Stochastic Optimization with High-Dimensional Uncertainties - repository code archive

This is the Zenodo repository for the Code used in "Stochastic Optimization with High-Dimensional Uncertainties" The following readme is copied from GitHub. StochOpt_Code Supplementary code used in the generation and postprocessing of the data published in the dissertation "Stochastic Optimization with High-Dimensional Uncertainties" . It includes the code fo reach numerical simulation presented by section and subsegmented into each example. Each folder contains an additional readme file to explain how it works. Additionally, data generated in the dissertation is available on Zenodo at https://doi.org/10.5281/zenodo.18714507 . The data files are structured in the same folder structure as the code. The readme file explaining the data structure which is generated by the code is also included in this repository for clarity about what is generated and stored. Usage Install the included conda enviroment from the provided yaml file. Install it with "conda env create -f 2dspring.yml". (The name is an unfortunate leftover). Now coordinate to the directory of the example which you want to run/test/plot data from. Read the included readme file in the folder to familiarize what the code in the directory does, what parameters are available to change, what data is generated, etc. Specify your desired parameters in the code file. Run it from the shell or whichever execution method you like. License / Permission of use The code is provided as-is without any warranty for its correctness under the MIT license (see LICENSE file) Contact Lennart IgelFriedrich-Alexander-University Erlangen-Nürnberg (FAU), Department of Mathematics, Chair for Applied Mathematics (Continuous Optimization)Email: lennart.g.igel@fau.de