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Nonlinear Stepsize Control Algorithms: Complexity Bounds for First- and Second-Order Optimality

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Abstract

A nonlinear stepsize control (NSC) framework has been proposed by Toint (Optim Methods Softw 28:82–95, 2013) for unconstrained optimization, generalizing several trust-region and regularization algorithms. More recently, worst-case complexity bounds to achieve approximate first-order optimality were proved by Grapiglia, Yuan and Yuan (Math Program 152:491–520, 2015) for the generic NSC framework. In this paper, improved complexity bounds for first-order optimality are obtained. Furthermore, complexity bounds for second-order optimality are also provided.

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Notes

  1. The inequality in (6) typically results from an error bound on Taylor series and a bounded \(H_{k}\).

  2. For details, see Section 2 in [3].

References

  1. Shultz, G.A., Schnabel, R.B., Byrd, R.H.: A family of trust-region-based algorithms for unconstrained minimization with strong global convergence properties. SIAM J. Numer. Anal. 22, 47–67 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  2. Toint, PhL: Global convergence of a class of trust region methods for nonconvex minimization in Hilbert space. IMA J. Numer. Anal. 8, 231–252 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  3. Toint, PhL: Nonlinear stepsize control, trust regions and regularizations for unconstrained optimization. Optim. Methods Softw. 28, 82–95 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Conn, A.R., Gould, N.I.M., Toint, PhL: Trust-Region Methods. SIAM, Philadelphia (2000)

    Book  MATH  Google Scholar 

  5. Powell, M.J.D.: Convergence properties of a class of minimization algorithms. In: Mangasarian, O.L., Meyer, R.R., Robinson, S.M. (eds.) Nonlinear Programming 2. Academic Press, New York (1975)

    Google Scholar 

  6. Cartis, C., Gould, N.I.M., Toint, PhL: Adaptive cubic overestimation methods for unconstrained optimization. Part I: motivation, convergence and numerical results. Math. Program. 127, 245–295 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cartis, C., Gould, N.I.M., Toint, PhL: Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function-and derivative-evaluation complexity. Math. Program. 130, 295–319 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  8. Nesterov, Yu.: Modified Gauss–Newton scheme with worst-case guarantees for global performance. Optim. Methods Softw. 22, 469–483 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Bellavia, S., Cartis, C., Gould, N.I.M., Morini, B., Toint, PhL: Convergence of a regularized euclidean residual algorithm for nonlinear least-squares. SIAM J. Numer. Anal. 48, 1–29 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Fan, J., Yuan, Y.: A new trust region algorithm with trust region radius converging to zero. In: Li, D. (ed.) Proceeding of the 5th International Conference on Optimization: Techiniques and Applications, pp. 786–794. Hong Kong (2001)

  11. Zhang, J., Wang, Y.: A new trust region method for nonlinear equations. Math. Methods Oper. Res. 58, 283–298 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  12. Fan, J.: Convergence rate of the trust region method for nonlinear equations under local error bound condition. Comput. Optim. Appl. 34, 215–227 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  13. Lu, X., Ni, Q.: A quasi-Newton trust-region method with a new conic model for the unconstrained optimization. Appl. Math. Comput. 204, 373–384 (2008)

    MathSciNet  MATH  Google Scholar 

  14. Grapiglia, G.N., Yuan, J., Yuan, Y.: On the worst-case complexity of nonlinear stepsize control algorithms for convex unconstrained optimization. Optim. Methods Softw. 31, 591–604 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  15. Grapiglia, G.N., Yuan, J., Yuan, Y.: On the convergence and worst-case complexity of trust-region and regularization methods for unconstrained optimization. Math. Program. 152, 491–520 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  16. Powell, M.J.D.: On the global convergence of trust region algorithms for unconstrained minimization. Math. Program. 29, 297–303 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  17. Cartis, C., Gould, N.I.M., Toint, P.L.: Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients. Technical Report NA-14-21, Mathematical Institute, University of Oxford (2014)

  18. Cartis, C., Gould, N.I.M., Toint, PhL: Complexity bounds for second-order optimality in unconstrained optimization. J. Complex. 28, 93–108 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  19. Nesterov, Yu.: Introductory Lectures on Convex Optimization. Applied Optimization. Kluwer, Dordrecht (2004)

    Book  MATH  Google Scholar 

  20. Gratton, S., Sartenaer, A., Toint, Ph L.: Recursive trust-region methods for multiscale nonlinear optimization. SIAM J. Optim. 19, 414–444 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. Cartis, C., Sampaio, PhR, Toint, PhL: Worst-case evaluation complexity of non-monotone gradient-related algorithms for unconstrained optimization. Optimization 64, 1349–1361 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  22. Ueda, K., Yamashita, N.: On a global complexity bound of the Levenberg–Marquardt method. J. Optim. Theory Appl. 147, 443–453 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  23. Nesterov, Yu., Polyak, B.T.: Cubic regularization of Newton method and its global performance. Math. Program. 108, 177–205 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  24. Curtis, F.E., Robinson, D.P., Samadi, M.: A trust region algorithm with a worst-case iteration complexity of \(O(\epsilon ^{-3/2})\) for nonconvex optimization. Math. Program. doi:10.1007/s10107-016-1026-2

  25. Martínez, J.M., Raydan, M.: Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization (2015). http://www.optimization-online.org/DB_FILE/2015/11/5203.pdf

  26. Birgin, E.G., Gardenghi, J.L., Martínez, J.M., Toint, Ph.L.: Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models. Technical Report naXys-05-2015, Namur Center for Complex Systems (naXys), University of Namur, Namur, Belgium (2015)

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Acknowledgments

The authors are grateful to two anonymous referees for their valuable comments and suggestions to improve the paper. G.N. Grapiglia was supported by the Coordination for Improvement of Higher Education Personnel (CAPES), Brazil. J. Yuan was partially supported by the Coordination of the Improvement of Higher Education Personnel (CAPES) and by the National Council for Scientific and Technological Development (CNPq), Brazil. Y. Yuan was partially supported by the Natural Science Foundation of China (NSFC), China.

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Correspondence to Geovani Nunes Grapiglia.

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Communicated by Johannes O. Royset.

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Grapiglia, G.N., Yuan, J. & Yuan, Yx. Nonlinear Stepsize Control Algorithms: Complexity Bounds for First- and Second-Order Optimality. J Optim Theory Appl 171, 980–997 (2016). https://doi.org/10.1007/s10957-016-1007-x

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