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Now we will state the theorem, which gives the improved bound.
We compare the improved bound to the enumeration results in the literature to find many cases for which our bounds are achieved.
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We prove an improved bound with the semiclassical constant and a gradient error term which is of lower order.
This Complimentary Pairs Stability Selection (CPSS) method has been mathematically proved to provide an improved bound for the estimation error control.
This improved bound is used to prove E s2 -optimality of thE s2 -optimalitybtained via algofitheic search in all cases with N="10, 12, 14, and 16 runs (except the N="14 run, m="16 factor case).
"They have placed an improved bound on a coefficient that is particularly difficult to measure," Kostelecky says.
We now show how bound (22) improves bound (3) presented in [14].
Using the D-discrete min-plus multiplication algorithm presented here, this immediately implies an algorithm having the improved time bound of O n 3 log 2 n.
The result improves the bound on the block RIC (delta_{2s|mathcal{I}}) in [1].
The result improves the bound on the block restricted isometry constant (delta_{2s|mathcal {I}}) of Lin and Li (Acta Math. Sin. Engl. Ser. 29 7):1401-1412013013).
Under a non line of sight (NLOS) condition, the roll off factor has negligible effect on error bounds, while under LOS condition, a higher roll-off factor helps to improve the bound for the timing error, possibly due to the sharper form of the first arrival in this case, related to the increase in the bandwidth.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com