Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data

<p>This repository contains the code and data for reproducibility of the paper &#39;Computing Star Discrepancies with Numerical Black-Box<br> Optimization Algorithms&#39;.&nbsp;</p> <p>The following files are included:</p> <p>- TA.zip and DEM.zip: The code used for the TA and DEM algorithms&nbsp;respectively.</p> <p>- experiment_runner: Python file&nbsp;which was&nbsp;used to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package &#39;ioh&#39;, version 0.3.6 or higher). This generates data in IOH-format, which is included in &#39;raw_data.zip&#39;</p> <p>- process_stardicr.R: R script which uses IOHanalyzer to extract the performance from the raw data into csv files for visualization. The resulting csvs are included in &#39;csv_with_pos&#39; for the final results including the corresponding coordinates and &#39;csv_perf.zip&#39;, which contains the convergence information.</p> <p>- Found_Values:&nbsp;The discrepancy values found by TA and DEM, separated by sampler.</p> <p>- A csv file of the relative performance of each of the optimizers compared to the values found by&nbsp;TA is included in &#39;final_precision_table.csv&#39;</p> <p>- Plot_StarDiscr: the python notebook used to generate all figures, except figure 3 which was created using the IOHanalyzer GUI (iohanalyzer.liacs.nl). The full dataset is available on the website under the source &#39;star_discrepancy&#39;</p> <p>- Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.</p>