We consider the problem of detecting a small subset of defective items from a
large set via non-adaptive "random pooling" group tests. We consider both the
case when the measurements are noiseless, and the case when the measurements
are noisy (the outcome of each group test may be independently faulty with
probability q). Order-optimal results for these scenarios are known in the
literature. We give information-theoretic lower bounds on the query complexity
of these problems, and provide corresponding computationally efficient
algorithms that match the lower bounds up to a constant factor. To the best of
our knowledge this work is the first to explicitly estimate such a constant
that characterizes the gap between the upper and lower bounds for these
problems
