  It is known that, for systems of initial-value problems, algorithms using
adaptive information perform much better in the worst case setting than the
algorithms using nonadaptive information. In the latter case, lower and upper
complexity bounds significantly depend on the number of equations. However, in
contrast with adaptive information, existing lower and upper complexity bounds
for nonadaptive information are not asymptotically tight. In this paper, we
close the gap in the complexity exponents, showing asymptotically matching
bounds for nonadaptive standard information, as well as for a more general
class of nonadaptive linear information.
