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An improved proximal alternating direction method for monotone variational inequalities with separable structure

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Abstract

To solve a class of variational inequalities with separable structure, this paper presents a new method to improve the proximal alternating direction method (PADM) in the following senses: an iterate generated by the PADM is utilized to generate a descent direction; and an appropriate step size along this descent direction is identified. Hence, a descent-like method is developed. Convergence of the new method is proved under mild assumptions. Some numerical results demonstrate that the new method is efficient.

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References

  1. Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation, Numerical Methods. Prentice-Hall, New York (1989)

    MATH  Google Scholar 

  2. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  3. Chen, G., Teboulle, M.: A proximal-based decomposition method for convex minimization problem. Math. Program. 64, 81–101 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dafermos, S.: Traffic equilibrium and variational inequalities. Transp. Sci. 4, 42–54 (1980)

    Article  MathSciNet  Google Scholar 

  5. Eaves, B.C.: On the basic theorem of complementarity. Math. Program. 1, 68–75 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  6. Eckstein, J.: Some saddle-function splitting methods for convex programming. Optim. Methods Softw. 4, 75–83 (1994)

    Article  MathSciNet  Google Scholar 

  7. Eckstein, J., Bertsekas, D.P.: On the Douglas-Rachford splitting method and the proximal points algorithm for maximal monotone operators. Math. Program. 55, 293–318 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  8. Eckstein, J., Fukushima, M.: Some reformulation and applications of the alternating direction method of multipliers. In: Hager, W.W., et al. (eds.) Large Scale Optimization: State of the Art, pp. 115–134. Kluwer Academic, Dordrecht (1994)

    Google Scholar 

  9. Fortin, M., Glowinski, R. (eds.): Augmented Lagrangian Methods: Applications to the solution of Boundary-Valued Problems. North-Holland, Amsterdam (1983)

    Google Scholar 

  10. Fukushima, M.: Application of the alternating direction method of multipliers to separable convex programming problems. Comput. Optim. Appl. 2, 93–111 (1992)

    MathSciNet  Google Scholar 

  11. Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite-element approximations. Comput. Math. Appl. 2, 17–40 (1976)

    Article  MATH  Google Scholar 

  12. Gabay, D.: Applications of the method of multipliers to variational inequalities. In: Fortin, M., Glowinski, R. (eds.) Augmented Lagrange Methods: Applications to the Solution of Boundary-valued Problems, pp. 299–331. North Holland, Amsterdam (1983)

    Chapter  Google Scholar 

  13. Gao, Y., Sun, D.F.: Calibrating least square covariance matrix problems with equality and inequality constraints. Manuscript, available on http://www.math.nus.edu.sg/~matsundf/

  14. Glowinski, R.: Numerical Methods for Nonlinear Variational Problems. Springer, New York, Berlin, Heidelberg, Tokyo (1984)

    MATH  Google Scholar 

  15. Glunt, W.: An alternating projections method for certain linear problems in a Hilbert space. IMA J. Numer. Anal. 15, 291–305 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  16. Golub, G.H., Van Loan, C.F.: Matrix Computation. The Johns Hopkins Press, Baltimore (1996)

    Google Scholar 

  17. Güler, O.: New proximal point algorithms for convex minimization. SIAM J. Optim. 2(4), 649–664 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hager, W.W., Zhang, H.C.: Self-adaptive inexact proximal point methods. Comput. Optim. Appl. 39(2), 161–181 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hager, W.W., Zhang, H.C.: Asymptotic convergence analysis of a new class of proximal point methods. SIAM J. Control Optim. 46(5), 1683–1704 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  20. Harker, P.T., Pang, J.S.: A damped–Newton method for the linear complementarity problem. Lect. Appl. Math. 26, 265–284 (1990)

    MathSciNet  Google Scholar 

  21. He, B.S., Yang, H., Wang, S.L.: Alternating directions method with self-adaptive parameter for monotone variational inequalities. J. Optim. Theory Appl. 106, 349–368 (2000)

    Article  MathSciNet  Google Scholar 

  22. He, B.S., Liao, L.Z., Han, D.R., Yang, H.: A new inexact alternating directions method for monotone variational inequalities. Math. Program. 92, 103–118 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  23. He, B.S., Yuan, X.M., Zhang, J.Z.: Comparison of two kinds of prediction-correction methods for monotone variational inequalities. Comput. Optim. Appl. 27(3), 247–267 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  24. Khobotov, E.N.: Modification of the extragradient method for solving variational inequalities and certain optimization problems. USSR Comput. Math. Phys. 27, 120–127 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  25. Kontogiorgis, S., Meyer, R.R.: A variable-penalty alternating directions method for convex optimization. Math. Program. 83, 29–53 (1998)

    MathSciNet  MATH  Google Scholar 

  26. Martinet, B.: Regularization d’inequations variationelles par approximations sucessives. Rev. Fr. Inform. Rech. Opér. 4, 154–159 (1970)

    MathSciNet  Google Scholar 

  27. Nagurney, A.: Network Economics, a Variational Inequality Approach. Kluwer Academic, Dordrecht (1993)

    MATH  Google Scholar 

  28. Rockafellar, R.T.: Monotone operators and the proximal point algorithm. SIAM J. Control Optim. 126, 877–898 (1976)

    Article  MathSciNet  Google Scholar 

  29. Rockafellar, R.T.: Augmented Lagrangians and applications of the proximal point algorithm in convex programming. Math. Oper. Res. 1, 97–116 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  30. Solodov, M.V., Tseng, P.: Modified projection-type methods for monotone variational inequalities. SIAM J. Control Optim. 34, 1814–1830 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  31. Sun, D.: A class of iterative methods for solving nonlinear projection equations. J. Optim. Theory Appl. 91, 123–140 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  32. Taji, K., Fukushima, M., Ibaraki, T.: A globally convergent Newton method for solving strongly monotone variational inequalities. Math. Program. 58, 369–383 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  33. Teboulle, M.: Convergence of proximal-like algorithms. SIAM J. Optim. 7, 1069–1083 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  34. Tseng, P.: Applications of splitting algorithm to decomposition in convex programming and variational inequalities. SIAM J. Control Optim. 29, 119–138 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  35. Tseng, P.: Alternating projection-proximal methods for convex programming and variational inequalities. SIAM J. Optim. 7, 951–965 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  36. Ye, C.H., Yuan, X.M.: A descent method for structured monotone variational inequalities. Optim. Methods Softw. 22(2), 329–338 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Xiaoming Yuan.

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This author was supported in part by the RGC Grant 203009 and the NSFC grant 10701055.

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Yuan, X. An improved proximal alternating direction method for monotone variational inequalities with separable structure. Comput Optim Appl 49, 17–29 (2011). https://doi.org/10.1007/s10589-009-9293-y

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  • DOI: https://doi.org/10.1007/s10589-009-9293-y

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