Successive Quadratic Upper-Bounding for Discrete Mean-Risk Minimization and Network Interdiction

Understand

The advances in conic optimization have led to its increased utilization for modeling data uncertainty.

• In particular, conic mean-risk optimization gained prominence in probabilistic and robust optimization.
• Whereas the corresponding conic models are solved efficiently over convex sets, their discrete counterparts are intractable.
• In this paper, we give a highly effective successive quadratic upper-bounding procedure for discrete mean-risk minimization problems.