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Therefore, we recently extended the Buice et al. neural master equation by formulating the network population dynamics in terms of a stochastic hybrid system also known as a 'velocity' jump Markov process [27].
There are several other setups of formulating the network recovery problem in terms of a sparse linear model.
By formulating the network querying problem as a conditional random fields (CRF) model, which is widely used in the fields of machine learning, we designed efficient algorithms specific to different structures of query networks.
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First, we formulate the network topology abstraction problem as a Mixed-Integer Linear Program (MILP).
We formulate the network lifetime maximization and fair rate allocation both as constrained maximization problems.
Based on this wideband sensing approach, we further formulate the network capacity optimization problem with the guaranteed PU link rate, and derive the corresponding optimum threshold.
Then we formulate the network utility maximization problem subject to constraints on this achievable rate region, and analyze the complexities of both node- and path-based formulation with no network coding, OTIC network coding, and overhearing network coding.
In this study, we formulate the network design problem as a single-level optimization problem with equilibrium constraints, and then we transform the equilibrium constraints into a set of mixed-integer constraints and linearize the travel time function.
Liang and Luo [28] formulates the network lifetime maximization for the h-hop constrained multiple-sink mobility problem such that the total travel distance of each sink is bounded by L, and the maximum number of hops from each sensor to a sink is bounded by h, where h≥1.
However, we lack a good way to formulate the network structure.
Upon doing so, we formulate the network identification algorithm as a bi-level optimization problem, governed by the hypothesis of network sparsity.
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CEO of Professional Science Editing for Scientists @ prosciediting.com