Abstract
We introduce a generalized forward–backward splitting method with penalty term for solving monotone inclusion problems involving the sum of a finite number of maximally monotone operators and the normal cone to the nonempty set of zeros of another maximally monotone operator. We show weak ergodic convergence of the generated sequence of iterates to a solution of the considered monotone inclusion problem, provided that the condition corresponding to the Fitzpatrick function of the operator describing the set of the normal cone is fulfilled. Under strong monotonicity of an operator, we show strong convergence of the iterates. Furthermore, we utilize the proposed method for minimizing a large-scale hierarchical minimization problem concerning the sum of differentiable and nondifferentiable convex functions subject to the set of minima of another differentiable convex function. We illustrate the functionality of the method through numerical experiments addressing constrained elastic net and generalized Heron location problems.
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References
Attouch, H., Czarnecki, M.-O.: Asymptotic behavior of coupled dynamical systems with multiscale aspects. J. Differ. Equ. 248(6), 1315–1344 (2010)
Attouch, H., Czarnecki, M.-O., Peypouquet, J.: Prox-penalization and splitting methods for constrained variational problems. SIAM J. Optim. 21(1), 149–173 (2011)
Attouch, H., Czarnecki, M.-O., Peypouquet, J.: Coupling forward–backward with penalty schemes and parallel splitting for constrained variational inequalities. SIAM J. Optim. 21(4), 1251–1274 (2011)
Attouch, H., Peypouquet, J.: The rate of convergence of Nesterov’s accelerated forward–backward method is actually faster than \(1/k^2\). SIAM J. Optim. 26, 1824–1834 (2016)
Baillon, J.-B., Haddad, G.: Quelques propriétés des opérateurs angle-bornés et \(n\)-cycliquement monotones. Israel J. Math. 26, 137–150 (1977)
Banert, S., Boţ, R.I.: Backward penalty schemes for monotone inclusion problems. J. Optim. Theory Appl. 166(3), 930–948 (2015)
Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, New York (2011)
Bottou, L., Curtis, F.E., Nocedal, J.: Optimization methods for large-scale machine learning. SIAM Rev. 60(2), 223–311 (2018)
Boţ, R.I., Csetnek, E.R.: A Tseng’s type penalty scheme for solving inclusion problems involving linearly composed and parallel-sum type monotone operators. Vietnam J. Math. 42(4), 451–465 (2014)
Boţ, R.I., Csetnek, E.R.: Forward–backward and Tseng’s type penalty schemes for monotone inclusion problems. Set-Valued Var. Anal. 22, 313–331 (2014)
Boţ, R.I., Csetnek, E.R.: An inertial forward–backward–forward primal–dual splitting algorithm for solving monotone inclusion problems. Numer. Algorithms 71, 519–540 (2016)
Boţ, R.I., Csetnek, E.R.: Penalty schemes with inertial effects for monotone inclusion problems. Optimization 66, 965–982 (2017)
Boţ, R.I., Csetnek, E.R., Nimana, N.: Gradient-type penalty method with inertial effects for solving constrained convex optimization problems with smooth data. Optim. Lett. 12(1), 17–33 (2018)
Boţ, R.I., Csetnek, E.R., Nimana, N.: An inertial proximal-gradient penalization scheme for constrained convex optimization problems. Vietnam J. Math. 46(1), 53–71 (2018)
Chen, C., Chan, R.H., MA, S., Yang, J.: Inertial proximal ADMM for linearly constrained separable convex optimization. SIAM J. Imaging Sci. 8(4), 2239–2267 (2015)
Chen, C., Ma, S., Yang, J.: A general inertial proximal point algorithm for mixed variational inequality problem. SIAM J. Optim. 25(4), 2120–2142 (2015)
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, 307–330 (2012)
Czarnecki, M.-O., Noun, N., Peypouquet, J.: Splitting forward–backward penalty scheme for constrained variational problems. J. Convex Anal. 23, 531–565 (2016)
Fitzpatrick, S.: Representing monotone operators by convex functions. In: Workshop/Miniconference on Functional Analysis and Optimization (Canberra, 1988), pp. 59–65, Proc. Centre Math. Anal. Austral. Nat. Univ., 20, Austral. Nat. Univ., Canberra (1988)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970)
Huang, T., Gong, H., Yang, C., He, Z.: ProteinLasso: a Lasso regression approach to protein inference problem in shotgun proteomics. Comp. Biol. Chem. 43, 46–54 (2013)
Mairal, J.: Incremental majorization–minimization optimization with application to large-scale machine learning. SIAM J. Optim. 25(2), 829–855 (2015)
Noun, N., Peypouquet, J.: Forward–backward penalty scheme for constrained convex minimization without inf-compactness. J. Optim. Theory Appl. 158(3), 787–795 (2013)
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)
Passty, G.B.: Ergodic convergence to a zero of the sum of monotone operators in Hilbert space. J. Math. Anal. Appl. 72, 383–390 (1979)
Peypouquet, J.: Coupling the gradient method with a general exterior penalization scheme for convex minimization. J. Optim. Theory Appl. 153(1), 123–138 (2012)
Polyak, B.T.: Introduction to Optimization (Translated from the Russian). Translations Series in Mathematics and Engineering. Optimization Software Inc., Publications Division, New York (1987)
Raguet, H., Fadili, J., Peyré, G.: A generalized forward–backward splitting: SIAM. J. Imaging Sci. 6, 1199–1226 (2013)
Raguet, H., Landrieu, L.: Preconditioning of a generalized forward–backward splitting and application to optimization on graphs. SIAM J. Imaging Sci. 8, 2706–2739 (2015)
Schmidt, M., Le Roux, N., Bach, F.: Minimizing finite sums with the stochastic average gradient. Math. Program. 162(1), 83–112 (2017)
Vũ, B.C.: A splitting algorithm for dual monotone inclusions involving cocoercive operators. Adv. Comput. Math. 38, 667–681 (2013)
Shalev-Schwartz, S., Zhang, T.: Stochastic dual coordinate ascent methods for regularized loss minimization. J. Mach. Learn. Res. 14, 567–599 (2013)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267–288 (1996)
Zălinescu, C.: Convex Analysis in General Vector Spaces. World Scientific, Singapore (2002)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005)
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The authors are thankful to two anonymous referees and the Associate Editor for comments and remarks which improved the quality of the paper.
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This work is partially supported by the Thailand Research Fund under the Project RSA5880028.
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Nimana, N., Petrot, N. Generalized forward–backward splitting with penalization for monotone inclusion problems. J Glob Optim 73, 825–847 (2019). https://doi.org/10.1007/s10898-018-00730-5
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DOI: https://doi.org/10.1007/s10898-018-00730-5