Stochastic L-BFGS Revisited: Improved Convergence Rates and Practical Acceleration Strategies

We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new framework for analyzing convergence, we theoretically improve the (linear) convergence rates and computational complexities of the stochastic LBFGS algorithms in previous works. In addition, we propose several practical acceleration strategies to speed up the empirical performance of such algorithms. We also provide theoretical analyses for most of the strategies. Experiments on large-scale logistic and ridge regression problems demonstrate that our proposed strategies yield significant improvements via-à-vis competing state-of-the-art algorithms.