Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts have been done on designing theoretically and practically fast algorithms. This paper provides a comprehensive …
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 extend APG for general nonconvex and nonsmooth programs by introducing a monitor that satisfies the sufficient descent property
We presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms.