![]() ![]() McLachlan, G.J.: Discriminant Analysis and Statistical Pattern Recognition, vol. Q.: On the linear convergence of the alternating direction method of multipliers, manuscript (August 2012) Hestenes, M.R.: Multiplier and gradient methods. He, B.S., Yuan, X.M.: On the \(O(1/n)\) convergence rate of the Douglas–Rachford alternating direction method. He, B.S., Tao, M., Yuan, X.M.: Convergence rate and iteration complexity on the alternating direction method of multipliers with a substitution procedure for separable convex programming. M.: A splitting method for separable convex programming. ![]() He, B.S., Tao, M., Yuan, X.M.: Alternating direction method with Gaussian back substitution for separable convex programming. Han, D.R., Yuan, X.M.: A note on the alternating direction method of multipliers. Gol’shtein, E.G., Tret’yakov, N.V.: Modified Lagrangian in convex programming and their generalizations. Glowinski, R., Marrocco, A.: Approximation par èlèments finis d’ordre un et rèsolution par pènalisation-dualitè d’une classe de problémes non linèaires. In: Springer Proceedings of a Conference Dedicated to J. Glowinski, R.: On alternating directon methods of multipliers: a historical perspective. Glowinski, R.: Numerical Methods for Nonlinear Variational Problems. Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite element approximations. ![]() (eds.) Augmented Lagrangian Methods: Applications to the Solution of Boundary Problems. 55, 293–318 (1992)įortin, M., Glowinski, R.: On decomposition-coordination methods using an augmented Lagrangian. 40, 1935–1967 (2012)Įckstein, J., Yao, W.: Augmented Lagrangian and alternating direction methods for convex optimization: A tutorial and some illustrative computational results, manuscript (2012)Įckstein, J., Bertsekas, D.P.: On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators. Stanford University, Stanford, CA (1978)Ĭhandrasekaran, V., Parrilo, P.A., Willsky, A.S.: Latent variable graphical model selection via convex optimization. 3, 1–122 (2010)Ĭhan, T.F., Glowinski, R.: Finite Element Approximation and Iterative Solution of a Class of Mildly Non-linear Elliptic Equations, Technical Report. Springer, Berlin-Heidelberg-New York (1975)īoyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Academic Press, London (1982)īlum, E., Oettli, W.: Mathematische Optimierung. Bertsekas, D.P.: Constrained Optimization and Lagrange Multiplier Methods. ![]()
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