Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Zeroth-Order Optimization for Composite Problems with Functional Constraints
Authors: Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu7453-7461
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical experiments to demonstrate the performance of our proposed ZO-i ALM. We consider the problem of resource allocation in sensor networks and the adversarial example generation problem. |
| Researcher Affiliation | Collaboration | Zichong Li1, Pin-Yu Chen2, Sijia Liu3, Songtao Lu2, Yangyang Xu1 1Department of Mathematical Sciences, Rensselaer Polytechnic Institute 2IBM Research 3Department of Computer Science and Engineering, Michigan State University |
| Pseudocode | Yes | Algorithm 1: Zeroth-order inexact augmented Lagrangian method (ZO-i ALM) and Algorithm 2: Zeroth-order accelerated proximal coordinate update for (15): ZO-APCU(G, H, µ, L, ε) |
| Open Source Code | No | The paper does not provide a direct link to source code or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | In the test, we use the ovarian cancer dataset (Conrads et al. 2004; Petricoin III et al. 2002) that are from m = 216 patients. Each data point has d = 4, 000 features and a label indicating whether the corresponding patient has ovarian cancer. We first use MATLAB s built-in lasso function (with λ = 0.01) to train a LASSO regression model parameterized by θ. |
| Dataset Splits | No | The paper describes the datasets used and some parameters but does not provide specific details on training, validation, or test dataset splits or splitting methodology. |
| Hardware Specification | Yes | All the tests were performed in MATLAB 2019b on a Macbook Pro with 4 cores and 16GB memory. |
| Software Dependencies | Yes | All the tests were performed in MATLAB 2019b on a Macbook Pro with 4 cores and 16GB memory. |
| Experiment Setup | Yes | We set d = 80, λ = 0.5, and ε = 0.5. ... In each call to the ZO-i PPM subroutine, we set the smoothness parameter to ˆLk = 50 + 0.3βk. We tune the parameters of ZO-Ada MM to α = 1, β1 = 0.75, β2 = 1, and fix the step size to 0.01 in ZO-Prox SGD. For each method, we choose a = 10 6 as the sampling radius and wk = 1/c(xk) as the dual step size. and In (23), we set λ = 0.01 and ε = 0.1. Due to the large variable dimension, we set ε = 1 in stopping conditions. ... In each method, we set a = 10 6 as the sampling radius and wk = 1/c(xk) as the dual step size. |