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Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization
Further, we show that the iteration complexity of the proposed method is $O(n\varepsilon^{-2})$ to achieve $\epsilon$-stationary point, where $n$ is the number of blocks of coordinates.
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems
In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continuous gradient assumption (e. g., the nonnegative matrix factorization (NMF) problem).
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization
We propose a novel distributed algorithm, called \textit{distributed incremental block coordinate descent} (DID), to solve the problem.
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