Skip to main content
Log in

Modification and improved implementation of the RPD method for computing state relaxations for global dynamic optimization

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

Abstract

This paper presents an improved method for computing convex and concave relaxations of the parametric solutions of ordinary differential equations (ODEs). These are called state relaxations and are crucial for solving dynamic optimization problems to global optimality via branch-and-bound (B &B). The new method improves upon an existing approach known as relaxation preserving dynamics (RPD). RPD is generally considered to be among the best available methods for computing state relaxations in terms of both efficiency and accuracy. However, it requires the solution of a hybrid dynamical system, whereas other similar methods only require the solution of a simple system of ODEs. This is problematic in the context of branch-and-bound because it leads to higher cost and reduced reliability (i.e., invalid relaxations can result if hybrid mode switches are not detected numerically). Moreover, there is no known sensitivity theory for the RPD hybrid system. This makes it impossible to compute subgradients of the RPD relaxations, which are essential for efficiently solving the associated B &B lower bounding problems. To address these limitations, this paper presents a small but important modification of the RPD theory, and a corresponding modification of its numerical implementation, that crucially allows state relaxations to be computed by solving a system of ODEs rather than a hybrid system. This new RPD method is then compared to the original using two examples and shown to be more efficient, more robust, and of almost identical accuracy.

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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Blanquero, R., Carrizosa, E., Jimenez-Cordero, A., Rodriguez, J.F.: A global optimization method for model selection in chemical reactions networks. Comput. Chem. Eng. 93, 52–62 (2016). https://doi.org/10.1016/j.compchemeng.2016.05.016

    Article  Google Scholar 

  2. Dowling, A.W., Vetukuri, S.R.R., Biegler, L.T.: Large-scale optimization strategies for pressure swing adsorption cycle synthesis. AIChE J. 58(12), 3777–3791 (2012). https://doi.org/10.1002/aic.13928

    Article  Google Scholar 

  3. Esposito, W.R., Floudas, C.A.: Deterministic global optimization in nonlinear optimal control problems. J. Glob. Optim. 17, 97–126 (2000). https://doi.org/10.1023/A:1026578104213

    Article  MathSciNet  Google Scholar 

  4. Esposito, W.R., Floudas, C.A.: Global optimization for the parameter estimation of differential-algebraic systems. Ind. Eng. Chem. Res. 39(5), 1291–1310 (2000). https://doi.org/10.1021/ie990486w

    Article  Google Scholar 

  5. Harrison, G.W.: Dynamic models with uncertain parameters. In: Avula, X.J.R. (ed.) Proc. of the First International Conference on Mathematical Modeling, vol. 1, pp. 295–304 (1977)

  6. Houska, B., Chachuat, B.: Branch-and-lift algorithm for deterministic global optimization in nonlinear optimal control. J. Optim. Theory Appl. 162, 208–248 (2014). https://doi.org/10.1007/s10957-013-0426-1

    Article  MathSciNet  Google Scholar 

  7. Huang, H., Adjiman, C.S., Shah, N.: Quantitative framework for reliable safety analysis. AIChE J. 48(1), 78–96 (2002). https://doi.org/10.1002/aic.690480110

    Article  Google Scholar 

  8. Khalil, H.K.: Nonlinear Systems, 3rd edn. Pearson (2001)

    Google Scholar 

  9. Ko, D., Siriwardane, R., Biegler, L.T.: Optimization of a pressure-swing adsorption process using zeolite 13x for CO2 sequestration. Ind. Eng. Chem. Res. 42(2), 339–348 (2003). https://doi.org/10.1021/ie0204540

    Article  Google Scholar 

  10. Land, A.H., Doig, A.G.: An automatic method of solving discrete programming problems. Econometrica 28(3), 497–520 (1960). https://doi.org/10.2307/1910129

    Article  MathSciNet  Google Scholar 

  11. Lin, Y., Stadtherr, M.A.: Deterministic global optimization for parameter estimation of dynamic systems. Ind. Eng. Chem. Res. 45(25), 8438–8448 (2006). https://doi.org/10.1021/ie0513907

    Article  Google Scholar 

  12. Lin, Y., Stadtherr, M.A.: Deterministic global optimization of nonlinear dynamic systems. AIChE J. 53(4), 866–875 (2007). https://doi.org/10.1002/aic.11101

    Article  Google Scholar 

  13. Lin, Y., Stadtherr, M.A.: Rigorous model-based safety analysis for nonlinear continuous-time systems. Comput. Chem. Eng. 33(2), 493–502 (2009). https://doi.org/10.1016/j.compchemeng.2008.11.010

    Article  Google Scholar 

  14. Luus, R.: Optimal control of batch reactors by iterative dynamic programming. J. Process Control 4(4), 218–226 (1994). https://doi.org/10.1016/0959-1524(94)80043-X

    Article  Google Scholar 

  15. Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13(11), 2467–2474 (2003). https://doi.org/10.1101/gr.1262503

    Article  Google Scholar 

  16. Papamichail, I., Adjiman, C.S.: A rigorous global optimization algorithm for problems with ordinary differential equations. J. Glob. Optim. 24, 1–33 (2002). https://doi.org/10.1023/A:1016259507911

    Article  MathSciNet  Google Scholar 

  17. Paulen, R., Villanueva, M., Fikar, M., Chachuat, B.: Guaranteed parameter estimation in nonlinear dynamic systems using improved bounding techniques. Institute of Electrical and Electronics Engineers (2013). https://doi.org/10.23919/ECC.2013.6669407

  18. Sahlodin, A.M., Chachuat, B.: Convex/concave relaxations of parametric ODEs using Taylor models. Comput. Chem. Eng. 35, 844–857 (2011). https://doi.org/10.1016/j.compchemeng.2011.01.031

    Article  Google Scholar 

  19. Sahlodin, A.M., Chachuat, B.: Discretize-then-relax approach for convex/concave relaxations of the solutions of parametric ODEs. Appl. Numer. Math. 61(7), 803–820 (2011). https://doi.org/10.1016/j.apnum.2011.01.009

    Article  MathSciNet  Google Scholar 

  20. Schaber, S.D., Scott, J.K., Barton, P.I.: Convergence-order analysis for differential-inequalities-based bounds and relaxations of the solutions of ODEs. J. Glob. Optim. 73(1), 113–151 (2019). https://doi.org/10.1007/s10898-018-0691-5

    Article  MathSciNet  Google Scholar 

  21. Scott, J.K.: Reachability analysis and deterministic global optimization of differential-algebraic systems. Ph.D. thesis, Massachusetts Institute of Technology (2012). https://www.semanticscholar.org/paper/Reachability-analysis-and-deterministic-global-of-Scott/90626ca617d1d8c2aff3d931c97e2cffee04cd20

  22. Scott, J.K., Barton, P.I.: Bounds on the reachable sets of nonlinear control systems. Automatica 49(1), 93–100 (2013). https://doi.org/10.1016/j.automatica.2012.09.020

    Article  MathSciNet  Google Scholar 

  23. 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

    Article  MathSciNet  Google Scholar 

  24. Scott, J.K., Chachuat, B., Barton, P.I.: Nonlinear convex and concave relaxations for the solutions of parametric ODEs. Optim. Control Appl. Methods 34(2), 145–163 (2012). https://doi.org/10.1002/oca.2014

    Article  MathSciNet  Google Scholar 

  25. Scott, J.K., Stuber, M.D., Barton, P.I.: Generalized McCormick relaxations. J. Glob. Optim. 51(4), 569–606 (2011). https://doi.org/10.1007/s10898-011-9664-7

    Article  MathSciNet  Google Scholar 

  26. Shen, K.J., Scott, J.K.: Rapid and accurate reachability analysis for nonlinear dynamic systems by exploiting model redundancy. Comput. Chem. Eng. 106, 596–608 (2017). https://doi.org/10.1016/j.compchemeng.2017.08.001

    Article  Google Scholar 

  27. Singer, A.B., Barton, P.I.: Bounding the solutions of parameter dependent nonlinear ordinary differential equations. SIAM J. Sci. Comput. 27(6), 2167–2182 (2006). https://doi.org/10.1137/040604388

    Article  MathSciNet  Google Scholar 

  28. Singer, A.B., Barton, P.I.: Global optimization with nonlinear ordinary differential equations. J. Glob. Optim. 34, 159–190 (2006). https://doi.org/10.1007/s10898-005-7074-4

    Article  MathSciNet  Google Scholar 

  29. Singer, A.B., Taylor, J.W., Barton, P.I., Green, W.H.: Global dynamic optimization for parameter estimation in chemical kinetics. J. Phys. Chem. A 110(3), 971–976 (2006). https://doi.org/10.1021/jp0548873

    Article  Google Scholar 

  30. Song, Y., Cao, H., Mehta, C., Khan, K.A.: Bounding convex relaxations of process models from below by trackable black-box sampling. Comput. Chem. Eng. (2021). https://doi.org/10.1016/j.compchemeng.2021.107413

    Article  Google Scholar 

  31. Song, Y., Khan, K.A.: Optimization-based convex relaxations for nonconvex parametric systems of ordinary differential equations. Math. Program. (2021). https://doi.org/10.1007/s10107-021-01654-x

    Article  Google Scholar 

  32. Szarski, J.: Differential Inequalities. Polish Scientific Publishers, Warszawa (1965). https://eudml.org/doc/219295

  33. 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(35), 7193–7203 (2004). https://doi.org/10.1021/jp0379547

    Article  Google Scholar 

  34. Villanueva, M.E., Houska, B., Chachuat, B.: Unified framework for the propagation of continuous-time enclosures for parametric nonlinear ODEs. J. Glob. Optim. 62, 575–613 (2014). https://doi.org/10.1007/s10898-014-0235-6

    Article  MathSciNet  Google Scholar 

  35. Wilhelm, M.E., Le, A.V., Stuber, M.D.: Global optimization of stiff dynamical systems. AIChE J. 65(12), 1–20 (2019). https://doi.org/10.1002/aic.16836

    Article  Google Scholar 

  36. Ye, J., Scott, J.K.: Extended McCormick relaxation rules for handling empty arguments representing infeasibility. J. Glob. Optim. 87, 57–95 (2023). https://doi.org/10.1007/s10898-023-01315-7

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joseph K. Scott.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

This material is based upon work supported by the National Science Foundation under Grant No. 1949747.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, J., Scott, J.K. Modification and improved implementation of the RPD method for computing state relaxations for global dynamic optimization. J Glob Optim (2024). https://doi.org/10.1007/s10898-024-01381-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10898-024-01381-5

Keywords

Navigation