Consensus-ADMM for General Quadratically Constrained Quadratic Programming

Nonconvex quadratically constrained quadratic programming (QCQP) problems have numerous applications in signal processing, machine learning, and wireless communications, albeit the general QCQP is NP-hard, and several interesting special cases are NP-hard as well. This paper proposes a new algorithm...

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Veröffentlicht in:	IEEE transactions on signal processing 2016-10, Vol.64 (20), p.5297-5310
Hauptverfasser:	Kejun Huang, Sidiropoulos, Nicholas D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithm design and analysis Algorithms Alternating direction method of multipliers (ADMM) Array signal processing Constraints Convergence Cost function feasible point pursuit multicast beamforming non-convex quadratically constrained quadratic programming (QCQP) Optimization phase retrieval Quadratic programming semi-definite relaxation (SDR) Signal processing Signal processing algorithms State of the art Transaction processing Wireless communication
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