A Randomized Algorithm for Generalized Accelerated Projection Method

Tiancheng Qin, S. Rasoul Etesami

Research output: Contribution to journalArticlepeer-review


We consider the convex feasibility problem, which is to determine a point in the intersection of a finite number of closed convex sets. It was shown in the past literature a so-called generalized acceleration method converges to a feasible point in the intersection of all the sets, assuming the intersection set has a nonempty interior. In this letter, we establish the same convergence result without any assumption on the interior of the feasible set. In particular, we devise a randomized accelerated projection algorithm and prove its linear convergence rate when all the convex sets are half-spaces in a finite-dimensional Euclidean space. Numerical experiments comparing the generalized acceleration method with the classic cyclic projection methods are presented, which justify the fast convergence rate and the out-performance of the proposed algorithm.

Original languageEnglish (US)
Article number9113702
Pages (from-to)85-90
Number of pages6
JournalIEEE Control Systems Letters
Issue number1
StatePublished - Jan 2021


  • Successive projection method
  • convex feasibility
  • convex optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization


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