Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
Authors: Haoyu Peter Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our framework by solving a synthetic graph optimization problem, and two real-world applications including resource allocation in circuit design and approximate computing. Our framework1 largely outperforms the baselines based on naïve relaxation, reinforcement learning, and Gumbel-softmax tricks. |
| Researcher Affiliation | Academia | Haoyu Wang Purdue University EMAIL Nan Wu UCSB EMAIL Hang Yang Georgia Tech. EMAIL Cong Hao Georgia Tech. EMAIL Pan Li Purdue University EMAIL |
| Pseudocode | No | The paper describes procedures (e.g., Definition 1 for rounding) in text, but no explicitly labeled pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | Our code and the datasets are available at: https://github.com/Graph-COM/CO_Proxy Design |
| Open Datasets | Yes | Our code and the datasets are available at: https://github.com/Graph-COM/CO_Proxy Design |
| Dataset Splits | Yes | We follow the settings in [1] and use a 6:2:2 dataset split for training, validating and testing |
| Hardware Specification | Yes | To be fair, all methods run on the same server with a Quadro RTX 6000 GPU. |
| Software Dependencies | No | The paper mentions software like PyTorch [76], PyTorch Geometric [77], DGL [66], and the Adam optimizer [78], but does not provide specific version numbers for any of these dependencies. |
| Experiment Setup | Yes | The Adam optimizer [78] is adopted with a learning rate of 1e-4 and the batch size of 256. For RL training, we use a constant learning rate of 1e-5. We train both the proxies and the Aθ module for 200 epochs. |