Research map / Optimization / Nonconvex and smooth optimization
On the Convergence of mSGD and AdaGrad for Stochastic Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use fixed-coefficient momentum in place of SGD (\alpha=0) | mSGD · Smooth stochastic optimization, possibly non-convex | expected squared gradient norm convergence rate: O\Big(e^{-\sum_{i=1}^{n}\frac{s}{p}\epsilon_{i}}\Big) -> O\Big(e^{-\sum_{i=1}^{n}\frac{s\epsilon_{i}}{p(1-\alpha)^{2}}}\ | Helped |
| Use standard norm-form AdaGrad with its adaptive step size | AdaGrad · Smooth stochastic optimization, possibly non-convex | almost-sure asymptotic convergence | Helped |
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