Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition
Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
–Neural Information Processing Systems
In this paper, we consider a multi-step version of the stochastic ADMM method with efficient guarantees for high-dimensional problems. We first analyze the simple setting, where the optimization problem consists of a loss function and asingleregularizer(e.g.
Neural Information Processing Systems
Feb-18-2026, 21:24:45 GMT