Research map / Optimization / Nonconvex and smooth optimization
Online Convex Optimization in the Random Order Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Random-order model instead of adversarial order for OGD on cumulative strongly convex losses | Online gradient descent · Theory: quadratic losses with cumulative strong convexity | regret | Helped |
| Rank-k SVD per iteration instead of full SVD in online PCA OGA (ablation) | Algorithm 1 (online PCA) · Synthetic data and MNIST | regret comparable to full SVD | No clear effect |
| Increasing k in rank-k SVD projection (ablation) | Algorithm 1 (online PCA) · Synthetic data | fraction of iterations with projection rank above k: 0.0082% at k=1 to 0.37% at k=7 | Hurt |
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