De-risking solutions to optimization problems

Abstract

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.