Skip to main content
Log in

An engineering interpretation of Nesterov’s convex minimization algorithm and time integration: application to optimal fiber orientation

  • Original Paper
  • Published:
Computational Mechanics Aims and scope Submit manuscript

Abstract

Nesterov’s 1983 first-order minimization algorithm is equivalent to the numerical solution of a second-order ODE with non-constant damping. It is known that the algorithm can be obtained by time discretization with asynchronous damping, a long-standing technique in computational explicit dynamics. We extend the solution of the ODE to other time discretization algorithms, analyze their properties and provide an engineering interpretation of the process as well as a prototype implementation, addressing the estimation of the relevant parameters in the computational mechanics context. The main result is that a standard Newmark-type time-integration finite element code can be adopted to perform classical optimization in mechanics. Standard FE analysis, sensitivity analysis and optimization are performed in sequence using a typical FE framework. Geometric nonlinearities in the conservative case are addressed by the adjoint-variable method and examples of optimal fiber orientation are shown, exhibiting remarkable advantages with respect to more traditional optimization algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Nesterov Y (1983) A method of solving a convex programming problem with convergence rate \(\bigcirc (1/k^{2})\). Sov Math Dokl 27(2):372–376

    MATH  Google Scholar 

  2. Polyak BT (1964) Some methods of speeding up the convergence of iteration methods. USSR Comput Math Math Phys 4(5):1–17

    Article  Google Scholar 

  3. Nesterov Y (2004) Introductory lectures on convex optimization. A basic course, Applied optimization. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  4. Su W, Boyd S, Candès EJ (2016) A differential equation for modeling Nesterov’s accelerated gradient method: theory and insights. J Mach Learn Res 17:1–43

    MathSciNet  MATH  Google Scholar 

  5. Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems. J Res Natl Bureau Stand 49:409–436

    Article  MathSciNet  Google Scholar 

  6. Karimi S, Vavasis SA (2016) A unified convergence bound for conjugate gradient and accelerated gradient

  7. Drori Y, Taylor AB (2020) Efficient first-order methods for convex minimization: a constructive approach. Math Programm Ser A 184:183–220

    Article  MathSciNet  Google Scholar 

  8. Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: ICML’13 proceedings of the 30th international conference on machine learning, volume 28, pp III-1139–III-1147

  9. Carlon AG, Dia BM, Espath L, Lopez RH, Tempone R (2020) Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization. Comput Method Appl Mech Eng 363:112909

    Article  MathSciNet  Google Scholar 

  10. Donoghue BO, Candès E (2015) Adaptive restart for accelerated gradient schemes. Found Comput Math 15:715–732

    Article  MathSciNet  Google Scholar 

  11. Schneider M (2017) An fft-based fast gradient method for elastic and inelastic unit cell homogenization problems. Comput Method Appl Mech Eng 315:846–866

    Article  MathSciNet  Google Scholar 

  12. Newmark NM (1959) A method of computation for structural dynamics. J Eng Mech Div 85(EM3):67–94

    Article  Google Scholar 

  13. Stegmann J, Lund E (2005) Discrete material optimization of general composite shell structures. Int J Numer Methods Eng 62:2009–2027

    Article  Google Scholar 

  14. Lund E (2018) Discrete material and thickness optimization of laminated composite structures including failure criteria. Strut Multidisc Optim 57:2357–2375

    Article  Google Scholar 

  15. Hvejsel CF, Lund E, Stolpe M (2011) Optimization strategies for discrete multi-material stiffness optimization. Strut Multidisc Optim 44:149–163

    Article  Google Scholar 

  16. Moré JJ, Garbow BS, Hillstrom KE (1981) Testing unconstrained optimization software. ACM Trans Math 7(1):17–41

    Article  MathSciNet  Google Scholar 

  17. Fazlyab M, Robey A, Hassani H, Morari M, Pappas G (2019) Efficient and accurate estimation of Lipschitz constants for deep neural networks. In: Wallach H, Larochelle H, Beygelzimer A, dAlché F, Fox E, Garnett R (eds) Advances in neural information processing systems. Curran Associates, Red Hook, pp 11427–11438

    Google Scholar 

  18. Areias P. Simplas. http://www.simplassoftware.com. Portuguese Software Association (ASSOFT) registry number 2281/D/17

  19. Fiacco AV, McCormick GP (1968) Nonlinear programming: sequential unconstrained minimization techniques. Wiley, New York. Reprinted by SIAM Publications in 1990

  20. Apostol TM (1967) Calculus, vol 1, 2nd edn. Wiley, New York, p 443

    Google Scholar 

  21. Belytschko T, Liu WK, Moran B (2000) Nonlinear finite elements for continua and structures. Wiley, New York

    MATH  Google Scholar 

  22. Meirovitch L (2001) Fundamentals of vibrations. Mechanical engineering series. McGraw-Hill International, New York, NY

    Google Scholar 

  23. Clough RW, Penzien J (2003) Dynamics of structures, 3rd edn. Computers & Structures Inc, Berkeley, CA

    MATH  Google Scholar 

  24. Hughes TJR (2000) The finite element method. Dover Publications. Reprint of Prentice-Hall edition 1987

  25. Gilbert JC, Nocedal J (1992) Global convergence properties of conjugate gradient methods for optimization. SIAM J Optim 2(1):21–42

    Article  MathSciNet  Google Scholar 

  26. Nocedal J, Wright S (2006) Numerical optimization. Series operations research. Springer, Berlin

    MATH  Google Scholar 

  27. Dai Y, Yuan J, Yuan Y-X (2002) Modified two-point stepsize gradient methods for unconstrained optimization. Comput Optim Appl 22:103–109

    Article  MathSciNet  Google Scholar 

  28. Barzilai J, Borwein JM (1988) Two-point step size gradient methods. IMA J Numer Anal 8:141–148

    Article  MathSciNet  Google Scholar 

  29. Areias P (2020) Nesterov/Newmark optimizer at IST. https://github.com/PedroAreiasIST/NesterovNewmark

  30. Gavrilovic M, Petrovic R, Siljak D (1963) Adjoint method in sensitivity analysis of optimal systems. J Frankl Inst Eng Appl Math 276(1):26

    Article  MathSciNet  Google Scholar 

  31. Byrne CL (2013) Alternating minimization as sequential unconstrained minimization: a survey. J Optim Theory Appl 156:554–566

    Article  MathSciNet  Google Scholar 

  32. Wriggers P (2008) Nonlinear finite element methods. Springer, Berlin

    MATH  Google Scholar 

  33. Wolfram Research, Inc., Mathematica, Version 9.0, Champaign, IL (2012)

  34. Korelc J (2002) Multi-language and multi-environment generation of nonlinear finite element codes. Eng Comput 18(4):312–327

    Article  Google Scholar 

  35. Sigmund O, Torquato S (1997) Design of materials with extreme thermal expansion using a three-phase topology optimization method. J Mech Phys Solids 45(6):1037–1067

    Article  MathSciNet  Google Scholar 

  36. Lund E, Stegmann J (2005) On structural optimization of composite shell structures using a discrete constitutive parametrization. Wind Energy 8:109–124

    Article  Google Scholar 

  37. Polak E, Ribière G (1969) Note sur la convergence de méthodes de directions conjuguées. Rev Française Informat Recherche Opérationelle 1(3):35–43

    MATH  Google Scholar 

Download references

Acknowledgements

The first author acknowledges the support of FCT, through IDMEC, under LAETA, Project UIDB/50022/2020. The first author would like to thank Professor Leonel Fernandes at IST for the outstanding insight concerning time integration algorithms.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. Areias.

Additional information

Publisher's Note

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

Appendix

Appendix

See Table 4.

Table 4 First order amplification matrices (using Hughes terminology [24]) for four ODE explicit integrators

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Areias, P., Rabczuk, T. An engineering interpretation of Nesterov’s convex minimization algorithm and time integration: application to optimal fiber orientation. Comput Mech 68, 211–227 (2021). https://doi.org/10.1007/s00466-021-02027-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00466-021-02027-z

Keywords

Navigation