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Exact(15)
N1,..., N P is equivalent to minimizing the function, where N= [N1... N P ]T.
By minimizing the function, we get the next expression for the orientation (18).
Consequently, if is a multiple of P then, the solution of minimizing the function in ℝ P coincides the solution of minimizing the function f(N) in ℕ P. Thus, the optimal placement minimizing the MSRL is.
It was shown in [15] that the stable state of this circuit network can be found by minimizing the function (2).
But it may be possible to find some sort of approximate fixed point of T by minimizing the function (d x, Tx)).
The above formula for the Riemannian gradient now allows to implement the geometric CG algorithm for minimizing the function g2as define in (14) in a straightforward way.
Similar(45)
Our first approach to optimizing stability is to directly minimize the function α(A x)).
It seems to me important, therefore, not to minimize the function of jurisdictional limitations upon adjudication by expressing views on the merits.
Thus, we search for the value of (varvec{lambda }) that minimizes the function (F varvec{lambda })).
To minimize the function of loss circulation, two optimizing algorithms are examined.
The objective of districting is to maximize or minimize the function of the weights between different districts.
More suggestions(15)
reduced the function
limit the function
minimize the function
minimizing the query
minimizing the effort
minimizing the processing
minimizing the level
minimizing the impact
minimizing the number
minimizing the damage
minimizing the embarrassment
minimizing the energy
minimizing the skewness
minimizing the entropy
minimizing the risk
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