In many real-life situations, we do not know the probability distribution of measurement errors but only upper bounds on these errors. In such situations, once we know the measurement results, we can only conclude that the actual (unknown) values of a quantity belongs to some interval. Based on this interval uncertainty, we want to find the range of possible values of a desired function of the uncertain quantities. In general, computing this range is an NP-hard problem, but in a linear approximation, valid for small uncertainties, there is a linear time algorithm for computing the range. In other situations, we know an ellipsoid that contains the actual values. In this case, we also have a linear time algorithm for computing the range of a linear function.
In some cases, however, we have a combination of interval and ellipsoid uncertainty. In this case, the actual values belong to the intersection of a box and an ellipsoid. In general, estimating the range over the intersection enables us to get a narrower range for the function. In this paper, we provide two algorithms for estimating the range of a linear function over an intersection in linear time: a simpler O(n log(n)) algorithm and a (somewhat more complex) linear time algorithm. Both algorithms can be extended to the l^p-case, when instead of an ellipsoid we have a set defined by a p-norm.