Skip to main content
Log in

Global optimization based on bisection of rectangles, function values at diagonals, and a set of Lipschitz constants

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

Abstract

We consider a global optimization problem for Lipschitz-continuous functions with an unknown Lipschitz constant. Our approach is based on the well-known DIRECT (DIviding RECTangles) algorithm and motivated by the diagonal partitioning strategy. One of the main advantages of the diagonal partitioning scheme is that the objective function is evaluated at two points at each hyper-rectangle and, therefore, more comprehensive information about the objective function is considered than using the central sampling strategy used in most DIRECT-type algorithms. In this paper, we introduce a new DIRECT-type algorithm, which we call BIRECT (BIsecting RECTangles). In this algorithm, a bisection is used instead of a trisection which is typical for diagonal-based and DIRECT-type algorithms. The bisection is preferable to the trisection because of the shapes of hyper-rectangles, but usual evaluation of the objective function at the center or at the endpoints of the diagonal are not favorable for bisection. In the proposed algorithm the objective function is evaluated at two points on the diagonal equidistant between themselves and the endpoints of a diagonal. This sampling strategy enables reuse of the sampling points in descendant hyper-rectangles. The developed algorithm gives very competitive numerical results compared to the DIRECT algorithm and its well know modifications.

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

Similar content being viewed by others

References

  1. Casado, L.G., García, I., Tóth-G, B., Hendrix, E.M.T.: On determining the cover of a simplex by spheres centered at its vertices. J. Glob. Optim. 50(4), 645–655 (2011). doi:10.1007/s10898-010-9524-x

    Article  MathSciNet  MATH  Google Scholar 

  2. Custódio, A.L., Rocha, H., Vicente, L.N.: Incorporating minimum Frobenius norm models in direct search. Comput. Optim. Appl. 46(2), 265–278 (2010). doi:10.1007/s10589-009-9283-0

    Article  MathSciNet  MATH  Google Scholar 

  3. Di Serafino, D., Liuzzi, G., Piccialli, V., Riccio, F., Toraldo, G.: A modified DIviding RECTangles algorithm for a problem in astrophysics. J. Optim. Theory Appl. 151(1), 175–190 (2011). doi:10.1007/s10957-011-9856-9

    Article  MathSciNet  MATH  Google Scholar 

  4. Finkel, D.E.: Global optimization with the Direct algorithm. Ph.D. thesis, North Carolina State University (2005)

  5. Finkel, D.E., Kelley, C.T.: Additive scaling and the DIRECT algorithm. J. Glob. Optim. 36(4), 597–608 (2006). doi:10.1007/s10898-006-9029-9

    Article  MathSciNet  MATH  Google Scholar 

  6. Floudas, C.A., Pardalos, P.M. (eds.): Encyclopedia of Optimization (Vol. 6), 2nd edn. Springer, Berlin (2009)

  7. Gablonsky, J.M.: Modifications of the Direct algorithm. Ph.D. thesis, North Carolina State University (2001)

  8. Gablonsky, J.M., Kelley, C.T.: A locally-biased form of the DIRECT algorithm. J. Glob. Optim. 21(1), 27–37 (2001). doi:10.1023/A:1017930332101

    Article  MathSciNet  MATH  Google Scholar 

  9. Gorodetsky, S.Y.: Paraboloid triangulation methods in solving multiextremal optimization problems with constraints for a class of functions with Lipschitz directional derivatives. Vestn. Lobachevsky State Univ. Nizhni Novgorod 1(1), 144–155 (2012). (in Russian)

    Google Scholar 

  10. Hedar, A.: Test functions for unconstrained global optimization. http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.htm (2005). Accessed: 22 Feb 2016

  11. Horst, R., Pardalos, P.M., Thoai, N.V.: Introduction to Global Optimization. Nonconvex Optimization and Its Application. Kluwer Academic Publishers, Dordrect (1995)

    MATH  Google Scholar 

  12. Horst, R., Tuy, H.: Global Optimization: Deterministic Approaches. Springer, Berlin (1996)

    Book  MATH  Google Scholar 

  13. Jones, D.R.: The direct global optimization algorithm. In: Floudas, C.A., Pardalos, P.M. (eds.) The Encyclopedia of Optimization, pp. 431–440. Kluwer Academic Publishers, Dordrect (2001)

    Chapter  Google Scholar 

  14. Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the Lipschitz constant. J. Optim. Theory Appl. 79(1), 157–181 (1993). doi:10.1007/BF00941892

    Article  MathSciNet  MATH  Google Scholar 

  15. Kvasov, D.E., Pizzuti, C., Sergeyev, Y.D.: Local tuning and partition strategies for diagonal GO methods. Numer. Math. 94(1), 93–106 (2003). doi:10.1007/s00211-002-0419-8

    Article  MathSciNet  MATH  Google Scholar 

  16. Kvasov, D.E., Sergeyev, Y.D.: A univariate global search working with a set of Lipschitz constants for the first derivative. Optim. Lett. 3(2), 303–318 (2009). doi:10.1007/s11590-008-0110-9

    Article  MathSciNet  MATH  Google Scholar 

  17. Kvasov, D.E., Sergeyev, Y.D.: Lipschitz gradients for global optimization in a one-point-based partitioning scheme. J. Comput. Appl. Math. 236(16), 4042–4054 (2012). doi:10.1016/j.cam.2012.02.020

    Article  MathSciNet  MATH  Google Scholar 

  18. Kvasov, D.E., Sergeyev, Y.D.: Lipschitz global optimization methods in control problems. Autom. Remote Control 74(9), 1435–1448 (2013). doi:10.1134/S0005117913090014

    Article  MathSciNet  MATH  Google Scholar 

  19. Lera, D., Sergeyev, Y.D.: Deterministic global optimization using space-filling curves and multiple estimates of Lipschitz and Hölder constants. Commun. Nonlinear Sci. Numer. Simul. 23(1), 328–342 (2015). doi:10.1016/j.cnsns.2014.11.015

    Article  MathSciNet  MATH  Google Scholar 

  20. Liu, Q., Cheng, W.: A modified DIRECT algorithm with bilevel partition. J. Glob. Optim. 60(3), 483–499 (2014). doi:10.1007/s10898-013-0119-1

    Article  MathSciNet  MATH  Google Scholar 

  21. Liu, Q., Zeng, J., Yang, G.: MrDIRECT: a multilevel robust DIRECT algorithm for global optimization problems. J. Glob. Optim. 62(2), 205–227 (2015). doi:10.1007/s10898-014-0241-8

    MathSciNet  MATH  Google Scholar 

  22. Liuzzi, G., Lucidi, S., Piccialli, V.: A direct-based approach exploiting local minimizations for the solution for large-scale global optimization problems. Comput. Optim. Appl. 45(2), 353–375 (2010). doi:10.1007/s10589-008-9217-2

    Article  MathSciNet  MATH  Google Scholar 

  23. Liuzzi, G., Lucidi, S., Piccialli, V.: A DIRECT-based approach exploiting local minimizations for the solution of large-scale global optimization problems. Comput. Optim. Appl. 45, 353–375 (2010). doi:10.1007/s10589-008-9217-2

    Article  MathSciNet  MATH  Google Scholar 

  24. Liuzzi, G., Lucidi, S., Piccialli, V.: A partition-based global optimization algorithm. J. Glob. Optim. 48(1), 113–128 (2010). doi:10.1007/s10898-009-9515-y

    Article  MathSciNet  MATH  Google Scholar 

  25. Liuzzi, G., Lucidi, S., Piccialli, V.: Exploiting derivative-free local searches in direct-type algorithms for global optimization. Computat. Optim. Appl. (2014). doi:10.1007/s10589-015-9741-9

    MATH  Google Scholar 

  26. Mockus, J., Paulavičius, R., Rusakevičius, D., Šešok, D., Žilinskas, J.: Application of Reduced-set Pareto-Lipschitzian Optimization to truss optimization. J. Glob. Optim. (2015). doi:10.1007/s10898-015-0364-6

    MATH  Google Scholar 

  27. Paulavičius, R., Sergeyev, Y.D., Kvasov, D.E., Žilinskas, J.: Globally-biased DISIMPL algorithm for expensive global optimization. J. Glob. Optim. 59(2–3), 545–567 (2014). doi:10.1007/s10898-014-0180-4

    Article  MathSciNet  MATH  Google Scholar 

  28. Paulavičius, R., Žilinskas, J.: Analysis of different norms and corresponding Lipschitz constants for global optimization. Inf. Technol. Control 36(4), 383–387 (2007)

    Google Scholar 

  29. Paulavičius, R., Žilinskas, J.: Simplicial Lipschitz optimization without the Lipschitz constant. J. Glob. Optim. 59(1), 23–40 (2013). doi:10.1007/s10898-013-0089-3

    Article  MathSciNet  MATH  Google Scholar 

  30. Paulavičius, R., Žilinskas, J.: Simplicial Global Optimization. SpringerBriefs in Optimization. Springer, New York (2014). doi:10.1007/978-1-4614-9093-7

    Book  MATH  Google Scholar 

  31. Paulavičius, R., Žilinskas, J.: Advantages of simplicial partitioning for Lipschitz optimization problems with linear constraints. Optim. Lett. 10(2), 237–246 (2016). doi:10.1007/s11590-014-0772-4

    Article  MathSciNet  MATH  Google Scholar 

  32. Paulavičius, R., Žilinskas, J., Grothey, A.: Parallel branch and bound for global optimization with combination of Lipschitz bounds. Optim. Methods Softw. 26(3), 487–498 (2011). doi:10.1080/10556788.2010.551537

    Article  MathSciNet  Google Scholar 

  33. Pintér, J.D.: Global Optimization in Action (Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications). Kluwer Academic Publishers, Dordrecht (1996)

    Book  MATH  Google Scholar 

  34. Sergeyev, Y.D.: On convergence of divide the best global optimization algorithms. Optimization 44(3), 303–325 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  35. Sergeyev, Y.D.: An efficient strategy for adaptive partition of \(N\)-dimensional intervals in the framework of diagonal algorithms. J. Optim. Theory Appl. 107(1), 145–168 (2000). doi:10.1023/A:1004613001755

    Article  MathSciNet  MATH  Google Scholar 

  36. Sergeyev, Y.D.: Efficient partition of n-dimensional intervals in the framework of one-point-based algorithms. J. Optim. Theory Appl. 124(2), 503–510 (2005). doi:10.1007/s10957-004-0948-7

    Article  MathSciNet  MATH  Google Scholar 

  37. Sergeyev, Y.D., Kvasov, D.E.: Global search based on diagonal partitions and a set of Lipschitz constants. SIAM J. Optim. 16(3), 910–937 (2006). doi:10.1137/040621132

    Article  MathSciNet  MATH  Google Scholar 

  38. Sergeyev, Y.D., Kvasov, D.E.: Diagonal Global Optimization Methods. FizMatLit, Moscow (2008). (in Russian)

    MATH  Google Scholar 

  39. Sergeyev, Y.D., Kvasov, D.E.: On deterministic diagonal methods for solving global optimization problems with Lipschitz gradients. In: Optimization, Control, and Applications in the Information Age, vol. 130, pp. 315–334. Springer, Switzerland (2015). doi:10.1007/978-3-319-18567-5-16

  40. Strongin, R.G., Sergeyev, Y.D.: Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Kluwer Academic Publishers, Dordrecht (2000)

    Book  MATH  Google Scholar 

  41. Tuy, H.: Convex Analysis and Global Optimization. Springer, Dordrecht (2013)

    MATH  Google Scholar 

  42. Žilinskas, A., Žilinskas, J.: Adaptation of a one-step worst-case optimal univariate algorithm of bi-objective Lipschitz optimization to multidimensional problems. Commun. Nonlinear Sci. Numer. Simul. 21(1–3), 89–98 (2015). doi:10.1016/j.cnsns.2014.08.025

    Article  MathSciNet  MATH  Google Scholar 

  43. Zhigljavsky, A., Žilinskas, A.: Stochastic Global Optimization. Springer, New York (2008)

    MATH  Google Scholar 

  44. Žilinskas, A., Žilinskas, J.: Global optimization based on a statistical model and simplicial partitioning. Comput. Math. Appl. 44(7), 957–967 (2002). doi:10.1016/S0898-1221(02)00206-7

    Article  MathSciNet  MATH  Google Scholar 

  45. Žilinskas, J.: Branch and bound with simplicial partitions for global optimization. Math. Model. Anal. 13(1), 145–159 (2008). doi:10.3846/1392-6292.2008.13.145-159

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research was funded by a Grant (No. MIP-051/2014) from the Research Council of Lithuania.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julius Žilinskas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paulavičius, R., Chiter, L. & Žilinskas, J. Global optimization based on bisection of rectangles, function values at diagonals, and a set of Lipschitz constants. J Glob Optim 71, 5–20 (2018). https://doi.org/10.1007/s10898-016-0485-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-016-0485-6

Keywords

Navigation