  The trust region method is an algorithm traditionally used in the field of
derivative free optimization. The method works by iteratively constructing
surrogate models (often linear or quadratic functions) to approximate the true
objective function inside some neighborhood of a current iterate. The
neighborhood is called "trust region in the sense that the model is trusted to
be good enough inside the neighborhood. Updated points are found by solving the
corresponding trust region subproblems. In this paper, we describe an
application of random projections to solving trust region subproblems
approximately.
