There are totally (K+1)(T+1) continuous variables in problem P2, where T is typically of the order of 104 to 105.
(G = left( {V,E} right)) denotes a directed acyclic graph, (V) is a set of nodes denoting variables in problem domain, and (E) is a set of arcs denoting the causal dependency between variables.
Then the variables in Problem (P) of Section "Numerical results" become Π r t = P t = r t α (14) Ψ r t, d tr = Π r t − 9 − 21.29 × log d tr (15).
To solve problem (P1) in an efficient and distributed manner via optimization decomposition, we first need to overcome the complexity imposed by the coupling of the optimization variables in problem's objective function, due to the nature of the two-tier SINR in (1).
BN is expressed as a complex and causal diagram intuitively, and can be denoted by a binary ({text{B}} = leftlangle {G,theta } rightrangle): (G = left( {V,E} right)) denotes a directed acyclic graph, (V) is a set of nodes denoting variables in problem domain, and (E) is a set of arcs denoting the causal dependency between variables.
The dependent variables in these problems are defined as y∈{-1,+1} in two-class problems or as y∈{1, 2, …, G} in multi-class problems.
The key merits of ADMM is its ability to split or decouple multiple variables in optimization problems, which enables one to find a solution to a large-scale global optimization problem by coordinating solutions to smaller sub-problems.
Therefore, this study proposes a technique for treating free variables in GGP problems.
The issue of noise and redundant feature variables in classification problems has been well addressed in statistical stepwise discriminant analysis.
The paper presents the results of applied research and application of sensor networks as a new emerging type of "distributed sensor" for physical variables in engineering problems.
