Abstract
We study the problem of checking pseudoconvexity of a twice differentiable function on an interval domain. Based on several characterizations of pseudoconvexity of a real function, we propose sufficient conditions for verifying pseudoconvexity on a domain formed by a Cartesian product of real intervals. We carried out numerical experiments to show which methods perform well from two perspectives—the computational complexity and effectiveness of recognizing pseudoconvexity.
Similar content being viewed by others
References
Adjiman, C.S., Androulakis, I.P., Floudas, C.A.: A global optimization method, \(\alpha \)BB, for general twice-differentiabe constrained NLPs-II. Implementation and computational results. Comput. Chem. Eng. 22(9), 1159–1179 (1998)
Adjiman, C.S., Dallwig, S., Floudas, C.A., Neumaier, A.: A globaloptimization method, \(\alpha \)BB, for general twice-differentiable constrained NLPs-I. Theoretical advances. Comput. Chem. Eng. 22(9), 1137–1158 (1998)
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)
Androulakis, I.P., Maranas, C.D., Floudas, C.A.: \(\alpha \)BB: a global optimization method for general constrained nonconvex problems. J. Glob. Optim. 7(4), 337–363 (1995)
Avriel, M., Schaible, S.: Second order characterizations of pseudoconvex functions. Math. Program. 14(1), 170–185 (1978)
Crouzeix, J.: On second order conditions for quasiconvexity. Math. Program. 18(1), 349–352 (1980)
Crouzeix, J., Ferland, J.A.: Criteria for quasi-convexity and pseudo-convexity: relationships and comparisons. Math. Program. 23(1), 193–205 (1982)
Crouzeix, J.P.: Characterizations of generalized convexity and generalized monotonicity: a survey. In: Crouzeix, J.P., Martinez-Legaz, J.E., Volle, M. (eds.) Generalized Convexity, Generalized Monotonicity: Recent Results, Nonconvex Optimization and Its Applications, vol. 27, pp. 237–256. Springer, Berlin (1998)
Ferland, J.A.: Mathematical programming problems with quasi-convex objective functions. Math. Program. 3(1), 296–301 (1972)
Ferland, J.A.: Matrix criteria for pseudo-convex functions in the class \(C^2\). Linear Algebra Appl. 21(1), 47–57 (1978)
Floudas, C.A.: Deterministic Global Optimization: Theory, Methods and Applications (Nonconvex Optimization and its Applications), vol. 37. Kluwer, Dordrecht (2000)
Hadjisavvas, N., Komlósi, S., Schaible, S. (eds.): Handbook of Generalized Convexity and Generalized Monotonicity. Springer, New York (2005)
Hansen, E.R., Walster, G.W.: Global Optimization Using Interval Analysis, 2nd edn. Marcel Dekker, New York (2004)
Hendrix, E.M.T., Gazdag-Tóth, B.: Introduction to Nonlinear and Global Optimization, Optimization and Its Applications, vol. 37. Springer, New York (2010)
Hertz, D.: The extreme eigenvalues and stability of real symmetric interval matrices. IEEE Trans. Automat. Contr. 37(4), 532–535 (1992)
Hladík, M.: On the efficient Gerschgorin inclusion usage in the global optimization \(\alpha \)BB method. J. Glob. Optim. 61(2), 235–253 (2015)
Hladík, M.: An extension of the \(\alpha \)BB-type underestimation to linear parametric Hessian matrices. J. Glob. Optim. 64(2), 217–231 (2016)
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)
Hladík, M., Daney, D., Tsigaridas, E.P.: Characterizing and approximating eigenvalue sets of symmetric interval matrices. Comput. Math. Appl. 62(8), 3152–3163 (2011)
Hladík, M., Daney, D., Tsigaridas, E.P.: A filtering method for the interval eigenvalue problem. Appl. Math. Comput. 217(12), 5236–5242 (2011)
Kearfott, R.B.: Interval computations, rigour and non-rigour in deterministic continuous global optimization. Optim. Methods Softw. 26(2), 259–279 (2011)
Kreinovich, V., Lakeyev, A., Rohn, J., Kahl, P.: Computational Complexity and Feasibility of Data Processing and Interval Computations. Kluwer, Dordrecht (1998)
Mereau, P., Paquet, J.G.: Second order conditions for pseudo-convex functions. SIAM J. Appl. Math. 27, 131–137 (1974)
Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to Interval Analysis. SIAM, Philadelphia (2009)
Nemirovskii, A.: Several NP-hard problems arising in robust stability analysis. Math. Control Signals Syst. 6(2), 99–105 (1993)
Neumaier, A.: Interval Methods for Systems of Equations. Cambridge University Press, Cambridge (1990)
Neumaier, A.: Complete search in continuous global optimization and constraint satisfaction. Acta Numer. 13, 271–369 (2004)
Rohn, J.: Checking positive definiteness or stability of symmetric interval matrices is NP-hard. Comment. Math. Univ. Carol. 35(4), 795–797 (1994). http://uivtx.cs.cas.cz/~rohn/publist/74.pdf
Rohn, J.: Positive definiteness and stability of interval matrices. SIAM J. Matrix Anal. Appl. 15(1), 175–184 (1994)
Rohn, J.: Checking Properties of Interval Matrices. Technical report 686, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague (1996). http://hdl.handle.net/11104/0123221
Rohn, J.: A Handbook of Results on Interval Linear Problems. Technical report 1163, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague (2012). http://www.nsc.ru/interval/Library/InteBooks/!handbook.pdf
Rump, S.M.: INTLAB—INTerval LABoratory. In: Csendes, T. (ed.) Developments in Reliable Computing, pp. 77–104. Kluwer Academic Publishers, Dordrecht (1999). http://www.ti3.tu-harburg.de/rump/
Skjäl, A., Westerlund, T.: New methods for calculating \(\alpha \)BB-type underestimators. J. Glob. Optim. 58(3), 411–427 (2014)
Acknowledgements
The author was supported by the Czech Science Foundation Grant P402-13-10660S.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hladík, M. Testing pseudoconvexity via interval computation. J Glob Optim 71, 443–455 (2018). https://doi.org/10.1007/s10898-017-0537-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-017-0537-6