Before revision:  The proposed method amounts to bootstrapping the value functions of a linear optimization problem, and subsumes subvector inference as a special case. In other words, this paper shows the conditions under which ``naively'' bootstrapping a linear program can be used to construct a confidence set with uniform correct coverage for a partially identified linear functional. Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient.
After revision:  The proposed method amounts to bootstrapping the value functions of four carefully constructed "perturbed" linear programming problems, and does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient.