Research map / Optimization / Nonconvex and smooth optimization
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use single-timescale ALSET instead of two-timescale SCGD | stochastic compositional optimization · 100 Book-to-Market portfolio, iteration 1000 | ln avg squared gradient norm: 2.57 -> 1.65 | Helped |
| use constant stepsizes instead of decreasing stepsizes in ALSET (ablation) | ALSET · 100 Book-to-Market portfolio, iteration 1000 | ln avg squared gradient norm: 1.65 -> 2.06 | Hurt |
| use ALSET-const instead of SCGD | stochastic compositional optimization · 100 Book-to-Market portfolio, iteration 1000 | ln avg squared gradient norm: 2.57 -> 2.06 | Helped |
| use ALSET instead of SCGD at very early iterations | stochastic compositional optimization · 100 Book-to-Market portfolio, iteration 10 | ln avg squared gradient norm: 5.32 -> 5.31 | No clear effect |
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