Scalable Online Learning framework created by the LIBOL team at SMU

Project Lead:
Steven HOI
Lead Developer:
Yue Wu

LIBSOL: Library for Scalable Online Learning

LIBSOL is an open-source library for scalable online learning algorithms, especially for high-dimensional data. It provides a family of regular and sparse online learning algorithms for binary and multi-class classification tasks, and is designed with high efficiency, scalability, portability and extensibility. LIBSOL provides easy-to-use command-line tools, python wrappers and library calls for users and developers, and comprehensive documents. LIBSOL is not only a machine learning toolbox, but also a comprehensive research platform for online learning . LIBSOL is released under the Apache 2.0 open source license.


Speed. Implemented in C++ and carefully optimized to reduce time and memory cost. We believe LIBSOL is one of the fastest implementations for large-scale online learning available (faster than VW and Liblinear).
Scalability. Data are stored in a sparse structure. All operations are optimized with sparse data structure.
Portability. All codes follow C++11 standard. No dependency on external libraries. Organized by CMake so that users on different platforms can build the library easily. LIBSOL can run on almost every platform.
Extensibility. Extensible code fosters active development. Easy to add new algorithms and new solutions.
Community: Join our LIBOL community on Github for more about Online Learning research.



Please cite the LIBSOL technical report in your publications if it helps your research:

  Author = {Wu, Yue and Hoi, Steven C.H. and Yu, Nenghai},
  Journal = {SMU Technical Report (SMU-TR-2016-07-25)},
  Title = {LIBSOL: A Library for Scalable Online Learning Algorithms},
  Year = {2016}

Contact Us

Contact us if you have any question: Steven Hoi


The project was partly supported by SMU MOE Tier 1 research grant. The work was done when Yue Wu visited Dr Hoi's research group.