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Found 4 papers, 2 papers with code
Robust Losses for Decision-Focused Learning
Despite the challenge of this loss function being possibly non-convex and in general non-differentiable, effective gradient-based learning approaches have been proposed to minimize the expected loss, using the empirical loss as a surrogate.
Machine Learning for K-adaptability in Two-stage Robust Optimization
One of the solution approaches to this class of problems is K-adaptability.
First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation
Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty.
Optimization and Control
Global optimality in model predictive control via hidden invariant convexity
Non-convex optimal control problems occurring in, e. g., water or power systems, typically involve a large number of variables related through nonlinear equality constraints.
Optimization and Control Systems and Control Systems and Control
