Advanced Search
Article Contents
Article Contents

Linearized alternating direction method of multipliers with Gaussian back substitution for separable convex programming

Abstract Related Papers Cited by
  • Recently, we have proposed combining the alternating direction method of multipliers (ADMM) with a Gaussian back substitution procedure for solving the convex minimization model with linear constraints and a general separable objective function, i.e., the objective function is the sum of many functions without coupled variables. In this paper, we further study this topic and show that the decomposed subproblems in the ADMM procedure can be substantially alleviated by linearizing the involved quadratic terms arising from the augmented Lagrangian penalty. When the resolvent operators of the separable functions in the objective have closed-form representations, embedding the linearization into the ADMM subproblems becomes necessary to yield easy subproblems with closed-form solutions. We thus show theoretically that the blend of ADMM, Gaussian back substitution and linearization works effectively for the separable convex minimization model under consideration.
    Mathematics Subject Classification: Primary: 90C25, 65K05; Secondary: 94A08.


    \begin{equation} \\ \end{equation}
  • [1]

    E. Blum and W. Oettli, "Mathematische Optimierung, Econometrics and Operations Research XX," Springer Verlag, 1975.


    N. Bose and K. Boo, High-resolution image reconstruction with multisensors, Int. J. Imag. Syst. Tech, 9 (1998), 294-304.doi: 10.1002/(SICI)1098-1098(1998)9:4<294::AID-IMA11>3.0.CO;2-X.


    S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learning, 3 (2010), 1-122.doi: 10.1561/2200000016.


    R. H. Chan, J. F. Yang and X. M. Yuan, Alternating direction method for image inpainting in wavelet domain, SIAM J. Imaging Sci., 4 (2011), 807-826.doi: 10.1137/100807247.


    T. F. Chan and R. Glowinski, Finite element approximation and iterative solution of a class of mildly non-linear elliptic equations, Technical report, Stanford University, 1978.


    C. H. Chen, B. S. He and X. M. Yuan, Matrix completion via alternating direction method, IMA J. Numer. Anal., 32 (2012), 227-245.doi: 10.1093/imanum/drq039.


    J. Douglas and H. H. Rachford, On the numerical solution of the heat conduction problem in 2 and 3 space variables, Tran. Amer. Math. Soc., 82 (1956), 421-439.doi: 10.1090/S0002-9947-1956-0084194-4.


    J. Eckstein and D. P. Bertsekas, On the Douglas-Rachford splitting method and the proximal points algorithm for maximal monotone operators, Math. Program., 55 (1992), 293-318.doi: 10.1007/BF01581204.


    E. Esser, Applications of Lagrangian-Based alternating direction methods and connections to split Bregman, UCLA CAM Report 09-31, 2009.


    M. Fortin and R. Glowinski, "Augmented Lagrangian Methods: Applications to the Numerical Solutions of Boundary Value Problems," Stud. Math. Appl., NorthHolland, Amsterdam, 15 (1983).


    M. Fukushima, Application of the alternating direction method of multipliers to separable convex programming problems, Comput. Optim. Appli., 2 (1992), 93-111.doi: 10.1007/BF00247655.


    M. Fukushima, The primal Douglas-Rachford splitting algorithm for a class of monotone mappings with application to the traffic equilibrium problem, Math. Program., 72 (1996), 1-15.doi: 10.1007/BF02592328.


    D. Gabay, Applications of the method of multipliers to variational inequalities, in "Augmented Lagrange Methods: Applications to the Solution of Boundary-valued Problems" (eds. M. Fortin and R. Glowinski), North Holland, Amsterdam, The Netherlands, (1983), 299-331.doi: 10.1016/S0168-2024(08)70034-1.


    D. Gabay and B. Mercier, A dual algorithm for the solution of nonlinear variational problems via finite-element approximations, Comput. Math. Appli., 2 (1976), 17-40.doi: 10.1016/0898-1221(76)90003-1.


    R. Glowinski, "Numerical Methods for Nonlinear Variational Problems," Springer-Verlag, New York, Berlin, Heidelberg, Tokyo, 1984.


    R. Glowinski and A. Marrocco, Approximation par éléments finis d'ordreun et résolution par pénalisation-dualité d'une classe de problèmes non linéaires, R.A.I.R.O., R2 (1975), 41-76.


    R. Glowinski and P. Le Tallec, "Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics," SIAM Studies in Applied Mathematics, Philadelphia, PA, 1989.doi: 10.1137/1.9781611970838.


    R. Glowinski, T. Kärkkäinen and K. Majava, On the convergence of operator-splitting methods, in "Numerical Methods for Scienfic computing, Variational Problems and Applications" (eds. Y. Kuznetsov, P. Neittanmaki and O. Pironneau), Barcelona, 2003.


    B. S. He, L. Z. Liao, D. R. Han and H. Yang, A new inexact alternating directions method for monontone variational inequalities, Math. Program., 92 (2002), 103-118.doi: 10.1007/s101070100280.


    B. S. He, M. Tao, M. H. Xu and X. M. YuanAlternating directions based contraction method for generally separable linearly constrained convex programming problems, Optimization, to appear.


    B. S. He, M. Tao and X. M. YuanA splitting method for separable convex programming, IMA J. Num. Anal., in revision.


    B. S. He, M. Tao and X. M. Yuan, Alternating direction method with Gaussian back substitution for separable convex programming, SIAM J. Optim., 12 (2012), 313-340.


    B. S. He, M. H. Xu and X. M. Yuan, Solving large-scale least squares covariance matrix problems by alternating direction methods, SIAM J. Matrix Anal. Appli., 32 (2011), 136-152.


    B. S. He and X. M. Yuan, On the O(1/n) convergence rate of Douglas-Rachford alternating direction method, SIAM J. Num. Anal., 50 (2012), 700-709.doi: 10.1137/110836936.


    M. R. Hestenes, Multiplier and gradient methods, J. Optim. Theory Appli., 4 (1969), 303-320.


    P. L. Lions and B. Mercier, Splitting algorithms for the sum of two nonlinear operators, SIAM J. Num. Anal., 16 (1979), 964-979.


    B. Martinet, Regularization d'inequations variationelles par approximations sucessives, Revue Francaise d'Informatique et de Recherche Opérationelle, 4 (1970), 154-158.


    M. K. Ng, P. A. Weiss and X. M. Yuan, Solving constrained total-variation problems via alternating direction methods, SIAM J. Sci. Comput., 32 (2010), 2710-2736.doi: 10.1137/090774823.


    G. B. Passty, Ergodic convergence to a zero of the sum of monotone operators in Hilbert space, J. Math. Analy. Appli., 72 (1979), 383-390.doi: 10.1016/0022-247X(79)90234-8.


    M. J. D. Powell, A method for nonlinear constraints in minimization problems, in "Optimization" (eds. R. Fletcher), Academic Press, New York, (1969), 283-298.


    R. T. Rockafellar, "Convex Analysis," Princeton, NJ, 1970.


    A. Ruszczyński, Parallel decomposition of multistage stochastic programming problems, Math. Program., 58 (1993), 201-228.


    S. Setzer, G. Steidl and T. Tebuber, Deblurring Poissonian images by split Bregman techniques, J. Visual Commun. Image Repres., 21 (2010), 193-199.doi: 10.1016/j.jvcir.2009.10.006.


    J. Sun and S. Zhang, A modified alternating direction method for convex quadratically constrained quadratic semidefinite programs, European J. Oper. Res., 207 (2010), 1210-1220.doi: 10.1016/j.ejor.2010.07.020.


    M. Tao and X. M. Yuan, Recovering low-rank and sparse components of matrices from incomplete and noisy observations, SIAM J. Optim., 21 (2011), 57-81.doi: 10.1137/100781894.


    R. Tibshirani, M. Saunders, S. Rosset, J. Zhu and K. Knight, Sparsity and smoothness via the fused lasso, J. Royal Statist. Soc., 67 (2005), 91-108.doi: 10.1111/j.1467-9868.2005.00490.x.


    Z. Wen, D. Goldfarb and W. Yin, Alternating direction augmented Lagrangian methods for semideffinite programming, Math. Program. Comput., 2 (2010), 203-230.doi: 10.1007/s12532-010-0017-1.


    X. M. Yuan, Alternating direction methods for covariance selection models, J. Sci. Comput., 51 (2012), 261-273.doi: 10.1007/s10915-011-9507-1.


    S. Zhang, J. Ang and J. SunAn alternating direction method for solving convex nonlinear semidefinite programming problem, Optimization, to appear.


    X. Q. Zhang, M. Burger, X. Bresson and S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction, SIAM J. Imag. Sci., 3 (2010), 253-276.doi: 10.1137/090746379.


    X. Q. Zhang, M. Burger and S. Osher, A unified primal-dual algorithm framework based on Bregman iteration, J. Sci. Comput., 46 (2010), 20-46.doi: 10.1007/s10915-010-9408-8.

  • 加载中

Article Metrics

HTML views() PDF downloads(183) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint