Skip to main content
Log in

A block coordinate variable metric forward–backward algorithm

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

A number of recent works have emphasized the prominent role played by the Kurdyka-Łojasiewicz inequality for proving the convergence of iterative algorithms solving possibly nonsmooth/nonconvex optimization problems. In this work, we consider the minimization of an objective function satisfying this property, which is a sum of two terms: (i) a differentiable, but not necessarily convex, function and (ii) a function that is not necessarily convex, nor necessarily differentiable. The latter function is expressed as a separable sum of functions of blocks of variables. Such an optimization problem can be addressed with the Forward–Backward algorithm which can be accelerated thanks to the use of variable metrics derived from the Majorize–Minimize principle. We propose to combine the latter acceleration technique with an alternating minimization strategy which relies upon a flexible update rule. We give conditions under which the sequence generated by the resulting Block Coordinate Variable Metric Forward–Backward algorithm converges to a critical point of the objective function. An application example to a nonconvex phase retrieval problem encountered in signal/image processing shows the efficiency of the proposed optimization method.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. We consider right derivatives at \(\omega =0\).

References

  1. Abboud, F., Chouzenoux, E., Pesquet, J.-C., Chenot, J.H., Laborelli, L.: A hybrid alternating proximal method for blind video restoration. In: Proceedings of European Signal Processing Conference (EUSIPCO 2014), pp. 1811–1815. Lisboa, Portugal (2014)

  2. Attouch, H., Bolte, J.: On the convergence of the proximal algorithm for nonsmooth functions involving analytic features. Math. Program. 116, 5–16 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Attouch, H., Bolte, J., Redont, P., Soubeyran, A.: Proximal alternating minimization and projection methods for nonconvex problems. An approach based on the Kurdyka-Łojasiewicz inequality. Math. Oper. Res. 35(2), 438–457 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Attouch, H., Bolte, J., Svaiter, B.F.: Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods. Math. Program. 137, 91–129 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Auslender, A.: Asymptotic properties of the Fenchel dual functional and applications to decomposition problems. J. Optim. Theory Appl. 73(3), 427–449 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bauschke, H.H., Combettes, P.L., Luke, D.R.: Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization. J. Opt. Soc. Am. A 19(7), 1334–1345 (2002)

    Article  MathSciNet  Google Scholar 

  7. Bauschke, H.H., Combettes, P.L., Luke, D.R.: A new generation of iterative transform algorithms for phase contrast tomography. In: Proceedings of IEEE International Conference Acoust., Speech Signal Process. (ICASSP 2005), vol. 4, pp. 89–92. Philadelphia, PA (2005)

  8. Bauschke, H.H., Combettes, P.L., Noll, D.: Joint minimization with alternating Bregman proximity operators. Pac. J. Optim. 2(3), 401–424 (2006)

    MathSciNet  MATH  Google Scholar 

  9. Bertsekas, D.P.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont, MA (1999)

    MATH  Google Scholar 

  10. Bolte, J., Daniilidis, A., Lewis, A.: The Łojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems. SIAM J. Optim. 17, 1205–1223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bolte, J., Daniilidis, A., Lewis, A., Shiota, M.: Clarke subgradients of stratifiable functions. SIAM J. Optim. 18(2), 556–572 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Bolte, J., Daniilidis, A., Ley, O., Mazet, L.: Characterizations of Łojasiewicz inequalities: subgradient flows, talweg, convexity. Trans. Am. Math. Soc. 362(6), 3319–3363 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Bolte, J., Sabach, S., Teboulle, M.: Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Math. Program. 146(1), 459–494 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Brègman, L.M.: The method of successive projection for finding a common point of convex sets. Soviet Math. Dokl. 6, 688–692 (1965)

    MATH  Google Scholar 

  15. Candès, E., Eldar, Y., Strohmer, T., Voroninski, V.: Phase retrieval via matrix completion. SIAM J. Imaging Sci. 6(1), 199–225 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Censor, Y., Lent, A.: Optimization of \(\log x\) entropy over linear equality constraints. SIAM J. Control Optim. 25(4), 921–933 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  17. Chaux, C., Combettes, P.L., Pesquet, J.-C., Wajs, V.R.: A variational formulation for frame based inverse problems. Inverse Probl. 23(4), 1495–1518 (2007)

  18. Chouzenoux, E., Pesquet, J.-C., Repetti, A.: Variable metric forward-backward algorithm for minimizing the sum of a differentiable function and a convex function. J. Optim. Theory Appl. 162(1), 107–132 (2014)

  19. Combettes, P.L., Pesquet, J.-C.: Proximal splitting methods in signal processing. In: Bauschke, H.H., Burachik, R., Combettes, P.L., Elser, V., Luke, D.R., Wolkowicz, H. (eds.) Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 185–212. Springer, New York (2010)

  20. Combettes, P.L., Pesquet, J.-C.: Stochastic quasi-Fejér block-coordinate fixed point iterations with random sweeping. SIAM J. Optim. 25, 1221–1248 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Combettes, P.L., Vũ, B.C.: Variable metric quasi-Fejér monotonicity. Nonlinear Anal. 78, 17–31 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  22. Combettes, P.L., Vũ, B.C.: Variable metric forward-backward splitting with applications to monotone inclusions in duality. Optimization 63(9), 1289–1318 (2014)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  24. Dainty, J.C., Fienup, J.R.: Phase retrieval and image reconstruction for astronomy. In: Stark, H. (ed.) Image Recovery: Theory and Application, pp. 231–275. Academic Press, Orlando, FL (1987)

    Google Scholar 

  25. Fessler, J.A.: Grouped coordinate ascent algorithms for penalized-likelihood transmission image reconstruction. IEEE Trans. Med. Imag. 16(2), 166–175 (1997)

    Article  Google Scholar 

  26. Fienup, J.R.: Phase retrieval algorithms: a comparison. Appl. Opt. 21(15), 2758–2769 (1982)

    Article  Google Scholar 

  27. Frankel, P., Garrigos, G., Peypouquet, J.: Splitting methods with variable metric for Kurdyka-Łojasiewicz functions and general convergence rates. J. Optim. Theory Appl. 165(3), 874–900 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  28. Gerchberg, R.W., Saxton, W.O.: A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik 35, 237–246 (1972)

    Google Scholar 

  29. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  30. Hesse, R., Luke, D.R., Sabach, S., Tam, M.K.: Proximal heterogeneous block input-output method and application to blind ptychographic diffraction imaging. Tech. rep. (2014). arXiv:1408.1887

  31. Hiriart-Urruty, J.B., Lemaréchal, C.: Convex Analysis and Minimization Algorithms. Springer, New York (1993)

    MATH  Google Scholar 

  32. Jacobson, M.W., Fessler, J.A.: An expanded theoretical treatment of iteration-dependent majorize-minimize algorithms. IEEE Trans. Image Process. 16(10), 2411–2422 (2007)

    Article  MathSciNet  Google Scholar 

  33. Kurdyka, K., Parusinski, A.: \(w_f\)-stratification of subanalytic functions and the Łojasiewicz inequality. Comptes rendus de l’Académie des sciences. Série 1, Mathématique 318(2), 129–133 (1994)

    MathSciNet  MATH  Google Scholar 

  34. Łojasiewicz, S.: Une propriété topologique des sous-ensembles analytiques réels. Editions du centre National de la Recherche Scientifique, pp. 87–89 (1963)

  35. Luenberger, D.G.: Linear and Nonlinear Programming. Addison-Wesley, Reading (1973)

    MATH  Google Scholar 

  36. Luo, Z.Q., Tseng, P.: On the convergence of the coordinate descent method for convex differentiable minimization. J. Optim. Theory Appl. 72(1), 7–35 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  37. 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 

  38. Mallat, S.: A Wavelet Tour of Signal Processing, 3rd edn. Academic Press, Burlington (2009)

    MATH  Google Scholar 

  39. Mordukhovich, B.S.: Variational Analysis and Generalized Differentiation. Vol. I: Basic theory, Series of Comprehensive Studies in Mathematics, vol. 330. Springer, Berlin (2006)

    Google Scholar 

  40. Moreau, J.J.: Proximité et dualité dans un espace hilbertien. Bull. Soc. Math. France 93, 273–299 (1965)

    MathSciNet  MATH  Google Scholar 

  41. Mukherjee, S., Seelamantula, C.S.: An iterative algorithm for phase retrieval with sparsity constraints: application to frequency domain optical coherence tomography. In: Proceedings of IEEE Internationl Conference Acoust., Speech and Signal Process. (ICASSP 2012), pp. 553–556. Kyoto, Japan (2012)

  42. Ochs, P., Chen, Y., Brox, T., Pock, T.: iPiano: inertial proximal algorithm for non-convex optimization. SIAM J. Imaging Sci. 7(2), 1388–1419 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  43. Ortega, J.M., Rheinboldt, W.C.: Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York (1970)

    MATH  Google Scholar 

  44. Pirayre, A., Couprie, C., Duval, L., Pesquet, J.-C.: Fast convex optimization for connectivity enforcement in gene regulatory network inference. In: Proceedings of IEEE International Conference Acoust., Speech Signal Process. (ICASSP 2015), pp. 1002–1006. Brisbane, Australia (2015)

  45. Powell, M.J.D.: On search directions for minimization algorithms. Math. Program. 4, 193–201 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  46. Pustelnik, N., Benazza-Benhayia, A., Zheng, Y., Pesquet, J.-C.: Wavelet-based image deconvolution and reconstruction. To appear in Wiley Encyclopedia of Electrical and Electronics Engineering (2016). https://hal.archives-ouvertes.fr/hal-01164833v1

  47. Razaviyayn, M., Hong, M., Luo, Z.: A unified convergence analysis of block successive minimization methods for nonsmooth optimization. SIAM J. Optim. 23(2), 1126–1153 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  48. Repetti, A., Pham, M.Q., Duval, L., Chouzenoux, E., Pesquet, J.-C.: Euclid in a taxicab: Sparse blind deconvolution with smoothed \(\ell _1/\ell _2\) regularization. IEEE Signal Process. Lett. 22(5), 539–543 (2015)

  49. Repetti, A., Chouzenoux, E., Pesquet, J.-C.: A preconditioned forward-backward approach with application to large-scale nonconvex spectral unmixing problems. In: Proceedings of IEEE International Conference Acoust., Speech Signal Process. (ICASSP 2014), pp. 1498–1502. Firenze, Italy (2014)

  50. Repetti, A., Chouzenoux, E., Pesquet, J.-C.: A nonconvex regularized approach for phase retrieval. In: Proceedings of IEEE International Conference Image Process. (ICIP 2014), pp. 1753–1757. Paris, France (2014)

  51. Richtárik, P., Talác, M.: Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math. Program. 144(1), 1–38 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  52. Rockafellar, R.T., Wets, R.J.B.: Variational Analysis, 1st edn. Springer, Berlin (1997)

    MATH  Google Scholar 

  53. Saquib, S., Zheng, J., Bouman, C.A., Sauer, K.D.: Parallel computation of sequential pixel updates in statistical tomographic reconstruction. In: Proceedings of IEEE International Conference Image Process. (ICIP 1995), vol. 2, 93–96. Washington, DC (1995)

  54. Saxton, W.O.: Computer Techniques for Image Processing in Electron Microscopy. Academic Press, New York (1978)

    Google Scholar 

  55. Shechtman, Y., Beck, A., Eldar, Y.: GESPAR: efficient phase retrieval of sparse signals. IEEE Trans. Signal Process. 4(62), 928–938 (2014)

    Article  MathSciNet  Google Scholar 

  56. Sotthivirat, S., Fessler, J.A.: Image recovery using partitioned-separable paraboloidal surrogate coordinate ascent algorithms. IEEE Trans. Signal Process. 11(3), 306–317 (2002)

    Google Scholar 

  57. Tappenden, R., Richtárik, P., Gondzio, J.: Inexact coordinate descent: complexity and preconditioning. J. Optim. Theory Appl. (to appear). arXiv:1304.5530v2

  58. Tseng, P.: Convergence of a block coordinate descent method for nondifferentiable minimization. J. Optim. Theory Appl. 109(3), 475–494 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  59. Waldspurger, I., d’Aspremont, A., Mallat, S.: Phase recovery, maxcut and complex semidefinite programming. Math. Program. 149(1), 47–81 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  60. Xu, Y., Yin, W.: A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM J. Imaging Sci. 6(3), 1758–1789 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  61. Xu, Y., Yin, W.: A globally convergent algorithm for nonconvex optimization based on block coordinate update. Tech. rep. (2014). arXiv:1410.1386

  62. Zangwill, W.I.: Nonlinear Programming. Prentice-Hall, Englewood Cliffs (1969)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emilie Chouzenoux.

Additional information

This work was supported by the CNRS MASTODONS project under grant 2013MesureHD and by the CNRS Imag’in Project under Grant 2015OPTIMISME.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chouzenoux, E., Pesquet, JC. & Repetti, A. A block coordinate variable metric forward–backward algorithm. J Glob Optim 66, 457–485 (2016). https://doi.org/10.1007/s10898-016-0405-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-016-0405-9

Keywords

Mathematics Subject Classification

Navigation