A Novel Stochastic Stratified Average Gradient Method: Convergence Rate and Its Complexity
This work addresses optimization efficiency for large-scale machine learning tasks, offering an incremental improvement over existing stochastic gradient methods.
The paper tackles the problem of gradient variance in stochastic gradient descent by proposing a novel algorithm, SSAG, which achieves a linear convergence rate of O((1-μ/(8CL))^k) with reduced storage and iterative costs, outperforming SAG and other methods in experiments.
SGD (Stochastic Gradient Descent) is a popular algorithm for large scale optimization problems due to its low iterative cost. However, SGD can not achieve linear convergence rate as FGD (Full Gradient Descent) because of the inherent gradient variance. To attack the problem, mini-batch SGD was proposed to get a trade-off in terms of convergence rate and iteration cost. In this paper, a general CVI (Convergence-Variance Inequality) equation is presented to state formally the interaction of convergence rate and gradient variance. Then a novel algorithm named SSAG (Stochastic Stratified Average Gradient) is introduced to reduce gradient variance based on two techniques, stratified sampling and averaging over iterations that is a key idea in SAG (Stochastic Average Gradient). Furthermore, SSAG can achieve linear convergence rate of $\mathcal {O}((1-\fracμ{8CL})^k)$ at smaller storage and iterative costs, where $C\geq 2$ is the category number of training data. This convergence rate depends mainly on the variance between classes, but not on the variance within the classes. In the case of $C\ll N$ ($N$ is the training data size), SSAG's convergence rate is much better than SAG's convergence rate of $\mathcal {O}((1-\fracμ{8NL})^k)$. Our experimental results show SSAG outperforms SAG and many other algorithms.