GENO – Optimization for Classical Machine Learning Made Fast and Easy


  • Sören Laue Friedrich-Schiller-University Jena and Data Assessment Solutions GmbH
  • Matthias Mitterreiter Friedrich-Schiller-University Jena
  • Joachim Giesen Friedrich-Schiller-University Jena



Most problems from classical machine learning can be cast as an optimization problem. We introduce GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver, i.e., Python code, that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class.

An online interface to our framework can be found at




How to Cite

Laue, S., Mitterreiter, M., & Giesen, J. (2020). GENO – Optimization for Classical Machine Learning Made Fast and Easy. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13620-13621.