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• W2945753404 abstract "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. A recently developed approach relies on the concept of Bregman distances, which generalizes the standard Euclidean distance. We exploit this theory by proposing a novel Bregman distance for matrix factorization problems, which, at the same time, allows for simple/closed form update steps. Therefore, for non-alternating schemes, such as the recently introduced Bregman Proximal Gradient (BPG) method and an inertial variant Convex--Concave Inertial BPG (CoCaIn BPG), convergence of the whole sequence to a stationary point is proved for Matrix Factorization. In several experiments, we observe a superior performance of our non-alternating schemes in terms of speed and objective value at the limit point." @default.
• W2945753404 created "2019-05-29" @default.
• W2945753404 creator A5024619887 @default.
• W2945753404 creator A5082194697 @default.
• W2945753404 date "2019-05-22" @default.
• W2945753404 modified "2023-09-27" @default.
• W2945753404 title "Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms" @default.
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