  We propose a Jacobi-style distributed algorithm to solve convex,
quadratically constrained quadratic programs (QCQPs), which arise from a broad
range of applications. While small to medium-sized convex QCQPs can be solved
efficiently by interior-point algorithms, large-scale problems pose significant
challenges to traditional algorithms that are mainly designed to be implemented
on a single computing unit. The exploding volume of data (and hence, the
problem size), however, may overwhelm any such units. In this paper, we propose
a distributed algorithm for general, non-separable, large-scale convex QCQPs,
using a novel idea of predictor-corrector primal-dual update with an adaptive
step size. The algorithm enables distributed storage of data as well as
parallel distributed computing. We establish the conditions for the proposed
algorithm to converge to a global optimum, and implement our algorithm on a
computer cluster with multiple nodes using Message Passing Interface (MPI). The
numerical experiments are conducted on data sets of various scales from
different applications, and the results show that our algorithm exhibits
favorable scalability for solving large-scale problems.
