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 Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
Authors: Saaduddin Mahmud, Md. Mosaddek Khan, Moumita Choudhury, Long Tran-Thanh, Nicholas R. Jennings
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, University of Dhaka 2School of Electronics and Computer Science, University of Southampton 3Departments of Computing and Electrical and Electronic Engineering, Imperial College London |
| Pseudocode | Yes | We will now describe the DPSA algorithm for solving MIFDCOPs (Algorithm 22). Algorithm 1: Cross-Entropy Sampling, Algorithm 2: The DPSA Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | No | The paper generates random graphs using Erd os R enyi topology and random coefficients for functions, rather than using a pre-existing publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper mentions running algorithms on independently generated problems but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | In order to conduct these experiments, we use a GCP-n2-highcpu-64 instance, a cloud computing service which is publicly accessible at cloud.google.com. |
| Software Dependencies | No | The paper mentions the use of algorithms and frameworks but does not specify version numbers for any software dependencies (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | For all the benchmarks, we use the following parameters for DPSA Itrmax = 2500, Rmax = 12, Smax = 1, Slan = 100, α = 0.5, S = .01 and K = 16. Finally, we use following parameters for DPSA Itrmax = 3000, Rmax = 12, Smax = 1, Slan = 120, α = 0.5, S = 0.005 and K = 25. To select neighbours in DPSA and DSAN, we use both uniform distribution and Normal distribution with σ = 6 over the domain. |