FROS: Fast Regularized Optimization by Sketching

Yingzhen Yang, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Randomized algorithms are important for solving large-scale optimization problems. In this paper, we propose Fast Regularized Optimization by Sketching (FROS) as an efficient solver for a general class of regularized optimization problems. FROS first generates a sketch of the original data matrix, then solves the sketched problem. Different from existing randomized algorithms, FROS handles general Frechet subdifferentiable regularization functions in an unified framework. It is proved that FROS achieves relative-error bounds for the approximation error between the optimization results of the sketched problem and that of the original problem for all convex and certain non-convex regularization. We further propose Iterative FROS which reduces the approximation error exponentially by iteratively invoking FROS. To our best knowledge, our results are among the few in approximation error of sketching algorithms for a broad class of optimization problems with general regularization. Experimental results demonstrate the effectiveness of the proposed FROS and Iterative FROS algorithms.

Original languageEnglish (US)
Title of host publication2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538682098
StatePublished - Jul 12 2021
Event2021 IEEE International Symposium on Information Theory, ISIT 2021 - Virtual, Melbourne, Australia
Duration: Jul 12 2021Jul 20 2021

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2021 IEEE International Symposium on Information Theory, ISIT 2021
CityVirtual, Melbourne

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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