De-risking solutions to optimization problems

We develop a cutting-plane methodology that adjusts solutions to optimization problems so
as to reduce features that bring about exposure to risk, such as concentration of assets
or resources. The methodology is agnostic to the representation of risk. Our procedure aims
to reduce the appropriate risk metric without accruing a significant increase in nominal
cost, rapidly, or proves that such an adjustment is not possible. The underlying approach
borrows from techniques used in first-order methods for optimization.