DC3: A learning method for optimization with hard constraints
Authors: Priya L. Donti, David Rolnick, J Zico Kolter
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 |