The phrase "approximation with a large" is not correct and lacks clarity in written English.
It may be intended to describe a method or result involving a large approximation, but it needs additional context to be meaningful.
Example: "The approximation with a large sample size yielded more accurate results."
Alternatives: "estimate using a large" or "calculation with a large".
After convergence, the advantage of differential training is small since the performance is not so sensitive to the approximation error with a large enough number of random trajectories.
Krick et al. [27] give a constant approximation algorithm with a larger constant than the one given by Karger and Mikoff [22].
They also provide a quantitative estimate of the errors associated with the approximation of a large number of discrete supports by a continuous support condition or vice versa.
The solution methods are based on the Bender decomposition approach to divide the problem into sub-problems, which can be solved in parallel, and an approximation algorithm to solve problems with a large set of scenarios.
In fact, several literature datasets proved that the number St~0.16 describes with reasonable (at times, excellent) approximation a large variety of periodic fluid phenomena, many of them having no clear affinity.
In the literature, some approximation methods have been presented for CTMCs with a large number of states [51, 52].
Here, we developed a novel approximation that is extremely accurate for a population with a large effective size.
Response surface models are frequently utilized to construct surrogate approximations; however, they may be inefficient for systems having with a large number of variables.
Thus, the provided approximation is attractive for a performance measure of queuing systems with a large number of devices.
In addition, Slavakis and Giannakis further used accelerated stochastic approximation method with online and modular learning algorithms to deal with a large class of nonconvex data models [118].
