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A sequential constraint relaxation algorithm for rank-one constrained problems

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Original languageUndefined/Unknown
Title of host publication2017 25th European Signal Processing Conference (EUSIPCO)
Number of pages5
Publication statusPublished - 1 Aug 2017


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.

    Research areas

  • 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

ID: 48474883