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Based on the above discussion, we can formulate the optimal path selection problem as a linear optimization using the objective function in (5) with constraints of the flow conservation in (8), the reliability in (9), and the wireless link scheduling in (12) and (13).
Next we will reformulate problem (2.4) as a linear optimization problem.
The problem is posed as a linear optimization problem that can be solved efficiently.
We need the following proposition to reformulate (2.4) as a linear optimization problem.
The algorithm determines the probabilistic transmission rates and the problem is posed as a linear optimization problem.
Second, we formulate the cross-layer design problem as a linear optimization problem to minimize the power cost of the whole network, using the minimum per-hop power cost determined in the first step as input and assuming time division multiple access (TDMA) at the MAC layer.
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Users can specify the stoichiometric matrix, the objective function, and the desired bounds in generic linear optimization programs such as GNU Linear Programming Kit (GLPK) and linprog (a linear optimization toolbox within MATLAB), or with a tool such as the COBRA Toolbox [ 23].
We can see that the optimization problem P- 2 is a linear optimization as the objective function and the constraints are all linear functions.
The next code section constructs a linear optimization problem as described in the Analyses section.
Therefore, a linear optimization algorithm such as Newton method cannot be applied directly to get an estimate of the vector σ.
We will formulate the EVM minimization problem as a convex linear optimization problem and obtain an EVM lower bound.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com