  Unbiased random vectors i.e. distributed uniformly in n-dimensional space,
are widely applied and the computational cost of generating a vector increases
only linearly with n. On the other hand, generating uniformly distributed
random vectors in its subspaces typically involves the inefficiency of
rejecting vectors falling outside, or re-weighting a non-uniformly distributed
set of samples. Both approaches become severely ineffective as n increases. We
present an efficient algorithm to generate uniformly distributed random
directions in n-dimensional cones, to aid searching and sampling tasks in high
dimensions.
