On Low Rank Recovery Problems Using Quadratic Envelope Regularization
Many optimization models from practical problems are too bad to be worked by standard optimization techniques. Here the bad properties include nonconvexity and high discontinuity, such as the problem named low-rank recovery. The traditional approach, nuclear norm minimization, can solve the low-rank recovery but may contain bias. Algorithms like the FBS and the ADMM are highly effective in convex