We propose D-LADMM, which is a K-layer LADMM inspired deep neural network and rigorously prove that there exist a set of learnable parameters for D-LADMM to generate globally converged solutions.

The Alternating Direction Method of Multipliers (ADMM) is widely used for linearly constrained convex problems. It is proven to have an O(1/√K) nonergodic convergence rateand a faster O(1/K) ergodic rate after ergodic averaging, where K is the number …

We propose an augmented Lagrange multiplier based algorithm to solve this nonconvex and nonsmooth problem with the convergence guarantee that every accumulation point is a KKT point.

We propose an efficient Alternating Direction Method of Multipliers (ADMM) to solve the nonconvex SSC and provide the convergence guarantee.

A Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed, where a variance-constrained rate term is introduced to the conventional sparse representation.

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