  Quadratic programs arise in robotics, communications, smart grids, and many
other applications. As these problems grow in size, finding solutions becomes
much more computationally demanding, and new algorithms are needed to
efficiently solve them. Targeting large-scale problems, we develop a
multi-agent quadratic programming framework in which each agent updates only a
small number of the total decision variables in a problem. Agents communicate
their updated values to each other, though we do not impose any restrictions on
the timing with which they do so, nor on the delays in these transmissions.
Furthermore, we allow weak parametric coupling among agents, in the sense that
they are free to independently choose their stepsizes, subject to mild
restrictions. We show that these stepsize restrictions depend upon a problem's
condition number. We further provide the means for agents to independently
regularize the problem they solve, thereby improving condition numbers and, as
we will show, convergence properties, while preserving agents' independence in
selecting parameters. Simulation results are provided to demonstrate the
success of this framework on a practical quadratic program.
