A hybrid splitting method for smoothing Tikhonov regularization problem

In this paper, a hybrid splitting method is proposed for solving a smoothing Tikhonov regularization problem. At each iteration, the proposed method solves three subproblems. First of all, two subproblems are solved in a parallel fashion, and the multiplier associated to these two block variables is updated in a rapid sequence. Then the third subproblem is solved in the sense of an alternative fashion with the former two subproblems. Finally, the multiplier associated to the last two block variables is updated. Global convergence of the proposed method is proven under some suitable conditions. Some numerical experiments on the discrete ill-posed problems (DIPPs) show the validity and efficiency of the proposed hybrid splitting method.

[1]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[2]  R. Glowinski,et al.  Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics , 1987 .

[3]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[4]  L. Liao,et al.  ALTERNATING PROJECTION BASED PREDICTION-CORRECTION METHODS FOR STRUCTURED VARIATIONAL INEQUALITIES , 2006 .

[5]  Roger Fletcher,et al.  Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming , 2005, Numerische Mathematik.

[6]  F ChenStanley,et al.  An Empirical Study of Smoothing Techniques for Language Modeling , 1996, ACL.

[7]  Alfredo N. Iusem,et al.  A new smoothing-regularization approach for a maximum-likelihood estimation problem , 1994 .

[8]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[9]  Per Christian Hansen,et al.  REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems , 1994, Numerical Algorithms.

[10]  Zheng Peng,et al.  A partial parallel splitting augmented Lagrangian method for solving constrained matrix optimization problems , 2010, Comput. Math. Appl..

[11]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[12]  Bingsheng He,et al.  Parallel splitting augmented Lagrangian methods for monotone structured variational inequalities , 2009, Comput. Optim. Appl..

[13]  Christian Kanzow,et al.  A smoothing-regularization approach to mathematical programs with vanishing constraints , 2013, Comput. Optim. Appl..

[14]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[15]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[16]  Yin Zhang,et al.  Theory of Compressive Sensing via ℓ1-Minimization: a Non-RIP Analysis and Extensions , 2013 .

[17]  Bingsheng He,et al.  The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent , 2014, Mathematical Programming.

[18]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.