Before revision:  Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient. In addition to the new procedure , the paper also discusses connections between the literature on optimization and the literature on subvector inference in partially identified models .
After revision:  Unlike other proposed subvector inference procedures, our procedure does not require the researcher to grid over the parameter space. Our low-level conditions for uniform validity rely on a novel application of Sard's Theorem from differential topology, and may be of substantial separate interest. Our procedure is asymptotically conservative for the true partially identified parameter, but draws its appeal from the fact that it is valid under weak assumptions, it performs well in finite samples, and is computationally simple to implement .