Research map / Optimization / Nonconvex and smooth optimization
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use unified shuffling rather than i.i.d. sampling | SGD in the general non-convex setting · Non-convex | gradient complexity to a stationary point: \Ocal\left(\sigma^2/\hat{\varepsilon}^2\right) -> \Ocal\left(n\sigma/\hat{\varepsilon}^{3/2}\right) | Helped |
| Use the proposed average-PL and generalized star-smooth-convex framework with small solution variance | Unified shuffling SGD · Non-convex over-parameterized finite-sum problems | gradient complexity to a global solution: \Ocal\left( \frac{n}{\hat{\varepsilon}^{3/2}} \right) | Helped |
| Use arbitrary unified shuffling schemes rather than restricting the analysis to one permutation strategy | Shuffling SGD · Non-convex over-parameterized finite-sum problems | convergence guarantee | Helped |
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