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Deng Kangkang (kk19931208163.com) Abstract: The main task of genetic regulatory networks is to construct a sparse probabilistic Boolean network (PBN) based on a given transitionprobability matrix and a set of Boolean networks (BNs). In this paper, a Bregman alternating direction method of multipliers (BADMM) is proposed to solve the minimization problem raised in PBN. All the customized subproblemsolvers of the BADMM do not involve matrix multiplication, consequently the proposed method is in a position to deal with some hugescale problems. The convergence to stationary point of the BADMM is proved under some mild conditions. Numerical experiments show that the BADMM is effective and efficient comparing with some existing methods. Keywords: Genetic regulatory networks, Sparse probabilistic Boolean network, $L_{\frac{1}{2}}$regularization, Separable minimization, Bregman alternating direction method of multipliers. Category 1: Applications  Science and Engineering Category 2: Applications  Science and Engineering (Biomedical Applications ) Category 3: Nonlinear Optimization (Constrained Nonlinear Optimization ) Citation: Download: [PDF] Entry Submitted: 04/11/2017 Modify/Update this entry  
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