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

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

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DOI10.5281/zenodo.7630260Zenodo7630260MaRDI QIDQ6690576

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 10 February 2023

Copyright license: No records found.



This repository contains the code and data for reproducibility of the paper Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms. The following files are included: - TA.zip and DEM.zip: The code used for the TA and DEM algorithmsrespectively. - experiment_runner: Python filewhich wasused to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package ioh, version 0.3.6 or higher). This generates data in IOH-format, which is included in raw_data.zip - 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 csv_with_pos for the final results including the corresponding coordinates and csv_perf.zip, which contains the convergence information. - Found_Values:The discrepancy values found by TA and DEM, separated by sampler. - A csv file of the relative performance of each of the optimizers compared to the values found byTA is included in final_precision_table.csv - 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 star_discrepancy - Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.






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