Abstract
This work is concerned with the classical problem of finding a zero of a sum of maximal monotone operators. For the projective splitting framework recently proposed by Combettes and Eckstein, we show how to replace the fundamental subproblem calculation using a backward step with one based on two forward steps. The resulting algorithms have the same kind of coordination procedure and can be implemented in the same block-iterative and highly flexible manner, but may perform backward steps on some operators and forward steps on others. Prior algorithms in the projective splitting family have used only backward steps. Forward steps can be used for any Lipschitz-continuous operators provided the stepsize is bounded by the inverse of the Lipschitz constant. If the Lipschitz constant is unknown, a simple backtracking linesearch procedure may be used. For affine operators, the stepsize can be chosen adaptively without knowledge of the Lipschitz constant and without any additional forward steps. We close the paper by empirically studying the performance of several kinds of splitting algorithms on a large-scale rare feature selection problem.


Similar content being viewed by others
References
Alotaibi, A., Combettes, P.L., Shahzad, N.: Solving coupled composite monotone inclusions by successive Fejér approximations of their Kuhn-Tucker set. SIAM J. Optim. 24(4), 2076–2095 (2014)
Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2nd edn. Springer, Berlin (2017)
Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18(11), 2419–2434 (2009)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)
Briceño-Arias, L.M., Combettes, P.L.: A monotone+ skew splitting model for composite monotone inclusions in duality. SIAM J. Optim. 21(4), 1230–1250 (2011)
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40(1), 120–145 (2011)
Combettes, P.L.: Fejér monotonicity in convex optimization. In: Encyclopedia of optimization, vol. 2, pp. 106–114. Springer (2001)
Combettes, P.L., Eckstein, J.: Asynchronous block-iterative primal-dual decomposition methods for monotone inclusions. Math. Program. 168(1–2), 645–672 (2018)
Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing. In: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 185–212. Springer (2011)
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)
Combettes, P.L., Vũ, B.C.: Variable metric forward-backward splitting with applications to monotone inclusions in duality. Optimization 63(9), 1289–1318 (2014)
Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward–backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005)
Condat, L.: A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms. J. Optim. Theory Appl. 158(2), 460–479 (2013)
Davis, D., Yin, W.: A three-operator splitting scheme and its optimization applications. Set-Valued Var. Anal. 25(4), 829–858 (2017)
Eckstein, J.: A simplified form of block-iterative operator splitting and an asynchronous algorithm resembling the multi-block alternating direction method of multipliers. J. Optim. Theory Appl. 173(1), 155–182 (2017)
Eckstein, J., Svaiter, B.F.: A family of projective splitting methods for the sum of two maximal monotone operators. Math. Program. 111(1), 173–199 (2008)
Eckstein, J., Svaiter, B.F.: General projective splitting methods for sums of maximal monotone operators. SIAM J. Control Optim. 48(2), 787–811 (2009)
Eckstein, J., Yao, W.: Approximate ADMM algorithms derived from Lagrangian splitting. Comput. Optim. Appl. 68(2), 363–405 (2017)
Eckstein, J., Yao, W.: Relative-error approximate versions of Douglas-Rachford splitting and special cases of the ADMM. Math. Program. 170(2), 417–444 (2018)
Iusem, A., Svaiter, B.: A variant of Korpelevich’s method for variational inequalities with a new search strategy. Optimization 42(4), 309–321 (1997)
Johnstone, P.R., Eckstein, J.: Convergence rates for projective splitting. SIAM J. Optim. 29(3), 1931–1957 (2019)
Komodakis, N., Pesquet, J.C.: Playing with duality: an overview of recent primal-dual approaches for solving large-scale optimization problems. IEEE Signal Process. Mag. 32(6), 31–54 (2015)
Korpelevich, G.: The extragradient method for finding saddle points and other problems. Matecon 12, 747–756 (1976)
Krasnosel’skii, M.A.: Two remarks on the method of successive approximations. Uspekhi Matematicheskikh Nauk 10(1), 123–127 (1955)
Lions, P.L., Mercier, B.: Splitting algorithms for the sum of two nonlinear operators. SIAM J. Numer. Anal. 16(6), 964–979 (1979)
Malitsky, Y., Pock, T.: A first-order primal-dual algorithm with linesearch. SIAM J. Optim. 28(1), 411–432 (2018)
Malitsky, Y., Tam, M.K.: A forward-backward splitting method for monotone inclusions without cocoercivity. arXiv preprint arXiv:1808.04162 (2018)
Mann, W.R.: Mean value methods in iteration. Proc. Am. Math. Soc. 4(3), 506–510 (1953)
Mercier, B., Vijayasundaram, G.: Lectures on Topics in Finite Element Solution of Elliptic Problems. Tata Institute of Fundamental Research, Bombay (1979)
Nocedal, J.: Updating quasi-Newton matrices with limited storage. Math. Comp. 35(151), 773–782 (1980)
Pedregosa, F., Gidel, G.: Adaptive three operator splitting. Tech. Rep. arXiv:1804.02339, arXiv (2018)
Solodov, M.V., Svaiter, B.F.: A hybrid projection-proximal point algorithm. J. Convex Anal. 6(1), 59–70 (1999)
Solodov, M.V., Svaiter, B.F.: A new projection method for variational inequality problems. SIAM J. Control Optim. 37(3), 765–776 (1999)
Tran-Dinh, Q., Vũ, B.C.: A new splitting method for solving composite monotone inclusions involving parallel-sum operators. Preprint arXiv:1505.07946, arXiv (2015)
Tseng, P.: A modified forward-backward splitting method for maximal monotone mappings. SIAM J. Control Optim. 38(2), 431–446 (2000)
Vũ, B.C.: A splitting algorithm for dual monotone inclusions involving cocoercive operators. Adv. Comput. Math. 38(3), 667–681 (2013)
Vũ, B.C.: A variable metric extension of the forward–backward–forward algorithm for monotone operators. Numer. Funct. Anal. Optim. 34(9), 1050–1065 (2013)
Yan, X., Bien, J.: Rare Feature Selection in High Dimensions. arXiv preprint arXiv:1803.06675 (2018)
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 1617617. We thank Xiaohan Yan and Jacob Bien for kindly sharing their data for the TripAdvisor reviews problem in Sect. 6.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Johnstone, P.R., Eckstein, J. Projective splitting with forward steps. Math. Program. 191, 631–670 (2022). https://doi.org/10.1007/s10107-020-01565-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10107-020-01565-3