Anirudh Subramanyam

Correction to: A Lagrangian dual method for two-stage robust optimization with binary uncertainties

  • Henri Lefebvre
  • Anirudh Subramanyam
    • Categories Robust Optimization Tags ,

    We provide a correction to the sufficient conditions under which closed-form expressions for the optimal Lagrange multiplier are provided in Subramanyam (2022). We first present a simple counterexample where the original conditions are insufficient, highlight where the original proof fails, and then provide modified conditions along with a correct proof of their validity. Finally, although … Read more

    Reduced Sample Complexity in Scenario-Based Control System Design via Constraint Scaling

  • Jaeseok Choi
  • Constantino Lagoa
  • Anirudh Subramanyam
    • Categories Control Applications, Convex Optimization, Stochastic Programming Tags , ,

    The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability distribution of uncertain parameters. However, the approach can demand large sample sizes, making it intractable for safety-critical applications that require very low levels of constraint violation. To address this challenge, we propose a … Read more