The Mixing of Markov Chains on Linear Extensions in Practice
Authors: Topi Talvitie, Teppo Niinimäki, Mikko Koivisto
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results suggest that the Markov chain approach to sample linear extensions can be made to scale well in practice... We have studied empirically the performance of the Markov chain algorithms of Section 4 using partial orders of different types and sizes. ... The results are shown in Figure 2. |
| Researcher Affiliation | Academia | Topi Talvitie Dept. of Computer Science University of Helsinki topi.talvitie@helsinki.fi Teppo Niinim aki Dept. of Computer Science Aalto University teppo.niinimaki@aalto.fi Mikko Koivisto Dept. of Computer Science University of Helsinki mikko.koivisto@helsinki.fi |
| Pseudocode | No | The paper describes algorithms (e.g., 'The Karzanov Khachiyan Chain', 'The Insertion Chain') in textual form, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for source code related to the methodology described. |
| Open Datasets | Yes | We consider the following classes of DAGs of sizes 10 n 50 generating the partial orders. AVGDEG(k)... MAXINDEG(k)... BIPARTITE(p)... GRIDTREE(k)... TWOPATHS... LADDER... The first four classes are adopted from Kangas et al. [2016]. |
| Dataset Splits | No | The paper mentions using 'partial orders of different types and sizes' and lists generation methods for 'test instance classes', but it does not provide specific training/validation/test dataset split information. |
| Hardware Specification | No | The paper states 'We implemented the simulation algorithms in C++, also paying attention to code optimization. We measured the performance over all the test instances.' but provides no specific details about the hardware used for these measurements or experiments. |
| Software Dependencies | No | The paper states 'we implemented the simulation algorithms in C++', but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Putting ϵ = 0.01 and K = 5 105, the confidence of each returned interval is at least 0.999 (cf. Theorem 2). For the exact samplers, we simply report the average number of iterations over 105 runs. |