  We consider the problem of solving a large-scale Quadratically Constrained
Quadratic Program. Such problems occur naturally in many scientific and web
applications. Although there are efficient methods which tackle this problem,
they are mostly not scalable. In this paper, we develop a method that
transforms the quadratic constraint into a linear form by sampling a set of
low-discrepancy points. The transformed problem can then be solved by applying
any state-of-the-art large-scale quadratic programming solvers. We show the
convergence of our approximate solution to the true solution as well as some
finite sample error bounds. Experimental results are also shown to prove
scalability as well as improved quality of approximation in practice.
