Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization
This provides an incremental improvement for machine learning practitioners needing efficient optimization methods for structured solutions like sparsity.
The authors tackled the problem of composite convex minimization by proposing a novel accelerated dual-averaging primal-dual algorithm, achieving improved convergence rates and demonstrating advantages for sparse data handling both theoretically and empirically.
Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g., sparsity) efficiently. In this paper, we propose a novel accelerated dual-averaging primal-dual algorithm for minimizing a composite convex function. We also derive a stochastic version of the proposed method which solves empirical risk minimization, and its advantages on handling sparse data are demonstrated both theoretically and empirically.