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The method solves a sub-optimization problem every iteration to obtain optimal boundary velocities.
Investigate the optimal boundary conditions on (partialOmega_{1}). .
Through the network evolutions, the optimal (or near optimal) boundary points are detected.
Considering (13), the optimal boundary points can be obtained as (14).
The optimal boundary conditions to our problem are given in Theorem 2.2.
In this article, we shown optimal boundary regularity of (1.1) under controllable growth condition.
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Globally optimal boundaries are then obtained after processing random walk segmentation with automatically determined background and foreground seeds.
The examples demonstrate that the optimal boundaries produced by FG ESO are much smoother than those by traditional ESO.
We show that for Gaussian inputs the optimal boundaries are planar, but for non Gaussian inputs the curvature is nonzero.
By changing the weight factors, the optimal solutions to P4 can collectively form the Pareto-optimal boundary of a power-time region while employing DF relaying protocol.
Briefly, the SVM classifier principle is to find the optimal decision boundary between classes by maximizing the margin hyperplanes (the geometrical representation of the decision boundaries in multi-dimension) between the support vectors.
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