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On the acceleration of the Barzilai–Borwein method

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Abstract

The Barzilai–Borwein (BB) gradient method is efficient for solving large-scale unconstrained problems to modest accuracy due to its ingenious stepsize which generally yields nonmonotone behavior. In this paper, we propose a new stepsize to accelerate the BB method by requiring finite termination for minimizing the two-dimensional strongly convex quadratic function. Based on this new stepsize, we develop an efficient gradient method for quadratic optimization which adaptively takes the nonmonotone BB stepsizes and certain monotone stepsizes. Two variants using retard stepsizes associated with the new stepsize are also presented. Numerical experiments show that our strategies of properly inserting monotone gradient steps into the nonmonotone BB method could significantly improve its performance and our new methods are competitive with the most successful gradient descent methods developed in the recent literature.

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References

  1. Akaike, H.: On a successive transformation of probability distribution and its application to the analysis of the optimum gradient method. Ann. Inst. Stat. Math. 11(1), 1–16 (1959)

    Article  MathSciNet  Google Scholar 

  2. Barzilai, J., Borwein, J.M.: Two-point step size gradient methods. IMA J. Numer. Anal. 8(1), 141–148 (1988)

    Article  MathSciNet  Google Scholar 

  3. Cauchy, A.: Méthode générale pour la résolution des systemes déquations simultanées. Comp. Rend. Sci. Paris 25, 536–538 (1847)

    Google Scholar 

  4. Dai, Y.H.: Alternate step gradient method. Optimization 52(4–5), 395–415 (2003)

    Article  MathSciNet  Google Scholar 

  5. Dai, Y.H., Fletcher, R.: Projected Barzilai–Borwein methods for large-scale box-constrained quadratic programming. Numer. Math. 100(1), 21–47 (2005)

    Article  MathSciNet  Google Scholar 

  6. Dai, Y.H., Huang, Y., Liu, X.W.: A family of spectral gradient methods for optimization. Comp. Optim. Appl. 74(1), 43–65 (2019)

    Article  MathSciNet  Google Scholar 

  7. Dai, Y.H., Kou, C.X.: A Barzilai–Borwein conjugate gradient method. Sci. China Math. 59(8), 1511–1524 (2016)

    Article  MathSciNet  Google Scholar 

  8. Dai, Y.H., Liao, L.Z.: \(R\)-linear convergence of the Barzilai and Borwein gradient method. IMA J. Numer. Anal. 22(1), 1–10 (2002)

    Article  MathSciNet  Google Scholar 

  9. Dai, Y.H., Yuan, Y.X.: Alternate minimization gradient method. IMA J. Numer. Anal. 23(3), 377–393 (2003)

    Article  MathSciNet  Google Scholar 

  10. Dai, Y.H., Yuan, Y.X.: Analysis of monotone gradient methods. J. Ind. Manag. Optim. 1(2), 181–192 (2005)

    MathSciNet  MATH  Google Scholar 

  11. De Asmundis, R., Di Serafino, D., Hager, W.W., Toraldo, G., Zhang, H.: An efficient gradient method using the yuan steplength. Comp. Optim. Appl. 59(3), 541–563 (2014)

    Article  MathSciNet  Google Scholar 

  12. Di Serafino, D., Ruggiero, V., Toraldo, G., Zanni, L.: On the steplength selection in gradient methods for unconstrained optimization. Appl. Math. Comput. 318, 176–195 (2018)

    MathSciNet  MATH  Google Scholar 

  13. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)

    Article  MathSciNet  Google Scholar 

  14. Fletcher, R.: On the Barzilai–Borwein method. In: Qi, L., Teo, K., Yang, X. (eds.) Optimization and Control with Applications, pp. 235–256. Springer, Boston (2005)

    Chapter  Google Scholar 

  15. Fletcher, R.: A limited memory steepest descent method. Math. Program. 135(1–2), 413–436 (2012)

    Article  MathSciNet  Google Scholar 

  16. Hestenes, M.R., Stiefel, E.: Method of conjugate gradient for solving linear system. J. Res. Nat. Bur. Stand. 49, 409–436 (1952)

    Article  MathSciNet  Google Scholar 

  17. Forsythe, G.E.: On the asymptotic directions of the s-dimensional optimum gradient method. Numer. Math. 11(1), 57–76 (1968)

    Article  MathSciNet  Google Scholar 

  18. Frassoldati, G., Zanni, L., Zanghirati, G.: New adaptive stepsize selections in gradient methods. J. Ind. Manag. Optim. 4(2), 299–312 (2008)

    MathSciNet  MATH  Google Scholar 

  19. Friedlander, A., Martínez, J.M., Molina, B., Raydan, M.: Gradient method with retards and generalizations. SIAM J. Numer. Anal. 36(1), 275–289 (1998)

    Article  MathSciNet  Google Scholar 

  20. Golub, G.H., Van Loan, C.F.: Matrix Computations, 4th edn. Johns Hopkins University Press, Baltimore, Maryland (2013)

    MATH  Google Scholar 

  21. Gonzaga, C.C., Schneider, R.M.: On the steepest descent algorithm for quadratic functions. Comp. Optim. Appl. 63(2), 523–542 (2016)

    Article  MathSciNet  Google Scholar 

  22. Huang, Y., Dai, Y.H., Liu, X.W., Zhang, H.: Gradient methods exploiting spectral properties. Optim. Method Softw. 35(4), 681–705 (2020)

    Article  MathSciNet  Google Scholar 

  23. Huang, Y., Dai, Y.H., Liu, X.W., Zhang, H.: On the asymptotic convergence and acceleration of gradient methods. J. Sci. Comput. 90, 7 (2022)

    Article  MathSciNet  Google Scholar 

  24. Huang, Y., Liu, H., Zhou, S.: Quadratic regularization projected Barzilai–Borwein method for nonnegative matrix factorization. Data Min. Knowl. Disc. 29(6), 1665–1684 (2015)

    Article  MathSciNet  Google Scholar 

  25. Jiang, B., Dai, Y.H.: Feasible Barzilai–Borwein-like methods for extreme symmetric eigenvalue problems. Optim. Method Softw. 28(4), 756–784 (2013)

    Article  MathSciNet  Google Scholar 

  26. Liu, Y.F., Dai, Y.H., Luo, Z.Q.: Coordinated beamforming for miso interference channel: complexity analysis and efficient algorithms. IEEE Trans. Signal Process. 59(3), 1142–1157 (2011)

    Article  MathSciNet  Google Scholar 

  27. Raydan, M.: On the Barzilai and Borwein choice of steplength for the gradient method. IMA J. Numer. Anal. 13(3), 321–326 (1993)

    Article  MathSciNet  Google Scholar 

  28. Raydan, M.: The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem. SIAM J. Optim. 7(1), 26–33 (1997)

    Article  MathSciNet  Google Scholar 

  29. Tan, C., Ma, S., Dai, Y.H., Qian, Y.: Barzilai–Borwein step size for stochastic gradient descent. In: Advances in Neural Information Processing Systems, pp. 685–693 (2016)

  30. Davis, T.A., Hu, Y.: The university of Florida sparse matrix collection. ACM Trans. Math. Softw. 38(1), 1–25 (2011)

    MathSciNet  MATH  Google Scholar 

  31. Wang, Y., Ma, S.: Projected Barzilai–Borwein method for large-scale nonnegative image restoration. Inverse Probl. Sci. En. 15(6), 559–583 (2007)

    Article  MathSciNet  Google Scholar 

  32. Wright, S.J., Nowak, R.D., Figueiredo, M.A.: Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57(7), 2479–2493 (2009)

    Article  MathSciNet  Google Scholar 

  33. Yuan, Y.X.: A new stepsize for the steepest descent method. J. Comput. Math. 24(2), 149–156 (2006)

    MathSciNet  MATH  Google Scholar 

  34. Yuan, Y.X.: Step-sizes for the gradient method. AMS IP Stud. Adv. Math. 42(2), 785–796 (2008)

    MathSciNet  MATH  Google Scholar 

  35. Zhou, B., Gao, L., Dai, Y.H.: Gradient methods with adaptive step-sizes. Comp. Optim. Appl. 35(1), 69–86 (2006)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors would like to thank the associate editor and the anonymous referees for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (Grant Nos. 11701137, 11631013, 12071108, 11671116, 11991021, 12021001), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA27000000), Beijing Academy of Artificial Intelligence (BAAI), China Scholarship Council (No. 201806705007), Natural Science Foundation of Hebei Province (Grant No. A2021202010), and USA National Science Foundation (Grant Nos. 2110722, 1819161).

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Correspondence to Yakui Huang.

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Huang, Y., Dai, YH., Liu, XW. et al. On the acceleration of the Barzilai–Borwein method. Comput Optim Appl 81, 717–740 (2022). https://doi.org/10.1007/s10589-022-00349-z

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  • DOI: https://doi.org/10.1007/s10589-022-00349-z

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