Lagrangian Decomposition Algorithm for Allocating Marketing Channels
Authors: Daisuke Hatano, Takuro Fukunaga, Takanori Maehara, Ken-ichi Kawarabayashi
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our algorithm computes better quality solutions than existing algorithms, scales up to graphs of 10M vertices, and performs well particularly in a parallel environment. |
| Researcher Affiliation | Academia | Daisuke Hatano, Takuro Fukunaga, Takanori Maehara, Ken-ichi Kawarabayashi National Institute of Informatics JST, ERATO, Kawarabayashi Large Graph Project {hatano,takuro,maehara,k keniti}@nii.ac.jp |
| Pseudocode | No | The paper describes algorithmic steps and procedures in natural language within the text, but it does not include any formally structured pseudocode blocks or figures explicitly labeled as "Algorithm" or "Pseudocode". |
| Open Source Code | No | The paper mentions an "open-advertising-dataset (https: //code.google.com/p/open-advertising-dataset/)" as the source for their real dataset graphs, but it does not provide any link or statement indicating that the source code for the methodology described in the paper is openly available. |
| Open Datasets | Yes | The third kind of graphs are constructed from open-advertising-dataset (https: //code.google.com/p/open-advertising-dataset/) of queryclick logs, which captures a certain situation in computational advertising. |
| Dataset Splits | No | The paper describes the creation of various problem instances (small-, middle-, and large-scale) from different graph types and parameter settings for evaluation, but it does not specify any explicit train/validation/test dataset splits or their percentages/counts needed for reproduction. |
| Hardware Specification | Yes | We conducted experiments on a Cent OS server with Intel Xeon E5-2670@2.6GHz and 512GB of memory. |
| Software Dependencies | Yes | The algorithms are implemented in Java and compiled with JDK 1.7.0 55. |
| Experiment Setup | Yes | We set the number of iterations in the Lagrangian decomposition algorithm to 20. By preliminary experiments, we conclude that 20 iterations suffice for the Lagrangian decomposition algorithm to output good quality solutions. |