Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms

Understand

Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used.

• They usually suffer from the major drawback that the solution is biased towards one of the optimization variables.
• A remedy is non-alternating schemes.
• However, due to a lack of Lipschitz continuity of the gradient in matrix factorization problems, convergence cannot be guaranteed.