Our algorithm views the system dynamics as a "black-box", i.e., we only require control of pseudorandom number generator inputs.
CLARANS (Clustering Large Applications based on RANdomized Search) [ 30] algorithm views the process of finding optimal medoids as searching through a certain graph, in which each node represents a set of medoids.
In particular, Reporter Features algorithm views the 'data + network' as a set of hypotheses pertaining to the biological information attributed to the edges in the network (e.g. an edge in a protein-DNA interaction network implies transcriptional regulation, while an edge in a protein-protein interaction network may imply signal transduction).
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The algorithms view the data points as a graph, where the vertices are the data points (instances) and the weighted edges are the similarity between the data points.
The first class of algorithms views the problem (1) as the constrained nonsmooth optimization problem, which can be solved directly by several classical nonsmooth methods, such as subgradient methods, bundle methods, and cutting plane methods, Refs. [6 9].
We note here that the development of the fine CFO tracking with wide tracking range while keeping high estimation accuracy and low computational complexity is beneficial to the design of the synchronization algorithms, viewing the fact that different coarse synchronization algorithms lead to different magnitudes of the fine residual frequency offsets.
Closed loop simulations performed on realistic test cases using a physics based tokamak integrated simulator confirm the relevance of the proposed control algorithm in view of practical implementation.
While the round number corresponds also to the round number of the consensus algorithm, the view number increases only upon reconfiguration.
This approach is a pure parallel algorithm in view of the computation itself and ignores the physical reality of the underlying problem.
