Convergence rates for the stochastic gradient descent method for non-convex objective functions
We prove the convergence to minima and estimates on the rate of convergence for the stochastic gradient descent method in the case of not necessarily locally convex nor contracting objective functions. In particular, the analysis relies on a quantitative use of mini-batches to control the loss of it...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Journal of Machine Learning Research
2020
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