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On the convergence of the entropy-exponential penalty trajectories and generalized proximal point methods in semidefinite optimization

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Abstract

The convergence of primal and dual central paths associated to entropy and exponential functions, respectively, for semidefinite programming problem are studied in this paper. It is proved that the primal path converges to the analytic center of the primal optimal set with respect to the entropy function, the dual path converges to a point in the dual optimal set and the primal-dual path associated to this paths converges to a point in the primal-dual optimal set. As an application, the generalized proximal point method with the Kullback-Leibler distance applied to semidefinite programming problems is considered. The convergence of the primal proximal sequence to the analytic center of the primal optimal set with respect to the entropy function is established and the convergence of a particular weighted dual proximal sequence to a point in the dual optimal set is obtained.

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References

  1. Aulender A., Teboulle M.: Interior gradient and proximal methods for convex and conic optimization. SIAM J. Optim. 16(3), 697–725 (2006)

    Article  Google Scholar 

  2. Aulender A., Héctor Ramírez C.: Penalty and barrier methods for convex semidefinite programming. Math. Methods Oper. Res. 63, 195–219 (2006)

    Article  Google Scholar 

  3. Bolte J., Daniilidis A., Lewis A.: The Lojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems. SIAM J. Optim. 17(4), 1205–1223 (2007)

    Article  Google Scholar 

  4. Cominetti R., San Martín J.: Asymptotic analysis of the exponential penalty trajectory in linear programming. Math. Program. 67(2), 169–187 (1994)

    Article  Google Scholar 

  5. da Cruz Neto J.X., Ferreira O.P., Monteiro R.D.C.: asymptotic behavior of the central path for a special class of degenerate SDP problems. Math. Program. 103(3), 487–514 (2005)

    Article  Google Scholar 

  6. da Cruz Neto J.X., Iusem A.N., Ferreira O.P., Monteiro R.D.C: Dual convergence of the proximal point method with Bregman distances for linear programming. Optim. Methods Softw. Engl. 22, 339–360 (2007)

    Article  Google Scholar 

  7. Dieudonné J.A.: Foundations of Modern Analysis. AcademicPress, New York (1960)

    Google Scholar 

  8. Doljansky M., Teboulle M.: An interior proximal algorithm and the exponential multiplier method for semidefinite programming. SIAM J. Optim. 9(1), 1–13 (1998)

    Article  Google Scholar 

  9. Dym, H.: Linear algebra in action. Graduate Studies in Mathematics 78, AMS 2007, Providence, RI

  10. Halická M., de Klerk E., Roos C.: On the convergence of the central path in semidefinite optimization. SIAM J. Optim. 12(4), 1090–1099 (2002)

    Article  Google Scholar 

  11. Halická M., de Klerk E., Roos C.: Limiting behavior of the central path in semidefinite optimization. Optim. Methods Softw. 20(1), 99–113 (2005)

    Article  Google Scholar 

  12. Horn R.A., Johnson C.R.: Matrix Analysis. Cambridge University Press, New York (1985)

    Google Scholar 

  13. Iusem A.: Augmented Lagrangian methods and proximal point methods for convex optimization. Investigación Operativa 8, 11–49 (1999)

    Google Scholar 

  14. Iusem A.N., Monteiro R.D.C.: On dual convergence of the generalized proximal point method with Bregman distances. Math. Oper. Res. 25(4), 606–624 (2000)

    Article  Google Scholar 

  15. Iusem A.N., Svaiter B.F., da Cruz Neto J.X.: Central paths, generalized proximal point methods, and cauchy trajectories in Riemannian manifolds. SIAM J. Control Optim. 37(2), 566–588 (1999)

    Article  Google Scholar 

  16. Jensen D.L., Polyak R.A.: The convergence of a modified barrier method for convex programming. IBM J. Res. Dev. 38(3), 307–321 (1994)

    Article  Google Scholar 

  17. de Klerk E.: Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications, Applied Optimization, Vol. 65. Kluwer Academic Publishers, Dordrecht (2002)

    Google Scholar 

  18. Kojima M., Meggido N., Noma T., Yoshise A.: A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems, Lecture Notes in Computer Science, Vol. 538. Springer Verlag, Berlin, Germany (1991)

    Google Scholar 

  19. Krantz S., Parks H.R.: A Primer of Real Analytic Functions. Birkhäuser Verlag, Boston (1992)

    Google Scholar 

  20. Kurdyka K., Mostowski T., Parusinski A.: Proof of the gradient conjecture of R.Thom. Annals Math. II Ser. 152(3), 763–792 (2000)

    Article  Google Scholar 

  21. Lojasiewicz, S.: Ensembles Semi-analitiques. I.H.E.S., Bures-sur-Yvette (1965)

  22. Mosheyev L., Zibulevski M.: Penalty/barrier multiplier algorithm for semidefinite programming. Optim. Methods Softw. 13, 235–261 (2000)

    Article  Google Scholar 

  23. Papa Quiroz E.A., Roberto Oliveira P.: A new barrier for a class of semidefinte problems. RAIRO-Oper. Res. 40, 303–323 (2006)

    Article  Google Scholar 

  24. Polyak R., Teboulle M.: Nonlinear rescaling and proximal-like methods in convex optimization. Math. Program. 76(2), 265–284 (1997)

    Article  Google Scholar 

  25. Powell M.J.D.: Some convergence properties of the modified log barrier method for linear programming. SIAM J. Optim. 5(4), 695–739 (1995)

    Article  Google Scholar 

  26. Shiota M.: Geometry of Subanalytic and Semialgebraics Sets. Birkhäuser, Boston (1997)

    Google Scholar 

  27. Sporre, G., Forsgren, A.: Characterization of the limit point of the central path in semidefinite programming. Technical Report TRITA-MAT-2002-OS12, Departament of Mathematics, Royal Institute of Technology, SE-100 44 Stockholm, Sweden, June 2002

  28. Todd M.J.: Semidefinite optimization. Acta Numerica 10, 515–560 (2001)

    Article  Google Scholar 

  29. Tseng P., Bertsekas D.P.: On the convergence of the exponential multiplier method for convex programming. Math. Program. 60(1), 1–19 (1993)

    Article  Google Scholar 

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Ferreira, O.P., Oliveira, P.R. & Silva, R.C.M. On the convergence of the entropy-exponential penalty trajectories and generalized proximal point methods in semidefinite optimization. J Glob Optim 45, 211–227 (2009). https://doi.org/10.1007/s10898-008-9367-x

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