We respectively present the unified frameworks of Gauss-Seidel ADMMs and Jacobian ADMMs, which use different historical information for the current updating. Our frameworks generalize previous ADMMs to solve the problems with non-separable objectives.
We apply the Majorization Minimization technique to solve the problem OF L1 -norm based low rank matrix factorization, at each iteration, we upper bound the original function with a strongly convex surrogate.
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods.