A sequential constraint relaxation algorithm for rank-one constrained problems

Pan Cao, J. Thompson, H. Vincent Poor

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

Abstract / Description of output

Many optimization problems in communications and signal processing can be formulated as rank-one constrained optimization problems. This has motivated the development of methods to solve such problem in specific scenarios. However, due to the non-convex nature of the rank-one constraint, limited progress has been made in solving generic rank-one constrained optimization problems. In particular, the problem of efficiently finding a locally optimal solution to a generic rank-one constrained problem remains open. This paper focuses on solving general rank-one constrained problems via relaxation techniques. However, instead of dropping the rank-one constraint completely as is done in traditional rank-one relaxation methods, a novel algorithm that gradually relaxes the rank-one constraint, termed the sequential rank-one constraint relaxation (SROCR) algorithm, is proposed. Compared with previous algorithms, the SROCR algorithm can solve general rank-one constrained problems, and can find feasible solutions with favorable complexity.
Original languageUndefined/Unknown
Title of host publication2017 25th European Signal Processing Conference (EUSIPCO)
Pages1060-1064
Number of pages5
DOIs
Publication statusPublished - 1 Aug 2017

Keywords / Materials (for Non-textual outputs)

  • concave programming
  • relaxation theory
  • signal processing
  • SROCR algorithm
  • generic rank-one constrained optimization problems
  • locally optimal solution
  • nonconvex nature
  • optimization problems
  • rank-one constrained problems
  • sequential constraint relaxation algorithm
  • sequential rank-one constraint relaxation algorithm
  • Complexity theory
  • Convex functions
  • Eigenvalues and eigenfunctions
  • Europe
  • Optimization
  • Signal processing
  • Signal processing algorithms

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