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On Globally Q-Linear Convergence of a Splitting Method for Group Lasso

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Abstract

In this paper, we discuss a splitting method for group Lasso. By assuming that the sequence of the step lengths has positive lower bound and positive upper bound (unrelated to the given problem data), we prove its Q-linear rate of convergence of the distance sequence of the iterates to the solution set. Moreover, we make comparisons with convergence of the proximal gradient method analyzed very recently.

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References

  1. Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B Stat. Methodol. 68, 49–67 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  2. Friedman, J., Hastie, T., Höfling, H., Tibshirani, R.: Pathwise coordinate optimization. Ann. Appl. Stat. 1, 302–332 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Liu, J., Yuan, L., Ye, J.: An efficient algorithm for a class of fused Lasso problems. In: The ACM SIG Knowledge Discovery and Data Mining. ACM, Washington, DC (2010)

  4. Bakin, S.: Adaptive Regression and Model Selection in Data Mining Problems. Ph.D. Thesis. Australian National University, Canberra (1999)

  5. Tibshirani, R., Saunders, M.: Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 91–108 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  6. Tseng, P.: Approximation accuracy, gradient methods, and error bound for structured convex optimization. Math. Program. 125(2), 263–295 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  7. Zhang, H.B., Wei, J., Li, M., Zhou, J., Chao, M.: On proximal gradient method for the convex problems regularized with the group reproducing kernel norm. J. Global Optim. 58, 169–188 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  8. Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward–backward splitting. Multiscale Model Simul. 4, 1168–1200 (2005)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Hager, W.W., Phan, D.T., Zhang, H.C.: Gradient-based methods for sparse recovery. SIAM J. Imaging Sci. 4(1), 146–165 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  11. Garrifos, G., Rosasco, L., Villa, S.: Convergence of the forward–backward algorithm: beyond the worst-case with the help of geometry. arxiv:1703.09477v2 (2017)

  12. Luo, Z.Q., Tseng, P.: On the linear convergence of descent methods for convex essentially smooth minimization. SIAM J. Control Optim. 30(2), 408–425 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  13. Zhang, H.B., Jiang, J., Luo, Z.Q.: On the linear convergence of a proximal gradient method for a class of nonsmooth convex minimization problems. J. Oper. Res. Soc. China 1(2), 163–186 (2013)

    Article  MATH  Google Scholar 

  14. Minty, G.J.: On the monotonicity of the gradient of a convex function. Pac. J. Math. 14, 243–247 (1964)

    Article  MathSciNet  MATH  Google Scholar 

  15. Huang, Y.Y., Dong, Y.D.: New properties of forward-backward splitting and a practical proximal-descent algorithm. Appl. Math. Comput. 237, 60–68 (2014)

    MathSciNet  MATH  Google Scholar 

  16. Dong, Y.D.: An LS-free splitting method for composite mappings. Appl. Math. Lett. 18, 843–848 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  17. Irschara, A., Zach, C., Klopschitz, M., Bischof, H.: Large-scale, dense city reconstruction from user-contributed photos. Comput. Vis. Image Underst. 116, 2–15 (2012)

    Article  Google Scholar 

  18. Alotaibi, A., Combettes, P.L., Shahzad, N.: Solving coupled composite monotone inclusions by successive Fejér approximations of their Kuhn–Tucker set. SIAM J. Optim. 24(4), 2076–2095 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Latafat, P., Patrinos, P.: Asymmetric forward-backward-adjoint splitting for solving monotone inclusions involving three operators. Comput. Optim. Appl. 68(1), 57–93 (2017)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors thank the anonymous referees for their patient and valuable comments, which improved the quality of this paper. Special thanks go to Ying-Yi Li for pointing out the references [1,2,3].

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Correspondence to Yun-Da Dong.

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This research was supported by the National Natural Science Foundation of China (No. 61179033), and Collaborative Innovation Center on Beijing Society-Building and Social Governance.

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Dong, YD., Zhang, HB. & Gao, H. On Globally Q-Linear Convergence of a Splitting Method for Group Lasso. J. Oper. Res. Soc. China 6, 445–454 (2018). https://doi.org/10.1007/s40305-017-0176-0

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  • DOI: https://doi.org/10.1007/s40305-017-0176-0

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