Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
Authors: Sam Reifenstein, Timothee Leleu, Yoshihisa Yamamoto
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our method through numerical experiments on artificial problems. Additionally, we show improved performance when tuning NP-hard combinatorial optimization solvers compared to existing state-of-the-art heuristic derivative-free and Bayesian optimization methods. |
| Researcher Affiliation | Collaboration | 1NTT Research Inc 2Stanford University. Correspondence to: Sam Reifenstein <Sam.Reifenstein@nttresearch.com>. |
| Pseudocode | Yes | Algorithm 2 |
| Open Source Code | Yes | An implementation for our algorithm can be found in the following Git Hub repository: https: //github.com/Sam1234567/DAS-Autotuner? tab=readme-ov-file#das-autotuner |
| Open Datasets | No | The paper uses generated problem instances (e.g., 'random 3-SAT', 'Sherrington-Kirkpatrick (SK) model') based on defined parameters and models, rather than explicitly mentioning or linking to a pre-existing, publicly available or open dataset. |
| Dataset Splits | No | The paper does not explicitly describe train/validation/test dataset splits in the context of model training. It describes generating problem instances and evaluating performance over multiple realizations or trajectories, which is different from standard dataset splitting methodologies. |
| Hardware Specification | No | The paper mentions parallel computation 'such as in a GPU' but does not provide specific hardware details (e.g., CPU or GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers) were mentioned in the paper. |
| Experiment Setup | Yes | The parameters for this algorithm are B0 (initial batch size), γ (batch size exponent) and t which is the integration time step as well as αL and αx in eq. (9). For all results in this paper, we use αL = 1/D, αx = 1 whereas the other parameters depend on the amount of parallelism that can be used and the properties of f. ... we always use wmax = 2, and, except for the benchmark results on the Rosenbrock function (section 3.2), we use wmin = 0. ... tuning the SAT solver with N = 150, α = 4.0 T = 148. ... N = 150, βE = 0.01 (left) and N = 300, βE = 0.005 (right). |