  Envelopes were recently proposed as methods for reducing estimative variation
in multivariate linear regression. Estimation of an envelope usually involves
optimization over Grassmann manifolds. We propose a fast and widely applicable
one-dimensional (1D) algorithm for estimating an envelope in general. We reveal
an important structural property of envelopes that facilitates our algorithm,
and we prove both Fisher consistency and root-n-consistency of the algorithm.
