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Inexact-restoration modelling with monotone interpolation and parameter estimation

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Abstract

Complex real-life problems may be simplified in many possible ways. In several recent papers exactness requirements were considered as constraints of optimization problems and were handled with the tools of inexact restoration. Such techniques are revisited, generalized and simplified in the present paper. As a consequence, a new algorithm is introduced and applied to the estimation of parameters in hydraulic one-dimensional models. Moreover, the simplification includes the representation of well-known hydraulic parameters by a new family of monotone interpolatory functions.

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References

  • Agresta A, Baioletti M, Biscarini C, Caraffini F, Milani A, Santucci V (2021) Using optimisation meta-heuristics for the roughness estimation problem in river flow analysis. Appl Sci 11:10575

    Article  Google Scholar 

  • Ayvaz MT (2013) A linked simulation-optimization model for simultaneously estimating the Manning’s surface roughness values and their parameter structures in shallow water flows. J Hydrol 500:183–199

    Article  Google Scholar 

  • Askar MK, Al-jumaily KK (2008) A nonlinear optimization model for estimating Manning’s roughness coefficient. In: Proceedings of the twelfth international water technology conference, IWTC12, Alexandria, Egypt, pp 1299–1306

  • Birgin EG, Bueno LF, Martínez JM (2015) Assessing the reliability of general-purpose inexact restoration methods. J Comput Appl Math 282:1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Birgin EG, Krejić N, Martínez JM (2018) On the employment of inexact restoration for the minimization of functions whose evaluation is subject to errors. Math Comput 87:1307–1326

    Article  MathSciNet  MATH  Google Scholar 

  • Birgin EG, Krejić N, Martínez JM (2020) Iteration and evaluation complexity on the minimization of functions whose computation is intrinsically inexact. Math Comput 89:253–278

    Article  MathSciNet  MATH  Google Scholar 

  • Birgin EG, Krejić N, Martínez JM (2022) Inexact restoration for derivative-free expensive function minimization and applications. J Comput Appl Math 410:1–15

    Article  MathSciNet  MATH  Google Scholar 

  • Birgin EG, Martínez JM (2022) Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients. Comput Optim Appl 81:689–715

    Article  MathSciNet  MATH  Google Scholar 

  • Bourbaki N (2003) Elements of mathematics—topological vector spaces, Chapters 1–5. Heidelberg, Springer-Verlag Berlin

  • Bueno LF, Friedlander A, Martínez JM, Sobral FNC (2013) Inexact restoration method for derivative-free optimization with smooth constraints. SIAM J Optim 23:1189–1231

    Article  MathSciNet  MATH  Google Scholar 

  • Bueno LF, Haeser G, Martínez JM (2015) A flexible inexact-restoration method for constrained optimization. J Optim Theory Appl 165:188–208

    Article  MathSciNet  MATH  Google Scholar 

  • Bueno LF, Larreal F, Martínez JM (2023) Inexact restoration for minimization with inexact evaluation both of the objective function and the constraints. Mathematics of Computation. https://doi.org/10.1090/mcom/3855

    Article  MathSciNet  Google Scholar 

  • Bueno F, Martínez JM (2020) On the complexity of an inexact restoration method for constrained optimization. SIAM J Optim 30:80–101

    Article  MathSciNet  MATH  Google Scholar 

  • Conn AR, Scheinberg K, Vicente LN (2009) Introduction to derivative-free optimization, MPS-SIAM series on optimization

  • Ding Y, Jia Y, Wang SSY (2004) Identification of Manning’s roughness coefficients in shallow water flows. J Hydraul Eng 130:501–510

    Article  Google Scholar 

  • Ding Y, Wang SSY (2005) Identification of Manning’s roughness coefficients in channel network using adjoint analysis. Int J Comput Fluid Dyn 19:3–13

    Article  MathSciNet  MATH  Google Scholar 

  • Emmett WW, Myrick WW, Meade RH (1979) Field data describing the movement and storage of sediment in the East Fork River, Wyoming, Part 1. River hydraulics and sediment transport, report no. 1

  • Ferreira PS, Karas EW, Sachine M, Sobral FNC (2017) Global convergence of a derivative-free inexact restoration filter algorithm for nonlinear programming. Optimization 66:271–292

    Article  MathSciNet  MATH  Google Scholar 

  • Fischer A, Friedlander A (2010) A new line search inexact restoration approach for nonlinear programming. Comput Optim Appl 46:336–346

    Article  MathSciNet  MATH  Google Scholar 

  • Francisco JB, Gonçalves DS, Bazán FSV, Paredes LLT (2020) Non-monotone inexact restoration method for nonlinear programming. Comput Optim Appl 76:867–888

    Article  MathSciNet  MATH  Google Scholar 

  • Gelfand IM, Shilov GE (1968) Generalized functions. Academic Press, Cambridge

    Google Scholar 

  • Gioia G, Bombardelli FA (2001) Scaling and similarity in rough channel flows. Phys Rev Lett 88:014501

    Article  Google Scholar 

  • Gregory JA (1986) Shape preserving spline interpolation. Comput Aided Des 18:53–58

    Article  Google Scholar 

  • Guta K, Prasad KSH (2018) Estimation of open channel flow parameters by using optimization techniques. Int J Sci Res 6:1295–1304

    Google Scholar 

  • Karas E, Pilotta EA, Ribeiro A (2009) Numerical comparison of merit function with filter criterion in inexact restoration algorithms using hard-spheres problems. Comput Optim Appl 44:427–441

    Article  MathSciNet  MATH  Google Scholar 

  • Krejić N, Martínez JM (2016) Inexact restoration approach for minimization with inexact evaluation of the objective function. Math Comput 85:1775–1791

    Article  MathSciNet  MATH  Google Scholar 

  • LeVeque RJ (1992) Numerical methods for conservation laws, Lectures in mathematics. ETH Zürich, Birkäuser

    Book  MATH  Google Scholar 

  • Martínez JM (2001) Inexact-restoration method with Lagrangian tangent decrease and new merit function for nonlinear programming. J Optim Theory Appl 111:39–58

    Article  MathSciNet  MATH  Google Scholar 

  • Martínez JM, Pilotta EA (2000) Inexact restoration algorithms for constrained optimization. J Optim Theory Appl 104:135–163

    Article  MathSciNet  MATH  Google Scholar 

  • Martínez JM, Pilotta EA (2005) Inexact restoration methods for nonlinear programming: advances and perspectives. In: Qi L, Teo K, Yang X (eds) Optimization and Control with Applications, Applied Optimization, vol 96. Springer, New York, pp 271–291

    Chapter  Google Scholar 

  • Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313

    Article  MathSciNet  MATH  Google Scholar 

  • Picanço JL, Martínez JM, Pfeiffer C, Meyer JF (eds) (2023) Conflitos. CRIAB Publication, Institute of Advanced Studies of University of Campinas, Riscos e Impactos Associados a Barragens

  • Polya G (1957) How to solve it. A new aspect of mathematical method, 2nd edn. Princeton, Princeton University Press

    MATH  Google Scholar 

  • Porto RM (2000) Hidráulica Básica. EESC-USP, São Paulo

    Google Scholar 

  • Saint-Venant AJC (1871) Théorie du mouvement non-permanent des eaux, avec application aux crues des rivière at à l’introduction des marées dans leur lit. C R Séances Acad Sci 73:147–154

    MATH  Google Scholar 

  • Schumaker LL (1983) On shape preserving quadratic spline interpolation. SIAM J Numer Anal 20:854–864

    Article  MathSciNet  MATH  Google Scholar 

  • Schwartz L (1951) Théorie des distributions. Hermann

    MATH  Google Scholar 

  • Silva CEP, Monteiro MTT (2008) A filter inexact-restoration method for nonlinear programming. TOP 16:126–146

    Article  MathSciNet  MATH  Google Scholar 

  • Walpen J, Lotito PA, Mancinelli EM, Parente L (2020) The demand adjustment problem via inexact restoration method. Comput Appl Math 39:204

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work has been partially supported by CRIAB (Group of Conflicts, Risks and Impacts associated with dams), CeMEAI (Center of Mathematics and Statistics Applied to Industry) and the Brazilian agencies FAPESP (grants 2013/03447-6, 2013/05475-7, 2013/07375-0, 2014/18711-3, and 2016/01860-1) and CNPq (grants 309517/2014-1 and 303750/2014-6).

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Correspondence to L. T. Santos.

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Martínez, J.M., Santos, L.T. Inexact-restoration modelling with monotone interpolation and parameter estimation. Optim Eng (2023). https://doi.org/10.1007/s11081-023-09861-5

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