Research map / Optimization / Nonconvex and smooth optimization
Gradientless Descent: High-Dimensional Zeroth-Order Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Project the HalfCheetah linear-policy state to 200 dimensions | GLD with a linear policy · HalfCheetah-v1 | maximum reward: 4064 -> 4342 | Helped |
| Use GLD-Fast rather than GLD-Search | GLD · BBOB benchmarks | optimality gap | Helped |
| Use GLD-Fast rather than ARS | zeroth-order optimization · high-dimensional quadratic objectives | optimality gap | Helped |
| Apply a monotone transformation to the objective | GLD · transformed quadratic objectives | convergence | No clear effect |
| Over-approximate the condition number supplied to GLD-Fast | GLD-Fast · quadratic objectives | optimality gap | Helped |
| Underestimate the condition number supplied to GLD-Fast | GLD-Fast · quadratic objectives | convergence | No clear effect |
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- Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations (ICLR 2025 · similarity 0.87)
- Stability and Generalization of Zeroth-Order Decentralized Stochastic Gradient Descent with Changing Topology (AAAI 2025 · similarity 0.87)
- Toward a Unified Theory of Gradient Descent under Generalized Smoothness (ICML 2025 · similarity 0.87)
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