Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty

Mohammadreza Chamanbaz, Fabrizio Dabbene, Roberto Tempo, Venkatakrishnan Venkataramanan, Qing Guo Wang

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


In this technical note, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respectively, is given. The proposed methods allow to enlarge the applicability of the existing randomized methods to real-world applications involving a large number of design variables. Since the proposed approach does not provide a priori bounds on the sample complexity, extensive numerical simulations, dealing with an application to hard-disk drive servo design, are provided. These simulations testify the goodness of the proposed solution.

Original languageEnglish
Article number7308002
Pages (from-to)2565-2571
Number of pages7
JournalIEEE Transactions on Automatic Control
Issue number9
Publication statusPublished - Sept 2016
Externally publishedYes


  • Convex optimization
  • hard-disk servo design
  • randomized algorithms
  • sequential algorithms

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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