4 papers · 1 filter
Composite and Stochastic Smoothed Decentralized Bregman Proximal-Gradient Methods
Shengze Xu, Chenhan Jin, Qingsong Wang +1
Smoothed decentralized Bregman proximal-gradient (SDBPG) methods combine relative-smoothness geometry with edge-dual consensus. We extend SDBPG to node-specific convex nonsmooth re…
Multi-step Inertial Accelerated Doubly Stochastic Gradient Methods for Block Term Tensor Decomposition
Zehui Liu, Qingsong Wang, Chunfeng Cui
In this paper, we explore a specific optimization problem that combines a differentiable nonconvex function with a nondifferentiable function for multi-block variables, which is pa…
Inertial Accelerated Stochastic Mirror Descent for Large-Scale Generalized Tensor CP Decomposition
Zehui Liu, Qingsong Wang, Chunfeng Cui +1
The majority of classic tensor CP decomposition models are designed for squared loss, employing Euclidean distance as a local proximal term. However, the Euclidean distance is unsu…
A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems
Qingsong Wang, Zehui Liu, Chunfeng Cui +1
In this paper, we explore a specific optimization problem that involves the combination of a differentiable nonconvex function and a nondifferentiable function. The differentiable…