Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization
Authors: Shenghe Xu, Shivendra S. Panwar, Murali Kodialam, T.V. Lakshman1684-1691
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test NDP on the linear sum assignment problem, the traveling salesman problem and the talent scheduling problem. Experimental results show that NDP can achieve considerable computation time reduction on hard problems with reasonable performance loss. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY 2Nokia Bell Labs, Crawford Hill, NJ |
| Pseudocode | Yes | Algorithm 1 Pre-training Process of NDP; Algorithm 2 Fine Tuning Process of NDP |
| Open Source Code | No | The paper does not provide any concrete access information (specific link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper mentions generating data for training (e.g., 'rewards are generated from a Beta distribution', 'cities are generated uniformly from the unit square', 'random integers from 1 to 100 for evaluation, and floating numbers from 1 to 100 for training') and refers to using 'the same test dataset and baseline methods as in (Kool, van Hoof, and Welling 2018)' for TSP, but it does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year for a publicly available or open dataset) to any of these datasets. |
| Dataset Splits | Yes | Figure 2 shows the change of validation performance gap during the fine-tuning phase of NDP. |
| Hardware Specification | Yes | Experiments are conducted on a server with one P100 GPU and two Xeon Silver 4114 CPUs. |
| Software Dependencies | No | The paper mentions software like 'Pytorch (Paszke et al. 2017)' and 'Adam optimizer (Kingma and Ba 2014)' but does not provide specific version numbers for these software dependencies, only the citations to their original papers. |
| Experiment Setup | Yes | A learning rate of 0.001 and batch size of 100 is used for both pre-training and fine-tuning. |