The Douglas–Peaceman–Rachford–Varga operator splitting methods are a class of efficient methods for finding a zero of the sum of two maximal monotone operators in a real Hilbert space; however, they are sometimes difficult or even impossible to solve the subproblems exactly. In this paper, we suggest an inexact version in which some relative error criterion is discussed. The corresponding convergence properties are established, and some preliminary numerical experiments are reported to illustrate its efficiency.
Yuan-Yuan Huang, Chang-He Liu, You-Lin Shang
. Inexact Operator Splitting Method for Monotone Inclusion Problems[J]. Journal of the Operations Research Society of China, 2021
, 9(2)
: 274
-306
.
DOI: 10.1007/s40305-020-00296-8
[1] Rockafellar, R.T., Wets, R.J.-B.: Variational Analysis. Springer, Berlin (1998)
[2] Tseng, P.: A modified forward-backward splitting method for maximal monotone mappings. SIAM J. control Optim. 38(2), 431–446(2000)
[3] Varga, R.S.: Matrix Iterative Analysis. Prentice-Hall Inc., Englewood Cliffs (1962)
[4] Eckstein, J., Bertsekas, D.P.: On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Program 55(3), 293–318(1992)
[5] Eckstein, J., Svaiter, B.F.: A family of projective splitting methods for the sum of two maximal monotone operators. Math. Program 111, 173–199(2008)
[6] Lawrence, J., Spingarn, J.E.: On fixed points of non-expensive piecewise isometric mappings. Proc. Lond. Math. Soc 3rd Ser. 55, 605–624(1987)
[7] Minty, G.J.: Monotone (nonlinear) operators in Hilbert space. Duke Math. J. 29, 341–346(1962)
[8] Dong, Y.D., Fang, B.B., Wang, X.Y.: New inertial factors of a splitting method for monotone inclusions (2018). http://www.optimization-online.org/DB_HTML/2018/04/6576.html. Accessed 16 Apr. 2018
[9] Rosasco, L., Villa, S., Vũ, B.C.: Stochastic Forward-Backward Splitting for Monotone Inclusions. J. Optim. Theory Appl. 169, 388–406(2016)
[10] He, B.S., Liao, L.Z., Wang, S.L.: Self-adaptive operator splitting methods for monotone variational inequalities. Numer. Math. 94, 715–737(2003)
[11] Dong, Y.D., Fischer, A.: A family of operator splitting methods revised. Nonlinear Anal. 72, 4307–4315(2010)
[12] Huang, Y.Y., Dong, Y.D.: New properties of forward-backward splitting and a practical proximaldescent algorithm. Appl. Math. Comput. 237, 60–68(2014)
[13] Li, M., Bnouhachem, A.: A modified inexact operator splitting methods for monotone variational inequalities. J. Global. Optim. 41, 417–426(2008)
[14] Han, D.R.: Inexact operator splitting methods with self-adaptive strategy for variational inequality problems. J. Optim. Theory Appl. 32(2), 227–243(2007)
[15] Candès, E., Romberg, J., Tao, T.: Stable signal recovery from imcomplete inaccurate information. Commun. Pure Appl. Math. 59, 1207–1233(2005)
[16] Candès, E., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory 52, 489–509(2006)
[17] Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 1, 586–597(2007)
[18] Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2, 183–202(2009)
[19] Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process 1, 586–597(2007)
[20] Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627(1995)
[21] Dolan, E.D., Morè, J.J.: Benchmarking optimization software with performance profiles. Math. Program 91, 201–213(2002)