A proximal stochastic gradient method with variance reduction and adaptive steps is shown to converge strongly at rate O(sqrt(1/k)) for convex composite problems when the smooth term is Lipschitz continuous.
Tibshirani, Regression shrinkage and selection via the lasso, Journal of The Royal Statistical Society Series B: Statistical Methodology, 58(1) (1996), 267-288
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Proximal Stochastic Gradient Method with Adaptive Step Size and Variance Reduction for Convex Composite Optimization
A proximal stochastic gradient method with variance reduction and adaptive steps is shown to converge strongly at rate O(sqrt(1/k)) for convex composite problems when the smooth term is Lipschitz continuous.