  We consider sensitivity analysis for Mixed Binary Quadratic Programs (MBQPs)
with respect to changing right-hand-sides (rhs). We show that even if the
optimal solution of a given MBQP is known, it is NP-hard to approximate the
change in objective function value with respect to changes in rhs. Next, we
study algorithmic approaches to obtaining dual bounds for MBQP with changing
rhs. We leverage Burer's completely-positive (CPP) reformulation of MBQPs. Its
dual is an instance of co-positive programming (COP), and can be used to obtain
sensitivity bounds. We prove that strong duality between the CPP and COP
problems holds if the feasible region is bounded or if the objective function
is convex, while the duality gap can be strictly positive if neither condition
is met. We also show that the COP dual has multiple optimal solutions, and the
choice of the dual solution affects the quality of the bounds with rhs changes.
We finally provide a method for finding good nearly optimal dual solutions, and
we present preliminary computational results on sensitivity analysis for MBQPs.
