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..
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem
Authors: Zhentao Tan, Yadong Mu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model s effectiveness is validated through extensive experiments on self-generated QAP instances of varying sizes and the QAPLIB benchmark. |
| Researcher Affiliation | Academia | 1Center for Data Science, Peking University, China. 2Wangxuan Institute of Computer Technology, Peking University, China. |
| Pseudocode | Yes | At first, we initialize the facility nodes with vectors randomly sampled from a one-hot vector pool with a dimension of Ninit = 128. Details are shown below in Python code. 1 f_init_emb = torch.zeros(size=(batch_size, node_cnt, N_{init})).to(device)... |
| Open Source Code | Yes | The corresponding code and other resources are released at https://github.com/PKUTAN/SAWT. |
| Open Datasets | Yes | QAPLIB (Burkard et al., 1997) benchmarks showcase the model s effectiveness and robust generalization to instances of varying sizes. |
| Dataset Splits | Yes | For all experiments, we use a training set of up to 5120 instances and evaluate results on a test set of 256 different instances from the same distribution. The gap, averaged over 256 validation instances across 400 search steps during training... |
| Hardware Specification | Yes | All experiments are executed on a single NVIDIA 3080Ti GPU (12GB) and a 12th Gen Intel(R) Core(TM) i5-12600KF 3.69 GHz CPU. |
| Software Dependencies | No | Our model SAWT is implemented using PyTorch (Paszke et al., 2019). While PyTorch is mentioned, a specific version number is not provided, nor are other software dependencies with version numbers. |
| Experiment Setup | Yes | We train the model for 200 epochs with a batch size of 512 and an episode length of 400 for all tasks. Table 7 below summarizes the hyper-parameter settings: HYPERPARAMETER QAP10 QAP20 QAP50 QAP100 demb 64 64 64 64 dhidden 64 64 64 64 L 3 2 3 3 lr 10 3 10 3 10 3 10 3 |