The phrase "approximation is better" is correct and usable in written English.
You can use it when discussing the value of an approximate solution or estimate compared to a precise one in various contexts, such as mathematics, science, or decision-making.
Example: "In many cases, especially in complex calculations, approximation is better than trying to achieve an exact answer that may not be feasible."
Alternatives: "an estimate is preferable" or "a rough calculation is more effective."
3. Successive approximation is better than delayed perfection.
For these non-regular structures, the effective medium approximation is better at reproducing their optical properties.
Mr. Rosenthal's description of sampling error as a "range of approximation" is better than what we generally hear, but not quite on the mark.
In both cases, this means that for sufficiently small ϵ, the homogeneous approximation is better than might be expected.
The phrase "what is the wrong word?" recurs in Ill Seen Ill Said, as if to say: "Of course language is insufficient, but approximation is better than nothing": Granite of no common variety assuredly.
Euclidean approximation is better when there are a large number of points.
Figures 2 and 3 clearly show that both the confidence ellipsoid and Markov bound approximations are better than the optimal training for standard channel estimation.
It's natural to think that computability, range of application, and other things being equal, true theories are better than false ones, good approximations are better than bad ones, and highly probable theoretical claims deserve to take precedence over less probable ones.
To measure the accuracy of this approximation, we usually say that approximation (2.3) is better as r 1 ( n ) − γ faster converges to zero.
end{aligned} (2.3) To measure the accuracy of this approximation, we usually say that an approximation (2.3) is better as (r_{1}(n) -gamma) faster converges to zero.
From a point view of the accuracy and the stability of the finite-difference approximation, it is better to keep the variation of spatial grid spacing smaller in the computational domain.
