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..
Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints
Authors: Tianyu Zhao, Xiang Pan, Minghua Chen, Steven Low
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results over IEEE test cases show that it outperforms existing strong DNN baselines in ensuring 100% feasibility and attaining consistent optimality loss (<0.19%) and speedup (up to 228) in both light-load and heavy-load regimes, as compared to a state-of-the-art solver. |
| Researcher Affiliation | Collaboration | Tianyu Zhao1,2, Xiang Pan2, Minghua Chen3,*, Steven H. Low4 1Lenovo Machine Intelligence Center, 2The Chinese University of Hong Kong, 3City University of Hong Kong, 4California Institute of Technology |
| Pseudocode | Yes | Algorithm 1: Determining Suf๏ฌcient DNN Size |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate its performance over IEEE 30-/118-/300bus test cases (tpc, 2018) on the input load region of [100%, 130%] of the default load covering both the light-load ([100%, 115%]) and heavy-load ([115%, 130%]) regimes, respectively. (tpc, 2018) refers to Power Systems Test Case Archive, 2018. http://labs.ece.uw.edu/pstca/. |
| Dataset Splits | No | The paper mentions 'training set' and 'test setting' but does not provide specific details on training, validation, and test splits (e.g., percentages or exact counts) needed for reproduction. |
| Hardware Specification | Yes | We conduct simulations in Cent OS 7.6 with a quad-core (i7-3770@3.40G Hz) CPU and 16GB RAM. |
| Software Dependencies | No | The paper mentions software like 'Pypower' and 'Gurobi' but does not provide specific version numbers for these or any other key software dependencies. |
| Experiment Setup | Yes | The paper describes the loss function (Equation 11) with weighting factors w1 and w2. A footnote in Table 1 states: 'The training epochs for the other DNN schemes are 200'. |