[1] Tibshirani, R.:Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58, 267-288(1996) [2] Chambolle, A., Lions, P.L.:Image recovery via total variation minimization and related problems. Numer. Math. 76, 167-188(1997) [3] Rudin, L.I., Osher, S., Fatemi, E.:Nonlinear total variation based noise removal algorithm. Phys. D. 60, 259-268(1992). (Experimental Mathematics:Computational Issues in Nonlinear Science (Los Alamos, NM, 1991)) [4] Cai, J.F., Candès, E.J., Shen, Z.:A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20, 1956-1982(2010) [5] Chen, C.H., He, B.S., Yuan, X.M.:Matrix completion via an alternating direction method. IMA J. Numer. Anal. 32, 227-245(2012) [6] Wang, Y., Yang, J., Yin, W., Zhang, Y.:A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci. 1, 248-272(2008) [7] 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) [8] Glowinski, R., Marroco, A.:Sur l'approximation, par éléments finis d'ordre un, et la résolution, par pénalisation-dualité d'une classe de problèmes de dirichlet non linéaires. ESAIM Math. Model. Numer. Anal. 9, 41-76(1975) [9] Eckstein, J., Bertsekas, D.P.:On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators. Math. Program. 55, 293-318(1992) [10] He, B., Yuan, X.:On the O(1/n) convergence rate of the Douglas-Rachford alternating direction method. SIAM J. Numer. Anal. 50, 700-709(2012) [11] Cai, X., Gu, G., He, B., Yuan, X.:A proximal point algorithm revisit on the alternating direction method of multipliers. Sci. China Math. 56, 2179-2186(2013) [12] Bai, J., Zhang, H., Li, J.:A parameterized proximal point algorithm for separable convex optimization. Optim. Lett. 12, 1589-1608(2018) [13] Gu, G., He, B., Yuan, X.:Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems:a unified approach. Comput. Optim. Appl. 59, 135-161(2014) [14] He, B., Yuan, X., Zhang, W.:A customized proximal point algorithm for convex minimization with linear constraints. Comput. Optim. Appl. 56, 559-572(2013) [15] Ma, F., Ni, M.:A class of customized proximal point algorithms for linearly constrained convex optimization. Comput. Appl. Math. 37, 896-911(2018) [16] Rockafellar, R.T.:Monotone operators and the proximal point algorithm. SIAM J. Control Optim. 14, 877-898(1976) [17] 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, 323-345(1999) [18] Solodov, M.V., Svaiter, B.F.:A hybrid projection-proximal point algorithm. J. Convex Anal. 6, 59-70(1999) [19] Rasch, J., Chambolle, A.:Inexact first-order primal-dual algorithms. Comput. Optim. Appl. 76, 381-430(2020) [20] Alves, M.M., Eckstein, J., Geremia, M., Melo, J.G.:Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms. Comput. Optim. Appl. 75, 389-422(2020) [21] Eckstein, J., Yao, W.:Relative-error approximate versions of Douglas-Rachford splitting and special cases of the ADMM. Math. Program. 170, 417-444(2018) [22] Lions, P.L., Mercier, B.:Splitting algorithms for the sum of two nonlinear operators. SIAM J. Numer. Anal. 16, 964-979(1979) [23] Eckstein, J., Yao, W.:Approximate ADMM algorithms derived from Lagrangian splitting. Comput. Optim. Appl. 68, 363-405(2017) [24] Xie, J.:On inexact ADMMs with relative error criteria. Comput. Optim. Appl. 71, 743-765(2018) [25] Xie, J., Liao, A., Yang, X.:An inexact alternating direction method of multipliers with relative error criteria. Optim. Lett. 11, 583-596(2017) [26] Hestenes, M.R.:Multiplier and gradient methods. J. Optim. Theory Appl. 4, 303-320(1969) [27] Cai, X., Han, D.:O(1/t) complexity analysis of the generalized alternating direction method of multipliers. Sci. China Math. 62, 795-808(2019) [28] Nocedal, J., Wright, S.J.:Numerical Optimization, Springer Series in Operations Research and Financial Engineering, 2nd edn. Springer, New York (2006) [29] 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, 1-122(2010) |