Improved complexities for stochastic conditional gradient methods under interpolation-like conditions
Operations Research Letters(2022)
Abstract
We analyze stochastic conditional gradient methods for constrained optimization problems arising in over-parametrized machine learning. We show that one could leverage the interpolation-like conditions satisfied by such models to obtain improved oracle complexities. Specifically, when the objective function is convex, we show that the conditional gradient method requires O(ϵ−2) calls to the stochastic gradient oracle to find an ϵ-optimal solution. Furthermore, by including a gradient sliding step, we show that the number of calls reduces to O(ϵ−1.5).
MoreTranslated text
Key words
Stochastic conditional gradient,Oracle complexity,Overparametrization,Zeroth-order optimization
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined