Skip to main content
Log in

Circumcentering the Douglas–Rachford method

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

We introduce and study a geometric modification of the Douglas–Rachford method called the Circumcentered–Douglas–Rachford method. This method iterates by taking the intersection of bisectors of reflection steps for solving certain classes of feasibility problems. The convergence analysis is established for best approximation problems involving two (affine) subspaces and both our theoretical and numerical results compare favorably to the original Douglas–Rachford method. Under suitable conditions, it is shown that the linear rate of convergence of the Circumcentered–Douglas–Rachford method is at least the cosine of the Friedrichs angle between the (affine) subspaces, which is known to be the sharp rate for the Douglas–Rachford method. We also present a preliminary discussion on the Circumcentered–Douglas–Rachford method applied to the many set case and to examples featuring non-affine convex sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Aragón Artacho, F.J., Borwein, J.M., Tam, M.K.: Recent results on Douglas–Rachford methods for combinatorial optimization problems. J. Optim. Theory Appl. 163(1), 1–30 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  2. Aragón Artacho, F.J., Borwein, J.M., Tam, M.K.: Global behavior of the Douglas–Rachford method for a nonconvex feasibility problem. J. Glob. Optim. 65(2), 309–327 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  3. Aragón Artacho, F.J., Borwein, J.M.: Global convergence of a non-convex Douglas–Rachford iteration. J. Glob. Optim. 57(3), 753–769 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bauschke, H.H., Bello Cruz, J.Y., Nghia, T.T.A., Phan, H.M., Wang, X.: The rate of linear convergence of the Douglas–Rachford algorithm for subspaces is the cosine of the Friedrichs angle. J. Approx. Theory 185, 63–79 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bauschke, H.H., Bello Cruz, J.Y., Nghia, T.T.A., Phan, H.M., Wang, X.: Optimal rates of linear convergence of relaxed alternating projections and generalized Douglas-Rachford methods for two subspaces. Numer. Algor. 73(1), 33–76 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bauschke, H.H., Borwein, J.M.: On projection algorithms for solving convex feasibility problems. Siam Rev. 38(3), 367–426 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bauschke, H.H., Combettes, P. L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 1 edn. CMS Books in Mathematics. Springer International Publishing, Cham (2017)

    Book  Google Scholar 

  8. Bauschke, H.H., Moursi, W.M.: On the Douglas–Rachford algorithm. Math. Program. 164(1-2), 263–284 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  9. Bauschke, H.H., Noll, D.: On the local convergence of the Douglas–Rachford algorithm. Arch. Math. 102(6), 589–600 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  10. Bauschke, H.H., Noll, D., Phan, H.M.: Linear and strong convergence of algorithms involving averaged nonexpansive operators. J. Math. Anal. Appl. 421(1), 1–20 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  11. Benoist, J.: The Douglas–Rachford algorithm for the case of the sphere and the line. J. Glob. Optim. 63(2), 363–380 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B.: Julia: A fresh approach to numerical computing. Siam Rev. 59(1), 65–98 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  13. Borwein, J.M., Sims, B.: The Douglas–Rachford algorithm in the absence of convexity. In: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 93–109. Springer, New York (2011)

  14. Borwein, J.M., Tam, M.K.: A cyclic Douglas–Rachford iteration scheme. J. Optim. Theory Appl. 160(1), 1–29 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Censor, Y., Cegielski, A.: Projection methods: an annotated bibliography of books and reviews. Optimization 64(11), 2343–2358 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  16. Cimmino, G.: Calcolo approssimato per le soluzioni dei sistemi di equazioni lineari. Ric. Sci. 9(II), 326–333 (1938)

    MATH  Google Scholar 

  17. Combettes, P.L.: The foundations of set theoretic estimation. Proc. IEEE 81 (2), 182–208 (1993)

    Article  Google Scholar 

  18. Deutsch, F.R.: Rate of convergence of the method of alternating projections. In: Brosowski, B., Deutsch, F. R. (eds.) Parametric Optimization and Approximation: Conference Held at the Mathematisches Forschungsinstitut, Oberwolfach, October 16–22, 1983, pp. 96–107. Basel, Birkhäuser (1985)

  19. Deutsch, F.R.: The angle between subspaces of a hilbert space. In: Approximation Theory, Wavelets and Applications, pp. 107–130. Springer, Dordrecht (1995)

  20. Deutsch, F.R.: Best Approximation in Inner Product Spaces. CMS Books in Mathematics. Springer, New York (2001)

    Book  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  22. 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), 421–421 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  23. Feuersänger, C.: PGFPlots Package, 1.13 edn (2016)

  24. Hesse, R., Luke, D.R.: Nonconvex notions of regularity and convergence of fundamental algorithms for feasibility problems. SIAM J. Optim. 23(4), 2397–2419 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  25. Hesse, R., Luke, D.R., Neumann, P.: Alternating projections and Douglas-Rachford for sparse affine feasibility. IEEE Trans. Signal Process. 62(18), 4868–4881 (2014)

    Article  MathSciNet  Google Scholar 

  26. Kayalar, S., Weinert, H.L.: Error bounds for the method of alternating projections. Math. Control Signals Syst. 1(1), 43–59 (1988)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  29. Siqueira, A.S., da Silva, R.C., Santos, L.R.: Perprof-py: A python package for performance profile of mathematical optimization software. J Open Res Softw 4(e12), 5 (2016)

    Google Scholar 

  30. Svaiter, B.F.: On weak convergence of the Douglas–Rachford method. SIAM J. Control. Optim. 49(1), 280–287 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

We thank the anonymous referees for their valuable suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roger Behling.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behling, R., Bello Cruz, J. & Santos, LR. Circumcentering the Douglas–Rachford method. Numer Algor 78, 759–776 (2018). https://doi.org/10.1007/s11075-017-0399-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-017-0399-5

Keywords

Mathematics Subject Classification (2010)

Navigation