Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes
Authors: Weijia Shao
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work proposes an algorithm improving the dimensionality dependence for gradient-free optimisation over cross-polytopes... The idea can be further applied to optimising nonsmooth and non-convex functions. We propose an algorithm with a convergence depending on O(d), which is the best-known dimensionality dependence... Despite the theoretically advantageous characteristics of the proposed algorithms, it is imperative to acknowledge the practical limitations of the idea... In any case, a systematic examination of the proposed algorithm s performance through rigorous experimentation is required. |
| Researcher Affiliation | Academia | 1Unit 2.6 Workplaces, Safety of Machinery, Operational Safety, Federal Institute for Occupational Safety and Health, Dresden, Germany. |
| Pseudocode | Yes | Algorithm 1 Mirror Descent Framework Algorithm 2 Gradient Estimator: GE(f, x, λ, γ) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on algorithm design and complexity analysis; it does not include empirical studies with datasets. |
| Dataset Splits | No | This is a theoretical paper focusing on algorithm design and complexity analysis; it does not include empirical studies with dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper that does not report on conducted experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not report on conducted experiments, therefore no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper that does not report on conducted experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |