Research map / Optimization / Nonconvex and smooth optimization
Accelerated Stochastic Optimization Methods under Quasar-convexity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add momentum (QASGD, quasar-accelerated SGD) to SGD | stochastic gradient descent on learning linear dynamical systems · LDS simulation (Figure 1) | convergence speed / overall complexity | Helped |
| Use bounded gradient assumption-free QASGD with sigma=1 choice (ablation) | QASGD · LDS simulation (Figure 1) | robustness to sigma choice | No clear effect |
| Use a restart scheme in QASVRG (break and start a new stage when <grad, y_{k+1}-y_k> > 0) | QASVRG · LDS simulation (Figure 1) | convergence performance | Helped |
| Accelerated momentum vs plain GD with Bregman-divergence generalization (QAGD) | deterministic gradient method on quasar-convex functions · theory (Table 1) | complexity upper bound | No clear effect |
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