GENO - Optimization for Classical Machine Learning Made Fast and Easy

被引:0
|
作者
Laue, Soeren [1 ,2 ]
Mitterreiter, Matthias [1 ]
Giesen, Joachim [1 ]
机构
[1] Friedrich Schiller Univ Jena, Fac Math & Comp Sci, Ernst Abbe Pl 2, D-07743 Jena, Germany
[2] Data Assessment Solut GmbH, Hannover, Germany
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 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.
引用
收藏
页码:13620 / 13621
页数:2
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