Skip to main content
Log in

Improved relaxations for the parametric solutions of ODEs using differential inequalities

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

Abstract

A new method is described for computing nonlinear convex and concave relaxations of the solutions of parametric ordinary differential equations (ODEs). Such relaxations enable deterministic global optimization algorithms to be applied to problems with ODEs embedded, which arise in a wide variety of engineering applications. The proposed method computes relaxations as the solutions of an auxiliary system of ODEs, and a method for automatically constructing and numerically solving appropriate auxiliary ODEs is presented. This approach is similar to two existing methods, which are analyzed and shown to have undesirable properties that are avoided by the new method. Two numerical examples demonstrate that these improvements lead to significantly tighter relaxations than previous methods.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adjiman C.S., Dallwig S., Floudas C.A., Neumaier A.: A global optimization method, αBB, for general twice-differentiable constrained NLPs—I. Theoretical advances. Comput. Chem. Eng. 22(9), 1137–1158 (1998)

    Article  Google Scholar 

  2. Aubin J.P.: Viability Theory. Birkhauser, Boston (1991)

    Google Scholar 

  3. Banga J., Seider W.: Global optimization of chemical processes using stochastic algorithms. In: Floudas, C., Pardalos, P. (eds.) State of the Art in Global Optimization: Computational Methods and Applications, Kluwer, Dordrecht (1996)

    Google Scholar 

  4. Bompadre A., Mitsos A.: Convergence rate of McCormick relaxations. J. Glob. Optim. 52(1), 1–28 (2012)

    Article  Google Scholar 

  5. Carrasco E., Banga J.: Dynamic optimization of batch reactors using adaptive stochastic algorithms. Ind. Eng. Chem. Res. 36(6), 2252–2261 (1997)

    Article  Google Scholar 

  6. Castiglione F., Piccoli B.: Cancer immunotherapy, mathematical modeling and optimal control. J. Theor. Biol. 247, 723–732 (2007)

    Article  Google Scholar 

  7. Cizniar M., Podmajersky M., Hirmajer T., Fikar M., Latifi A.M.: Global optimization for parameter estimation of differential-algebraic systems. Chem. Pap. 63(3), 274–283 (2009)

    Article  Google Scholar 

  8. Cohen S.D., Hindmarsh A.C.: CVODE, a stiff/nonstiff ODE solver in C. Comput. Phys. 10(2), 138–143 (1996)

    Google Scholar 

  9. Esposito W.R., Floudas C.A.: Global optimization for the parameter estimation of differential-algabraic systems. Ind. Eng. Chem. Res. 39, 1291–1310 (2000)

    Article  Google Scholar 

  10. Filippov A.F.: Differential Equations with Discontinuous Righthand Sides. Kluwer, Dordrecht (1988)

    Google Scholar 

  11. Harrison, G.W.: Dynamic models with uncertain parameters. In: Avula, X. (eds.) Proceedings of the 1st International Conference on Mathematical Modeling, vol. 1, pp. 295–304 (1977)

  12. Huang H., Adjiman C.S., Shah N.: Quantitative framework for reliable safety analysis. AIChE J. 48(1), 78–96 (2002)

    Article  Google Scholar 

  13. Khalil K.H.: Nonlinear Systems, 3rd edn. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  14. Lin Y., Stadtherr M.A.: Deterministic global optimization for parameter estimation of dynamic systems. Ind. Eng. Chem. Res. 45, 8438–8448 (2006)

    Article  Google Scholar 

  15. Lin Y., Stadtherr M.A.: Deterministic global optimization of nonlinear dynamic systems. AIChE J. 53(4), 866–875 (2007)

    Article  Google Scholar 

  16. Luus R., Dittrich J., Keil F.: Multiplicity of solutions in the optimization of a bifunctional catalyst blend in a tubular reactor. Can. J. Chem. Eng. 70, 780–785 (1992)

    Article  Google Scholar 

  17. Martin R.: Optimal control drug scheduling of cancer chemotherapy. Automatica 28(6), 1113–1123 (1992)

    Article  Google Scholar 

  18. McCormick G.P.: Computability of global solutions to factorable nonconvex programs: Part I—convex underestimating problems. Math. Program. 10, 147–175 (1976)

    Article  Google Scholar 

  19. Mitsos A., Chachuat B., Barton P.I.: McCormick-based relaxations of algorithms. SIAM J. Optim. 20(2), 573–601 (2009)

    Article  Google Scholar 

  20. Moore R.E.: Methods and Applications of Interval Analysis. SIAM, Philadelphia (1979)

    Book  Google Scholar 

  21. Neher M., Jackson K.R., Nedialkov N.S.: On Taylor model based integration of ODEs. SIAM J. Numer. Anal. 45(1), 236–262 (2007)

    Article  Google Scholar 

  22. Papamichail I., Adjiman C.S.: A rigorous global optimization algorithm for problems with ordinary differential equations. J. Glob. Optim. 24(1), 1–33 (2002)

    Article  Google Scholar 

  23. Papamichail I., Adjiman C.S.: Global optimization of dynamic systems. Comput. Chem. Eng. 28, 408–415 (2004)

    Article  Google Scholar 

  24. Park T., Barton P.: State event location in differential-algebraic models. ACM Trans. Model. Comput. Simul. 6(2), 137–165 (1996)

    Article  Google Scholar 

  25. Sahlodin A.M., Chachuat B.: Convex/concave relaxations of parametric ODEs using Taylor models. Comp. Chem. Eng. 35, 844–857 (2011)

    Article  Google Scholar 

  26. Sahlodin A.M., Chachuat B.: Discretize-then-relax approach for convex/concave relaxations of the solutions of parametric ODEs. Appl. Numer. Math. 61, 803–820 (2011)

    Article  Google Scholar 

  27. Scott J.K., Barton P.I.: Tight, efficient bounds on the solutions of chemical kinetics models. Comput. Chem. Eng. 34, 717–731 (2010)

    Article  Google Scholar 

  28. Scott, J.K., Barton, P.I.: Bounds on the reachable sets of nonlinear control systems (2011, submitted)

  29. Scott, J.K., Chachuat, B., Barton, P.I.: Nonlinear convex and concave relaxations for the solutions of parametric ODEs. Optim. Control Appl. Methods (2012, in press). doi:10.1002/oca.2014

  30. Scott J.K., Stuber M.D., Barton P.I.: Generalized McCormick relaxations. J. Glob. Optim. 51, 569–606 (2011). doi:10.1007/s10898-011-9664-7

    Article  Google Scholar 

  31. Singer A.B., Barton P.I.: Global solution of optimization problems with parameter-embedded linear dynamic systems. J. Optim. Theory Appl. 121, 613–646 (2004)

    Article  Google Scholar 

  32. Singer A.B., Barton P.I.: Bounding the solutions of parameter dependent nonlinear ordinary differential equations. SIAM J. Sci. Comput. 27, 2167–2182 (2006)

    Article  Google Scholar 

  33. Singer A.B., Barton P.I.: Global dynamic optimization for parameter estimation in chemical kinetics. J. Phys. Chem. A 110(3), 971–976 (2006)

    Article  Google Scholar 

  34. Singer A.B., Barton P.I.: Global optimization with nonlinear ordinary differential equations. J. Glob. Optim. 34, 159–190 (2006)

    Article  Google Scholar 

  35. Srinivasan B., Palanki S., Bonvin D.: Dynamic optimization of batch processes—I. characterization of the nominal solution. Comp. Chem. Eng. 27(1), 1–26 (2003)

    Article  Google Scholar 

  36. Szarski J.: Differential Inequalities. Polish Scientific Publishers, Warszawa (1965)

    Google Scholar 

  37. Taylor J.W., Ehlker G., Carstensen H.H., Ruslen L., Field R.W., Green W.H.: Direct measurement of the fast, reversible addition of oxygen to cyclohexadienyl radicals in nonpolar solvents. J. Phys. Chem. A 108, 7193–7203 (2004)

    Article  Google Scholar 

  38. Walter W.: Differential and Integral Inequalities. Springer, New York (1970)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paul I. Barton.

Additional information

This paper is based on work funded by the National Science Foundation under grant CBET-0933095.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Scott, J.K., Barton, P.I. Improved relaxations for the parametric solutions of ODEs using differential inequalities. J Glob Optim 57, 143–176 (2013). https://doi.org/10.1007/s10898-012-9909-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-012-9909-0

Keywords

Mathematics Subject Classification

Navigation