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
相关论文
共 50 条
  • [21] Robust Stochastic Optimization Made Easy with RSOME
    Chen, Zhi
    Sim, Melvyn
    Xiong, Peng
    MANAGEMENT SCIENCE, 2020, 66 (08) : 3329 - 3339
  • [22] Optimization on microchannel structures made of typical materials based on machine learning
    Yu, Chenyang
    Yang, Ming
    Yao, Jun
    Melhi, Saad
    Elashiry, Mustafa
    El-Bahy, Salah M.
    Tan, Sicong
    Li, Zhigang
    Huang, Shien
    Bao, Ergude
    Zhang, Hang
    ADVANCED COMPOSITES AND HYBRID MATERIALS, 2024, 7 (06)
  • [23] Fast and Intelligent Antenna Design Optimization using Machine Learning
    Gampala, Gopinath
    Reddy, C. J.
    APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL, 2020, 35 (11): : 1350 - 1351
  • [24] Machine Learning Methods as Fast Heuristics for Network Topology Optimization
    Preuschoff, Felix
    Zirkel, Luna
    Moser, Albert
    2024 IEEE 22ND MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, MELECON 2024, 2024, : 127 - 132
  • [25] The Fast Inertial ADMM optimization framework for distributed machine learning
    Wang, Guozheng
    Wang, Dongxia
    Li, Chengfan
    Lei, Yongmei
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2025, 164
  • [26] Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
    Klein, Aaron
    Falkner, Stefan
    Bartels, Simon
    Hennig, Philipp
    Hutter, Frank
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54, 2017, 54 : 528 - 536
  • [27] Fast and Intelligent Antenna Design Optimization using Machine Learning
    Gampala, Gopinath
    Reddy, C. J.
    2020 INTERNATIONAL APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY SYMPOSIUM (2020 ACES-MONTEREY), 2020,
  • [28] Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields
    Befort, Bridgette J.
    DeFever, Ryan S.
    Tow, Garrett M.
    Dowling, Alexander W.
    Maginn, Edward J.
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2021, 61 (09) : 4400 - 4414
  • [29] Project Florida: Federated Learning Made Easy
    Diaz, Daniel Madrigal
    Manoel, Andre
    Chen, Jialei
    Singal, Nalin
    Sim, Robert
    arXiv, 2023,
  • [30] Fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
    Koide-Majima, Naoko
    Majima, Kei
    PHYSICAL REVIEW A, 2021, 104 (06)