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Exact(7)
Let (t^) and (t_) be the global maximum point and global minimum point.
where Let be the global maximum point of on Then as we claim that (3.4).
If multiple local maxima exist, the algorithm will find one of them but there is no guarantee that it will be the global maximum.
After a few iterations, it converges to a local maximum, and the local maximum may or may not be the global maximum.
Let t̅, (underline{t}), respectively, be the global maximum point and global minimum point (u(t)) on ([0,T]); then (u' overline{t})=0) and (u' underline {t})=0).
Our partition is likely not to be the global maximum and there are probably alternative local maxima with a high modularity score.
Similar(53)
Thus, f(1) is the global maximum when 0≤b<1.
As with all such search algorithms, there is no guarantee that this local maximum is the global maximum.
Thus, (fleft (frac {1}{b}right)) is the global maximum of f when b≥1. Therefore an optimal τ exists for f in the interval of [0,1].
In addition, even if the obtained optimum by search is the global maximum, most existing search methods directly consider the position of the global maximum as the best one.
The CpA was scaled, so that 1 was the global maximum value in order to have an index that is relative to the smallest habitat loss risk.
More suggestions(15)
be the global ambassador
be the global pattern
be the global marvel
be the global equivalent
be the global platform
be the global standard-bearer
be the global war
be the compulsory maximum
be the global focus
be the global tax
be the global hub
be the global window
be the global player
be the global center
be the absolute maximum
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com