First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation

Understand

Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty.

• Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimization problems.
• For that reason, first order approaches, based on online-convex-optimization, have been proposed (Ben-Tal et al.
• (2015), Kilinc-Karzan and Ho-Nguyen (2018)) as alternatives for the case of large-scale problems.