Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization-Part II: Applications

In Part I of this paper, we proposed and analyzed a novel algorithmic framework, termed penalty dual decomposition (PDD), for the minimization of a nonconvex nonsmooth objective function, subject to difficult coupling constraints. Part II of this paper is devoted to evaluation of the proposed method...

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Vydané v:IEEE transactions on signal processing Ročník 68; s. 4242 - 4257
Hlavní autori: Shi, Qingjiang, Hong, Mingyi, Fu, Xiao, Chang, Tsung-Hui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Array signal processing
Beamforming
Broadcasting antennas
Communication networks
Convergence
Couplings
Decomposition
matrix factorization
Multicast
Multicast algorithms
multicast beamforming
Optimization
Penalty dual decomposition
Relays
Signal processing algorithms
Structured matrices
sum-rate maximization
ISSN:1053-587X, 1941-0476
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Popis
Shrnutí:In Part I of this paper, we proposed and analyzed a novel algorithmic framework, termed penalty dual decomposition (PDD), for the minimization of a nonconvex nonsmooth objective function, subject to difficult coupling constraints. Part II of this paper is devoted to evaluation of the proposed methods in the following three timely applications, ranging from communication networks to data analytics: i) the max-min rate fair multicast beamforming problem; ii) the sum-rate maximization problem in multi-antenna relay broadcast networks; and iii) the volume-min based structured matrix factorization problem. By exploiting the structure of the aforementioned problems, we show that effective algorithms for all these problems can be devised under the PDD framework. Unlike the state-of-the-art algorithms, the PDD-based algorithms are proven to achieve convergence to stationary solutions of the aforementioned nonconvex problems. Numerical results validate the efficacy of the proposed schemes.
Bibliografia:ObjectType-Article-1
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.3001397