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 | Conference PDF | Archive PDF | Plain Text | 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. |