Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions
Authors: Yuya Hikima, Akiko Takeda
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our simulation experiments with real data on a retail service application show that our methods output solutions with lower objective values than the conventional zeroth-order methods. |
| Researcher Affiliation | Academia | 1Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan 2Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Zeroth-order method with the improved one-point gradient estimator Algorithm 2: Zeroth-order method with the two-point gradient estimator |
| Open Source Code | Yes | Code https://github.com/Yuya-Hikima/AAAI25-Zeroth Order-Methods-for-Nonconvex-Stochastic-Problemswith-Decision-Dependent-Distributions |
| Open Datasets | Yes | We performed simulation experiments with real retail data from a supermarket service provider in Japan.5 All 5We used publicly available data, New Product Sales Ranking , provided by KSP-SP Co., Ltd, http://www.ksp-sp.com. Accessed August 15, 2024. |
| Dataset Splits | No | The paper mentions using real retail data but does not specify how this data was split into training, validation, or test sets for the experiments. |
| Hardware Specification | Yes | All experiments were conducted on a computer with an AMD EPYC 7413 24-Core Processor, 503.6 Gi B of RAM, and Ubuntu 20.04.6 LTS. |
| Software Dependencies | Yes | The program code was implemented in Python 3.8.3. |
| Experiment Setup | Yes | Proposed-1 (mini-batch). We implemented Algorithm 1 with µ0 := 0.19, µmin := 0.0001, c0 := P20 j=1 f(x0, ξj(x0)), smax := 10, β := 0.001 0.95k+1, γ = 0.95, mk = 30 + 2k, and M = 0.1, where k is the current iteration number. |