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It is possible to limit the two behaviors of the variable B l (i) by computing an optimal threshold (hat {eta }_{text {opt}}) such that: left{ begin{aligned} text{If}~~ B_{l}(i)> hat{eta}_{text{opt}} ~~text{then}~~ tilde{mathbf{a}}_{i}^{(l)} in mathcal{C}^{perp} text{If}~~ B_{l}(i leq hat{eta}_{text{opt}} ~~text{then} ~~tilde{mathbf{a}}_{i}^{(l)} notin mathcal{C}^{perp} end{aligned} right.
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Two methods for computing an optimal rate vector are proposed.
We then develop a heuristic for computing an optimal tanglegram for two rooted phylogenetic networks.
The previous section describes a heuristic for computing an optimal tanglegram.
Even computing an optimal 2-seed of usable weight and length is infeasible.
We also assess the quality of our solutions by computing a lower bound of the optimal solution, and by representing a large set of locally optimum solutions.
We give lower bounds on these ranks and show that they are sharp by providing an algorithm that computes an optimal solution.
We present a decentralized control strategy that permits each vehicle to autonomously compute an optimal trajectory by using only locally generated information.
We simulate phase drift by computing a running average of the phase variance, with a pseudo-randomly chosen phase, near the optimal phase, for each measurement.
In another study, a power optimization objective is gained by computing the optimal control settings of wind turbines using data mining and an evolutionary strategy algorithm (see [5]).
The computational burden has been minimized to a great extent by computing the optimal state feedback gains and the Kalman state space model off-line.
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