Research map / Optimization / Nonconvex and smooth optimization
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Gen-Oja instead of alternating Oja + tau steps of averaged SGD (tau=10, 1000, 10000) | Gen-Oja · Synthetic generalized eigenvalue problem (d=20, T=10^6) | log10 sin_B error | Helped |
| vary inner SGD steps tau in two-step baseline (10 vs 1000 vs 10000) (ablation) | two-step Oja+SGD baseline · Synthetic generalized eigenvalue problem | log10 sin_B error | Mixed |
| scale step size alpha by 1, 1/8, 1/16 of alpha*=1/R^2 (ablation) | Gen-Oja · Synthetic generalized eigenvalue problem | log10 sin_B error | No clear effect |
| shrink constant in beta_t = beta*/t by 16x (ablation) | Gen-Oja · Synthetic generalized eigenvalue problem | log10 sin_B error | Hurt |
| add streaming averaging with beta_t proportional to 1/sqrt(t) (ablation) | Gen-Oja · Synthetic generalized eigenvalue problem | log10 sin_B error | Helped |
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