Abstract
This paper analyzes the iteration-complexity of a generalized alternating direction method of multipliers (G-ADMM) for solving separable linearly constrained convex optimization problems. This ADMM variant, first proposed by Bertsekas and Eckstein, introduces a relaxation parameter \(\alpha \) into the second ADMM subproblem in order to improve its computational performance. It is shown that, for a given tolerance \(\varepsilon >0\), the G-ADMM with \(\alpha \in (0, 2)\) provides, in at most \({\mathcal {O}}(1/\varepsilon ^2)\) iterations, an approximate solution of the Lagrangian system associated to the optimization problem under consideration. It is further demonstrated that, in at most \({\mathcal {O}}(1/\varepsilon )\) iterations, an approximate solution of the Lagrangian system can be obtained by means of an ergodic sequence associated to a sequence generated by the G-ADMM with \(\alpha \in (0, 2]\). Our approach consists of interpreting the G-ADMM as an instance of a hybrid proximal extragradient framework with some special properties. Some preliminary numerical experiments are reported in order to confirm that the use of \(\alpha >1\) can lead to a better numerical performance than \(\alpha =1\) (which corresponds to the standard ADMM).
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Notes
An operator \(T:\mathbb {R}^s\rightrightarrows \mathbb {R}^s\) is said to be monotone if \( \langle {z-z'},{s-s'}\rangle \ge 0\), for every \(z,z'\in \mathbb {R}^s,\)\(s\in T(z)\) and \(s'\in T(z')\). Moreover, T is maximal monotone if it is monotone and, additionally, if S is a monotone operator such that \(T(z)\subset S(z)\) for every \(z\in \mathbb {R}^s\) then \(T=S\).
References
Bertsekas, D.P.: Constrained Optimization and Lagrange Multiplier Methods. Academic Press, New York (1982)
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)
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)
Corman, E., Yuan, X.: A generalized proximal point algorithm and its convergence rate. SIAM J. Optim. 24(4), 1614–1638 (2014)
Cui, Y., Li, X., Sun, D., Toh, K.C.: On the convergence properties of a majorized ADMM for linearly constrained convex optimization problems with coupled objective functions. J. Optim. Theory Appl. 169(3), 1013–1041 (2016)
Deng, W., Yin, W.: On the global and linear convergence of the generalized alternating direction method of multipliers. J. Sci. Comput. 66(3), 889–916 (2016)
Eckstein, J.: Parallel alternating direction multiplier decomposition of convex programs. J. Optim. Theory Appl. 80(1), 39–62 (1994)
Eckstein, J.: Some saddle-function splitting methods for convex programming. Optim. Methods Softw. 4(1), 75–83 (1994)
Eckstein, J., Bertsekas, D.P.: On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Progr. 55(3 Ser. A), 293–318 (1992)
Fang, E.X., He, B., Liu, H., Yuan, X.: Generalized alternating direction method of multipliers: new theoretical insights and applications. Math. Prog. Comput. 7(2), 149–187 (2015)
Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput. Math. Appl. 2, 17–40 (1976)
Glowinski, R.: Numerical Methods for Nonlinear Variational Problems. Springer Series in Computational Physics. Springer, Berlin (1984)
Glowinski, R., Marroco, A.: Sur l’approximation, par éléments finis d’ordre un, et la résolution, par penalisation-dualité, d’une classe de problèmes de Dirichlet non linéaires. RAIRO Anal. Numér. 9, 41–76 (1975)
Gonçalves, M.L.N., Alves, M.M., Melo, J.G.: Pointwise and ergodic convergence rates of a variable metric proximal alternating direction method of multipliers. J. Optim. Theory Appl. 177(2), 448–478 (2018)
Gonçalves, M.L.N., Melo, J.G., Monteiro, R.D.C.: Extending the ergodic convergence rate of the proximal ADMM. arXiv preprint arXiv:1611.02903 (2016)
Gonçalves, M.L.N., Melo, J.G., Monteiro, R.D.C.: Improved pointwise iteration-complexity of a regularized ADMM and of a regularized non-euclidean HPE framework. SIAM J. Optim. 27(1), 379–407 (2017)
Gu, Y., Jiang, B., Deren, H.: A semi-proximal-based strictly contractive Peaceman–Rachford splitting method. arXiv preprint arXiv:1506.02221 (2015)
Hager, W.W., Yashtini, M., Zhang, H.: An \({O}(1/k)\) convergence rate for the variable stepsize Bregman operator splitting algorithm. SIAM J. Numer. Anal. 54(3), 1535–1556 (2016)
He, B., Yuan, X.: On the \(\cal{O}(1/n)\) convergence rate of the Douglas–Rachford alternating direction method. SIAM J. Numer. Anal. 50(2), 700–709 (2012)
He, B., Yuan, X.: On non-ergodic convergence rate of Douglas–Rachford alternating direction method of multipliers. Numer. Math. 130(3), 567–577 (2015)
Lin, T., Ma, S., Zhang, S.: An extragradient-based alternating direction method for convex minimization. Found. Comput. Math. 17(1), 17–35 (2017)
Liu, J., Duan, Y., Sun, M.: A symmetric version of the generalized alternating direction method of multipliers for two-block separable convex programming. J. Inequal. Appl. 2017(1), 129 (2017)
Monteiro, R.D.C., Svaiter, B.F.: On the complexity of the hybrid proximal extragradient method for the iterates and the ergodic mean. SIAM J. Optim. 20(6), 2755–2787 (2010)
Monteiro, R.D.C., Svaiter, B.F.: Iteration-complexity of block-decomposition algorithms and the alternating direction method of multipliers. SIAM J. Optim. 23(1), 475–507 (2013)
Nishihara, R., Lessard, L., Recht, B., Packard, A., Jordan, M.I.: A general analysis of the convergence of ADMM. arXiv preprint arXiv:1502.02009 (2015)
Ouyang, Y., Chen, Y., Lan, G., Pasiliao Jr., E.: An accelerated linearized alternating direction method of multipliers. SIAM J. Imaging Sci. 8(1), 644–681 (2015)
Solodov, M.V., Svaiter, B.F.: A hybrid approximate extragradient-proximal point algorithm using the enlargement of a maximal monotone operator. Set-Valued Anal. 7(4), 323–345 (1999)
Sun, H.: Analysis of fully preconditioned ADMM with relaxation in Hilbert spaces. arXiv preprint arXiv:1611.04801 (2016)
Tao, M., Yuan, X.: On the optimal linear convergence rate of a generalized proximal point algorithm. J. Sci. Comput. 74(2), 826–850 (2018)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58(1), 267–288 (1996)
Tibshirani, R.J.: The lasso problem and uniqueness. Electron. J. Stat. 7, 1456–1490 (2013)
Wang, X., Yuan, X.: The linearized alternating direction method of multipliers for Dantzig selector. SIAM J. Sci. Comput. 34(5), 2792–2811 (2012)
Yang, J., Yuan, X.: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization. Math. Comput. 82(281), 301–329 (2013)
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The work of these authors was supported in part by CNPq Grants 302666/2017-6, 406975/2016-7 and CAPES.
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Adona, V.A., Gonçalves, M.L.N. & Melo, J.G. Iteration-complexity analysis of a generalized alternating direction method of multipliers. J Glob Optim 73, 331–348 (2019). https://doi.org/10.1007/s10898-018-0697-z
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DOI: https://doi.org/10.1007/s10898-018-0697-z
Keywords
- Generalized alternating direction method of multipliers
- Hybrid extragradient method
- Convex program
- Pointwise iteration-complexity
- Ergodic iteration-complexity