"convergence error" is correct and usable in written English.
You can use it to describe when the rate at which new information is incorporated is not fast enough, resulting in a slow or incomplete resolution. For example, "The convergence error resulted in a much slower process than expected."
The proposed algorithm converges very fast, so that only in its first iteration, the convergence error is less than 0.2%.
It is then used to obtain an a priori estimate of the convergence error.
Statistical convergence error is defined and monitored by using the confidence intervals of the mean and variance.
This convergence error measure is supported on the shock trajectories and, as we demonstrate here, is usually "small".
This convergence error is easily seen in the numerical approximation of Riemann problems, in which the error appears and propagates at the formation of discontinuity waves.
This leads us to the conclusion that for systems having nonconservative products associated with linearly degenerate characteristic fields, the convergence error vanishes.
We then propose several strategies for the design of numerical schemes which reduce these convergence errors.
Both round-off and iterative convergence errors are discussed, and a posteriori methods for estimating the discretization error are examined.
No convergence errors were found in the 256 array system, but with new RRAM array systems reaching higher density, applicability of the model there remains a question.
Furthermore, unlike the method proposed by McShane et al. [ 5] to correct for measurement error in continuous exposures, the Bayesian method proposed in this paper is neither prone to convergence errors nor highly dependent on the settings of a particular individually matched case-control study.
In Section 2, we establish a semi-discretized super-convergence format about time for the Sobolev equation in the 2D unbounded domain and deduce the super-convergence error estimates of the semi-discretized solutions about time.
