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..
DC3: A learning method for optimization with hard constraints
Authors: Priya L. Donti, David Rolnick, J Zico Kolter
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate DC3 for convex quadratic programs (QPs), a simple family of non-convex optimization problems, and the real-world task of AC optimal power flow (ACOPF). Each experiment is run 5 times for 10,000 examples x (with train/test/validation ratio 10:1:1). |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University, 2Mc Gill University and Mila, 3Bosch Center for AI |
| Pseudocode | Yes | A schematic of the DC3 framework is given in Figure 1, and corresponding pseudocode is given in Algorithm 1. |
| Open Source Code | Yes | Code for all experiments is available at https://github.com/locuslab/DC3 |
| Open Datasets | Yes | Each experiment is run 5 times for 10,000 examples x (with train/test/validation ratio 10:1:1). During training, we use examples x with entries drawn i.i.d. from the uniform distribution on [-1, 1]. We assess our method on a 57-node power system test case available via the MATPOWER package. |
| Dataset Splits | Yes | Each experiment is run 5 times for 10,000 examples x (with train/test/validation ratio 10:1:1). |
| Hardware Specification | Yes | all neural nets and the qpth optimizer were run with full parallelization on a Ge Force GTX 2080 Ti GPU. The OSQP, IPOPT, and PYPOWER optimizers were run sequentially on an Intel Xeon 2.10GHz CPU |
| Software Dependencies | No | The paper mentions software like PyTorch, OSQP, IPOPT, and PYPOWER/MATPOWER, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The following parameters were kept fixed for all neural network-based methods across all experiments... Epochs: 1000 Batch size: 200 Hidden layer size: 200 (for both hidden layers) Correction procedure stopping tolerance: 10^-4 Correction procedure momentum: 0.5 |