Counting the Optimal Solutions in Graphical Models
Authors: Radu Marinescu, Rina Dechter
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate empirically our proposed counting algorithms on four benchmarks for graphical models. All experiments were run on a 2.6GHz processor with 10GB of RAM. ... Table 1 summarizes the results obtained on the grid and random domains. ... In Table 2 we show the results for solving the ISCAS and SPOT5 problem instances. ... Figure 3 plots the running time of AOBB and the number of optimal solutions as a function of the perturbation value (p) for two representative problem classes from the grid and random domains. |
| Researcher Affiliation | Collaboration | Radu Marinescu IBM Research Dublin, Ireland radu.marinescu@ie.ibm.com Rina Dechter University of California, Irvine Irvine, CA 92697, USA dechter@ics.uci.edu |
| Pseudocode | Yes | Algorithm 1 BE for #opt |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | No | The paper mentions "ISCAS circuits [23]" and "SPOT5 satellite scheduling benchmark [24]" and states they were obtained from "UCI Graphical Models Repository (graphmod.ics.uci.edu)" but it doesn't provide specific citations for the authors and year for the datasets themselves, only for the papers describing them, and doesn't specify if the random networks created are publicly available or how to access them. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It only mentions generating random instances for grid and random problems and using real-world WCSP instances. |
| Hardware Specification | Yes | All experiments were run on a 2.6GHz processor with 10GB of RAM. |
| Software Dependencies | No | The paper mentions using a "mini-bucket heuristic MBE(i)" and |
| Experiment Setup | Yes | for grid problems, m ranged between 8 and 14, respectively (so that the number of variables varied between 64 and 196); for random problems, the number of variables ranged between 60 and 120, respectively. In all cases, the domain size of the variables was set to 3. The function values were distributed uniformly at random between 1 and 10. ... we post-processed each function by randomly setting p percent of the function values to 1. We refer to p as the perturbation parameter ... We set the i-bound to 10 and allowed a 1 hour time limit to all algorithms. |