A repository for exploring real-world applications of mathematical optimization. It aims to make optimization more accessible to a wider audience. To this end, it provies a simple graphical user interface that allows formulating optimization problems via drag and drop and solves them automatically. The problems solvable in this manner come from optimal design, optimal estimation, and optimal control. Some well-documented examples showcase usage and usefulness. The repository is work-in-progress and as of now contains the following subfolders.
- homepage_tutorials: Folder including the scripts and functions that were used for creating the figures on the atlasoptimization.ch homepage.
- examples: Documented examples of problems solvable via optimization for illustrative purposes.
- optimization_suite: Folder including the main software for formulating and manipulating optimization problems.
- documentation: Documentation of program functionalities
The set of solvable optimization problems includes among others those dealing with transport, scheduling, topology, experiment design, parameter estimation, functional statistics, correlation analysis, uncertainty quantification, system analysis, robust and stochastic control, sequential decisions, and reinforcment learning. We hope this featureset is useful for people without much expertise in programming but willing to do some experimentation with the graphical user interface. The repository is written in Python and meant for open and free use. It requires basic open source packages for optimization and machine learning.