The relation between iteration step and iteration error is shown in Table 1 and Figure 1.
This paper presents some generalizations of results concerning the stability of iterations in the sense that the iteration scheme subject to error sequences converges asymptotically to its nominal fixed point provided that the iteration error converges asymptotically to zero.
These conditions are related to the accuracy of the restriction operators, the choice of the boundary conditions, the distortion of the grids and the magnitude of the iteration error.
Otherwise update the bid volume of ADNs in (24) and go back to Step 2. left| {P_{ADN}^{l,t,w} - P_{ADN}^{l,t,w - 1} } right| le varepsilon (37 where (varepsilon) is the iteration error.
However, convergence to a point where the residual of the total error (the sum of the iteration error and the discretisation error) is of the order of the truncation error can be obtained in about seven defect correction cycles, according to estimates for the linear constant-coefficient equations.
According to Theorem 3.1, the discrete algebraic Riccati equation (1.3) has a unique positive definite solution (P_{0}), and (widetilde{P}_{1}leq P_{0}leqwidehat{P}_{1}). Let (e k)=|P^{ k+1)}-P^{ k)}-P^{ kthe iteration error at the kth iteration, k denote the iteration number, where we choose (varepsilon=10^{-8}) and (P^{(0)}=widetilde{P}_{1}) and (P^{(0)}=Q), respectively.
From Iteration Error series.
Input: Training tensor, the dimensionality of the output tensors.,,, maximum number of training iterations, error threshold.
In each of the following iterations, error residuals of normals for all lighting directions are computed and the normals are updated based only on those directions with small residuals.
In each of the following iterations, error residuals in normals for all lighting directions are computed and the normals are updated based only on those directions with small residuals.
