  In the context of structured nonconvex optimization, we estimate the increase
in minimum value for a decision that is robust to parameter perturbations as
compared to the value of a nominal problem. The estimates rely on detailed
expressions for subgradients and local Lipschitz moduli of min-value functions
in nonconvex robust optimization and require only the solution of the nominal
problem. The theoretical results are illustrated by examples from military
operations research involving mixed-integer optimization models. Across 54
cases examined, the median error in estimating the increase in minimum value is
12%. Therefore, the derived expressions for subgradients and local Lipschitz
moduli may accurately inform analysts about the possibility of obtaining
cost-effective, parameter-robust decisions in nonconvex optimization.
