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Convergence analysis of two-step inertial Douglas-Rachford algorithm and application

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Abstract

Monotone inclusion problems are crucial to solve engineering problems and problems arising in different branches of science. In this paper, we propose a novel two-step inertial Douglas-Rachford algorithm to solve the monotone inclusion problem of the sum of two maximally monotone operators based on the normal S-iteration method (Sahu, D.R.: Applications of the S-iteration process to constrained minimization problems and split feasibility problems. Fixed Point Theory 12(1), 187–204 (2011)). We have studied the convergence behavior of the proposed algorithm. Based on the proposed method, we develop an inertial primal-dual algorithm to solve highly structured monotone inclusions containing the mixtures of linearly composed and parallel-sum type operators. Finally, we apply the proposed inertial primal-dual algorithm to solve a highly structured minimization problem. We also perform a numerical experiment to solve the generalized Heron problem and compare the performance of the proposed inertial primal-dual algorithm to already known algorithms.

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References

  1. Agarwal, R. P., O’Regan, D., and Sahu, D. R. (2009). Fixed point theory for Lipschitzian-type mappings with applications (Vol. 6, pp. x+-368). New York: Springer

  2. Alvarez, F., Attouch, H.: An inertial proximal method for maximal monotone operators via discretization of a nonlinear oscillator with damping. Set-Valued Anal. 9(1–2), 3–11 (2001)

    Article  MathSciNet  Google Scholar 

  3. Alvarez, F.: Weak convergence of a relaxed and inertial hybrid projection-proximal point algorithm for maximal monotone operators in Hilbert space. SIAM J. Optim. 14(3), 773–782 (2004)

    Article  MathSciNet  Google Scholar 

  4. Attouch, H., Peypouquet, J.: The rate of convergence of Nesterov’s accelerated forward-backward method is actually faster than \(1/k^2\). SIAM J. Optim. 26(3), 1824–1834 (2016)

    Article  MathSciNet  Google Scholar 

  5. Baillon, J.B., Haddad, G.: Quelques propriétés des opérateurs angle-bornés etn-cycliquement monotones. Israel J. Math. 26, 137–150 (1977)

    Article  MathSciNet  Google Scholar 

  6. Bauschke, H.H., Combettes, P.L.: Convex analysis and monotone operator theory in Hilbert spaces, vol. 408. Springer, Berlin (2011)

    Book  Google Scholar 

  7. Bauschke, H.H., Dao, M.N., Noll, D., Phan, H.M.: Proximal point algorithm, Douglas-Rachford algorithm and alternating projections: a case study. J. Convex. Anal. 23(1), 237–261 (2016)

    MathSciNet  MATH  Google Scholar 

  8. Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)

    Article  MathSciNet  Google Scholar 

  9. Bennar, A., Monnez, J.M.: Almost sure convergence of a stochastic approximation process in a convex set. Int. J. Apll. Math 20(5), 713–722 (2007)

    MathSciNet  MATH  Google Scholar 

  10. Bot, R.I., Hendrich, C.: A Douglas-Rachford type primal-dual method for solving inclusions with mixtures of composite and parallel-sum type monotone operators. SIAM J. Optim. 23(4), 2541–2565 (2013)

    Article  MathSciNet  Google Scholar 

  11. Boţ, R.I., Csetnek, E.R., Hendrich, C.: Inertial Douglas-Rachford splitting for monotone inclusion problems. Appl. Math. Comput. 256, 472–487 (2015)

    MathSciNet  MATH  Google Scholar 

  12. Boyd, S., Parikh, N., Chu, E.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc (2011)

  13. Bruck, R.E., Jr.: On the weak convergence of an ergodic iteration for the solution of variational inequalities for monotone operators in Hilbert space. J. Math. Anal. Appl. 61(1), 159–164 (1977)

    Article  MathSciNet  Google Scholar 

  14. Chambolle, A., Dossal, C.: On the convergence of the iterates of the fast iterative shrinkage/thresholding algorithm. J. Optim. Theory Appl. 166(3), 968–982 (2015)

    Article  MathSciNet  Google Scholar 

  15. Cholamjiak, W., Cholamjiak, P., Suantai, S.: An inertial forward-backward splitting method for solving inclusion problems in Hilbert spaces. J. Fixed Point Theory Appl. 20(1), 1–17 (2018)

    Article  MathSciNet  Google Scholar 

  16. Combettes, P.L., Pesquet, J.C.: Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators. Set-Valued Var. Anal. 20(2), 307–330 (2012)

    Article  MathSciNet  Google Scholar 

  17. Dao, M.N., Phan, H.M.: Adaptive Douglas-Rachford splitting algorithm for the sum of two operators. SIAM J. Optim. 29(4), 2697–2724 (2019)

    Article  MathSciNet  Google Scholar 

  18. Dixit, A., Sahu, D.R., Singh, A.K., Som, T.: Application of a new accelerated algorithm to regression problems. Soft Comput. 24(2), 1539–1552 (2020)

    Article  Google Scholar 

  19. Dong, Y.: New inertial factors of the Krasnosel’skii-Mann iteration. Set-Valued and Variational Analysis 1–17, (2020)

  20. Douglas, J., Rachford, H.H.: On the numerical solution of heat conduction problems in two and three space variables. Trans. Am. Math. Soc. 82(2), 421–439 (1956)

    Article  MathSciNet  Google Scholar 

  21. Eckstein, J.: Splitting methods for monotone operators with applications to parallel optimization (Doctoral dissertation, Massachusetts Institute of Technology) (1989)

  22. Levitin, E.S., Polyak, B.T.: Constrained minimization methods. USSR Comput. Math. Math. Phys. 6(5), 1–50 (1966)

    Article  Google Scholar 

  23. Li, G., Pong, T.K.: Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems. Math. Progr. 159(1–2), 371–401 (2016)

    Article  MathSciNet  Google Scholar 

  24. Lieutaud, J.: Approximations d’opérateurs monotones par des méthodes de splitting, doctoral thesis, University of Paris (1969)

  25. Lions, P.L., Mercier, B.: Splitting algorithms for the sum of two nonlinear operators. SIAM J. Numer. Anal. 16(6), 964–979 (1979)

    Article  MathSciNet  Google Scholar 

  26. Lorenz, D.A., Pock, T.: An inertial forward-backward algorithm for monotone inclusions. J. Math. Imaging Vis. 51(2), 311–325 (2015)

    Article  MathSciNet  Google Scholar 

  27. Luke, D.R., Martins, A.L.: Convergence analysis of the relaxed Douglas-Rachford algorithm. SIAM J. Optim. 30(1), 542–584 (2020)

    Article  MathSciNet  Google Scholar 

  28. Mann, W.R.: Mean value methods in iteration. Proc. Am. Math. Soc. 4(3), 506–510 (1953)

    Article  MathSciNet  Google Scholar 

  29. Maingé, P.E.: Convergence theorems for inertial KM-type algorithms. J. Comput. Appl. Math. 219(1), 223–236 (2008)

    Article  MathSciNet  Google Scholar 

  30. Minty, G.J.: Monotone (Nonlinear) operators in Hilbert space. Duke Math. J. 29, 341–346 (1962)

    Article  MathSciNet  Google Scholar 

  31. Mordukhovich, B.S., Nam, N.M., Salinas, J.: Applications of variational analysis to a generalized Heron problem. Appl. Anal. 91(10), 1915–1942 (2012)

    Article  MathSciNet  Google Scholar 

  32. Mordukhovich, B.S., Nam, N.M., Salinas, J.: Solving a generalized Heron problem by means of convex analysis. Am. Math. Mon. 119(2), 87–99 (2012)

    Article  MathSciNet  Google Scholar 

  33. Opial, Z.: Weak convergence of the sequence of successive approximations for nonexpansive mappings. Bulletin of the American Mathematical Society 73(4), 591–597 (1967)

    Article  MathSciNet  Google Scholar 

  34. Phan, H.M.: Linear convergence of the Douglas-Rachford method for two closed sets. Optimization 65(2), 369–385 (2016)

    Article  MathSciNet  Google Scholar 

  35. Polyak, B.T.: Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics 4(5), 1–17 (1964)

    Article  Google Scholar 

  36. Sahu, D.R.: Applications of the S-iteration process to constrained minimization problems and split feasibility problems. Fixed Point Theory 12(1), 187–204 (2011)

    MathSciNet  MATH  Google Scholar 

  37. Svaiter, B.F.: On weak convergence of the Douglas-Rachford method. SIAM Journal on Control and Optimization 49(1), 280–287 (2011)

    Article  MathSciNet  Google Scholar 

  38. Vũ, B.C.: A splitting algorithm for dual monotone inclusions involving cocoercive operators. Advances in Computational Mathematics 38(3), 667–681 (2013)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

Avinash Dixit express thanks to IIT(BHU) for the fellowship in form of Teaching Assistantship. UGC, India is acknowledged gratefully by Pankaj Gautam for senior research fellowship.

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Correspondence to Pankaj Gautam.

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Dixit, A., Sahu, D.R., Gautam, P. et al. Convergence analysis of two-step inertial Douglas-Rachford algorithm and application. J. Appl. Math. Comput. 68, 953–977 (2022). https://doi.org/10.1007/s12190-021-01554-5

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