An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems

We present an alternating augmented Lagrangian method for convex optimization problems where the cost function is the sum of two terms, one that is separable in the variable blocks, and a second th ...

[1]  Alexandre d'Aspremont,et al.  Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .

[2]  Stephen P. Boyd,et al.  1 Trend Filtering , 2009, SIAM Rev..

[3]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[4]  Kim-Chuan Toh,et al.  SDPT3 -- A Matlab Software Package for Semidefinite Programming , 1996 .

[5]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[6]  Simon Setzer,et al.  Operator Splittings, Bregman Methods and Frame Shrinkage in Image Processing , 2011, International Journal of Computer Vision.

[7]  J.-C. Pesquet,et al.  A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery , 2007, IEEE Journal of Selected Topics in Signal Processing.

[8]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

[9]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[10]  Stephen P. Boyd,et al.  Segmentation of ARX-models using sum-of-norms regularization , 2010, Autom..

[11]  Dimitri P. Bertsekas,et al.  On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..

[12]  Bo Wahlberg,et al.  On l1 mean and variance filtering , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[13]  R. Tibshirani,et al.  Sparsity and smoothness via the fused lasso , 2005 .