Sentence examples for sparse enough for from inspiring English sources
The phrase "sparse enough for" is correct and usable in written English.
It can be used when describing a situation or condition where something is sufficiently limited or minimal to meet a specific requirement or purpose. Example: "The data set was sparse enough for the analysis to yield meaningful results without overwhelming the model."
The arrangements fluctuate like jam sessions; one song, "Cold," builds a funk ziggurat around the sound of a swishing, slapping windshield wiper, while "Playing Possum" is stripped down to acoustic guitar, vocal and occasional percussion, sparse enough for a band Maxwell often cites: Radiohead.
More specifically, are genetic networks sparse enough for naturally segregating smaller-effect mutations to reveal individual network connections?
If that doesn't seem sparse enough, her Web site, anebrun.com, offers for download "Sketches," which features the same tunes in demo form.
Changes to the blowout lobe surrounding the feature were also determined for areas with sparse enough vegetation cover to allow laser returns from the sand surface.
First, many real taxon coverage patterns are sparse enough that they are not decisive for all trees.
Game was abundant, land free for the clearing, settlement sparse enough to prevent epidemics.
For a signal whose wavelet representation fails to be sparse enough, it is more convenient to impose the penalized shrinkage condition (P2) instead of zero forcing since small coefficients may contain significant information about the signal.
Inside, things are sparse enough.
As for the intermittently connected mobile ad hoc networks, since the node distribution is sparse enough, we could ignore the impact of the channel collision.
Previous work has established that a representation is unique if it is sparse enough, requiring.
For a given orthonormal basis, the expansion of the tampering in that basis, that is,, might not be sparse enough with respect to the number of available random projections and the optimization algorithm might not converge to a feasible solution.
