GENO – Optimization for Classical Machine Learning Made Fast and Easy

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 easyto-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 handwritten, 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 http: //www. geno-project. org.

Authors

• Sören Laue Technical University Kaiserslautern, Germany
• Matthias Mitterreiter Friedrich-Schiller-University, Jena, Germany Data Assessment Solutions GmbH, Hannover, Germany
• Joachim Giesen Friedrich-Schiller Universität Jena

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