OCAM: Out-of-core coordinate descent algorithm for matrix completion

Dongha Lee, Jinoh Oh, Hwanjo Yu

Research output: Contribution to journalArticlepeer-review


Recently, there are increasing reports that most datasets can be actually stored in disks of a single off-the-shelf workstation, and utilizing out-of-core methods is much cheaper and even faster than using a distributed system. For these reasons, out-of-core methods have been actively developed for machine learning and graph processing. The goal of this paper is to develop an efficient out-of-core matrix completion method based on coordinate descent approach. Coordinate descent-based matrix completion (CD-MC) has two strong benefits over other approaches: 1) it does not involve heavy computation such as matrix inversion and 2) it does not have step-size hyper-parameters, which reduces the effort for hyper-parameter tuning. Existing solutions for CD-MC have been developed and analyzed for in-memory setting and they do not take disk-I/O into account. Thus, we propose OCAM, a novel out-of-core coordinate descent algorithm for matrix completion. Our evaluation results and cost analyses provide sound evidences supporting the following benefits of OCAM: (1) Scalability – OCAM is a truly scalable out-of-core method and thus decomposes a matrix larger than the size of memory, (2) Efficiency – OCAM is super fast. OCAM is up to 10x faster than the state-of-the-art out-of-core method, and up to 4.1x faster than a competing distributed method when using eight machines. The source code of OCAM will be available for reproducibility.

Original languageEnglish
Pages (from-to)587-604
Number of pages18
JournalInformation sciences
Publication statusPublished - 2020 Apr

Bibliographical note

Funding Information:
This research was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (IITP-2018-0-00584), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (NRF-2017M3C4A7063570) and (NRF-2016R1E1A1A01942642). ear TEam, Regards, Nandini.

Publisher Copyright:
© 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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