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The range of the Douglas–Rachford operator in infinite-dimensional Hilbert spaces

Published online by Cambridge University Press:  24 October 2025

Walaa M. Moursi*
Affiliation:
Department of Combinatorics and Optimization, University of Waterloo, Waterloo, Ontario N2L 3G1, Canadaand Mansoura University, Mansoura 35516, Egypt

Abstract

The Douglas–Rachford algorithm is one of the most prominent splitting algorithms for solving convex optimization problems. Recently, the method has been successful in finding a generalized solution (provided that one exists) for optimization problems in the inconsistent case (i.e., when a solution does not exist). The convergence analysis of the inconsistent case hinges on the study of the range of the displacement operator associated with the Douglas–Rachford splitting operator and the corresponding minimal displacement vector. A comprehensive study of this range has been developed in finite-dimensional Hilbert spaces. In this paper, we provide a formula for the range of the Douglas–Rachford splitting operator in (possibly) infinite-dimensional Hilbert spaces under mild assumptions on the underlying operators. Our new results complement known results in finite-dimensional Hilbert spaces. Several examples illustrate and tighten our conclusions.

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Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Canadian Mathematical Society

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References

Baillon, J. B., Bruck, R. E. and Reich, S., On the asymptotic behaviour of nonexpansive mappings and semigroups in Banach spaces . Houston J Math. 4(1978), 19.Google Scholar
Bauschke, H. H. and Borwein, J. M., Dykstra’s alternating projection algorithm for two sets. J. Approx. Theory 79(1994), 418443.Google Scholar
Bauschke, H. H. and Combettes, P. L., Convex analysis and monotone operator theory in Hilbert spaces, 2nd edition, Springer, 2017.Google Scholar
Bauschke, H. H., Hare, W. L. and Moursi, W. M., Generalized solutions for the sum of two maximally monotone operators . SIAM J. Control Optim. 52(2014), 10341047.Google Scholar
Bauschke, H. H., Hare, W. L. and Moursi, W. M., On the range of the Douglas–Rachford operator . Math. Oper. Res. 41(2016), 884897.Google Scholar
Bauschke, H. H., Moffat, S. M. and Wang, X., Near equality, near convexity, sums of maximally monotone operators, and averages of firmly nonexpansive mappings . Math. Program. Ser. B 139(2013), 5570.Google Scholar
Bauschke, H. H. and Moursi, W. M., On the Douglas–Rachford algorithm for solving possibly inconsistent optimization problems. Math. Oper. Res. (2023). https://doi.org/10.1287/moor.2022.1347Google Scholar
Brezis, H. and Haraux, A., Image d’une Somme d’opérateurs Monotones et Applications . Israel J. Math. 23(1976), 165186.Google Scholar
Bruck, R. E. and Reich, S., Nonexpansive projections and resolvents of accretive operators in Banach spaces . Houston J. Math. 3(1977), 459470.Google Scholar
Deutsch, F., Best approximation in inner product spaces, Springer, 2001.Google Scholar
Eckstein, J., Splitting methods for monotone operators with applications to parallel optimization, PhD thesis, MIT, 1989.Google Scholar
Lions, P. L. and Mercier, B., Splitting algorithms for the sum of two nonlinear operators . SIAM J. Numer. Anal. 16(1979), no. 6, 964979.Google Scholar
Minty, G. J., Monotone (nonlinear) operators in Hilbert spaces . Duke Math. J. 29(1962), 341346.Google Scholar
Moffat, S. M., Moursi, W. M. and Wang, X., Nearly convex sets: fine properties and domains or ranges of subdifferentials of convex functions . Math. Program. Ser. A 126(2016), 193223.Google Scholar
Pazy, A., Asymptotic behavior of contractions in Hilbert space . Israel J. Math. 9(1971), 235240.Google Scholar
Rockafellar, R. T., Convex analysis, Princeton University Press, Princeton, 1970.Google Scholar
Rockafellar, R. T. and Wets, R.J-B, Variational analysis, Springer-Verlag, corrected 3rd printing, 2009.Google Scholar
Rockafellar, R. T., On the maximal monotonicity of subdifferential mappings . Pacific J. Math. 33(1970), 209216 .Google Scholar
Simons, S., Minimax and monotonicity, Springer-Verlag, 1998.Google Scholar
Simons, S., From Hahn-Banach to monotonicity, Springer-Verlag, 2008.Google Scholar
Zarantonello, E.H., Projections on convex sets in Hilbert space and spectral theory . In Contributions to nonlinear functional analysis, Academic Press, New York, 1971, 237424.Google Scholar