1 for other WR algorithms to compare the effectiveness of the newly found DNWR and NNWR algorithms.
1 states that NNWR converges in about half as many iterations as DNWR.
5, the error bound for NNWR may increase for the first few iterations for large T, before superlinear convergence kicks in.
1) posed on the time interval (0, T), one may consider applying NNWR to the whole interval (0, T), or to a sequence of smaller time windows (0, T/M), (T/M, 2T/M),.
Let B([alpha], k, t) be the contraction factor of NNWR, as defined in (4.
1 are based on technical estimates of kernels arising in the Laplace transform of the DNWR and NNWR algorithms.
2 in our analysis, we have to show positivity of the inverse transforms of kernels appearing in the DNWR and NNWR iteration.
We now prove the main convergence results for the DNWR and NNWR algorithms stated in Sections 2 and 3.
Green Tree Reservoirs at NNWR were moderate probability use areas for RBEB and SEM (Fig.
Protection of streamside management zones and cavity trees in public forests like NNWR allow forests to reach older age classes in riparian areas (Dickson and Sheffield, 2001).
Elevations within floodplain forests of NNWR where most RBEB and SEM were detected in roost trees ranged from 200-235 m whereas unoccupied roosts ranged from 200-533 m.
Green Tree Reservoirs at NNWR were moderately selected areas for both RBEB and SEM (Fig.