 349–352 (1980)

    Article  MathSciNet  Google Scholar 

  4. Crouzeix, J., Ferland, J.A.: Criteria for quasi-convexity and pseudo-convexity: relationships and comparisons. Math. Program. 23(1), 193–205 (1982)

    Article  Google Scholar 

  5. Crouzeix, J.P.: Characterizations of generalized convexity and generalized monotonicity, a survey. In: J.P. Crouzeix, J.E. Martinez-Legaz, M. Volle (eds.) Generalized Convexity, Generalized Monotonicity: Recent Results, pp. 237–256. Springer, Berlin (1998)

  6. Ferland, J.A.: Mathematical programming problems with quasi-convex objective functions. Math. Program. 3(1), 296–301 (1972)

    Article  MathSciNet  Google Scholar 

  7. Ferland, J.A.: Matrix criteria for pseudo-convex functions in the class \(C^2\). Linear Algebra Appl. 21(1), 47–57 (1978)

    Article  MathSciNet  Google Scholar 

  8. de Figueiredo, L., Stolfi, J.: Self-Validated Numerical Methods and Applications. Brazilian Mathematics Colloquium monograph. IMPA, Rio de Janeiro, Brazil (1997)

  9. Floudas, C.A.: Deterministic global optimization. Theory, methods and applications. In: Nonconvex Optimization and its Applications, vol. 37. Kluwer, Dordrecht (2000)

  10. Hadjisavvas, N., Komlósi, S., Schaible, S. (eds.): Handbook of Generalized Convexity and Generalized Monotonicity. Springer, New York (2005)

    MATH  Google Scholar 

  11. Hendrix, E.M.T., Gazdag-Tóth, B.: Introduction to Nonlinear and Global Optimization, Optimization and Its Applications, vol. 37. Springer, New York (2010)

    Book  Google Scholar 

  12. Hladík, M.: On the efficient Gerschgorin inclusion usage in the global optimization \(\alpha \)BB method. J. Glob. Optim. 61(2), 235–253 (2015)

    Article  MathSciNet  Google Scholar 

  13. Hladík, M.: An extension of the \(\alpha \)BB-type underestimation to linear parametric Hessian matrices. J. Glob. Optim. 64(2), 217–231 (2016)

    Article  MathSciNet  Google Scholar 

  14. Hladík, M.: The effect of Hessian evaluations in the global optimization \(\alpha \)BB method. In: Bock, H., et al. (eds.) Modeling, Simulation and Optimization of Complex Processes HPSC 2015, pp. 67–79. Springer, Cham (2017)

  15. Hladík, M.: Testing pseudoconvexity via interval computation. J. Glob. Optim. 71(3), 443–455 (2018)

    Article  MathSciNet  Google Scholar 

  16. Hladík, M., Daney, D., Tsigaridas, E.: Bounds on real eigenvalues and singular values of interval matrices. SIAM J. Matrix Anal. Appl. 31(4), 2116–2129 (2010)

    Article  MathSciNet  Google Scholar 

  17. Hladík, M., Skalna, I.: Relations between various methods for solving linear interval and parametric equations. Linear Algebra Appl. 574, 1–21 (2019)

    Article  MathSciNet  Google Scholar 

  18. Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (1985)

    Book  Google Scholar 

  19. Kearfott, R.B.: Interval computations, rigour and non-rigour in deterministic continuous global optimization. Optim. Methods Softw. 26(2), 259–279 (2011)

    Article  MathSciNet  Google Scholar 

  20. Kolev, L., Skalna, I.: Exact solution to a parametric linear programming problem. Numer. Algorithms 78(4), 1183–1194 (2018)

    Article  MathSciNet  Google Scholar 

  21. Kolev, L.V.: Outer interval solution of the eigenvalue problem under general form parametric dependencies. Reliab. Comput. 12(2), 121–140 (2006)

    Article  MathSciNet  Google Scholar 

  22. Kolev, L.V.: Eigenvalue range determination for interval and parametric matrices. Int. J. Circuit Theory Appl. 38(10), 1027–1061 (2010)

    Article  Google Scholar 

  23. Kolev, L.V.: Parameterized solution of linear interval parametric systems. Appl. Math. Comput. 246, 229–246 (2014)

    MathSciNet  MATH  Google Scholar 

  24. Kolev, L.V.: A class of iterative methods for determining p-solutions of linear interval parametric systems. Reliab. Comput. 22, 26–46 (2016)

    MathSciNet  Google Scholar 

  25. Kreinovich, V., Lakeyev, A., Rohn, J., Kahl, P.: Computational Complexity and Feasibility of Data Processing and Interval Computations. Kluwer, Dordrecht (1998)

    Book  Google Scholar 

  26. Mereau, P., Paquet, J.G.: Second order conditions for pseudo-convex functions. SIAM J. Appl. Math. 27, 131–137 (1974)

    Article  MathSciNet  Google Scholar 

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

    Book  Google Scholar 

  28. Nemirovskii, A.: Several NP-hard problems arising in robust stability analysis. Math. Control Signals Syst. 6(2), 99–105 (1993)

    Article  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  30. Pardalos, P.M., Vavasis, S.A.: Quadratic programming with one negative eigenvalue is NP-hard. J. Glob. Optim. 1(1), 15–22 (1991)

    Article  MathSciNet  Google Scholar 

  31. Poljak, S., Rohn, J.: Checking robust nonsingularity is NP-hard. Math. Control Signals Syst. 6(1), 1–9 (1993)

    Article  MathSciNet  Google Scholar 

  32. Popova, E.D.: Strong regularity of parametric interval matrices. In: I. Dimovski et al. (ed.) Mathematics and Education in Mathematics. In: Proceedings of the 33rd Spring Conference of the Union of Bulgarian Mathematicians, Borovets, Bulgaria, pp. 446–451. BAS (2004)

  33. Rex, G., Rohn, J.: Sufficient conditions for regularity and singularity of interval matrices. SIAM J. Matrix Anal. Appl. 20(2), 437–445 (1998)

    Article  MathSciNet  Google Scholar 

  34. Skalna, I.: Strong regularity of parametric interval matrices. Linear Multilinear Algebra 65(12), 2472–2482 (2017)

    Article  MathSciNet  Google Scholar 

  35. Skalna, I.: Parametric Interval Algebraic Systems. Springer, Berlin (2018)

    Book  Google Scholar 

  36. Skalna, I., Hladík, M.: A new algorithm for Chebyshev minimum-error multiplication of reduced affine forms. Numer. Algorithms 76(4), 1131–1152 (2017)

    Article  MathSciNet  Google Scholar 

  37. Skalna, I., Hladík, M.: A new method for computing a p-solution to parametric interval linear systems with affine-linear and nonlinear dependencies. BIT Numer. Math. 57(4), 1109–1136 (2017)

    Article  MathSciNet  Google Scholar 

  38. Skjäl, A., Westerlund, T.: New methods for calculating \(\alpha \)BB-type underestimators. J. Glob. Optim. 58(3), 411–427 (2014)

    Article  MathSciNet  Google Scholar 

  39. Stolfi, J., de Figueiredo, L.: An introduction to affine arithmetic. TEMA Tend. Mat. Apl. Comput. 4(3), 297–312 (2003)

    MathSciNet  MATH  Google Scholar 

  40. Vavasis, S.A.: Nonlinear Optimization: Complexity Issues. Oxford University Press, New York (1991)

    MATH  Google Scholar 

Download references

Acknowledgements

M. Hladík was supported by the Czech Science Foundation Grant P403-18-04735S.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Milan Hladík.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hladík, M., Kolev, L.V. & Skalna, I. Linear interval parametric approach to testing pseudoconvexity. J Glob Optim 79, 351–368 (2021). https://doi.org/10.1007/s10898-020-00924-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-020-00924-w

Keywords

Navigation