ObRO is a min-max robust optimization formulation under objective functional uncertainty with an alternating algorithm proven to converge to a semi-global saddle point via operator theory, a numerically consistent piecewise linear approximation, and an application to battery charging scheduling.
Distributionally robust optimization
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A new equilibrium concept uses optimal-transport ambiguity sets to make agents robust to uncertain aggregate behavior in convex aggregative games, yielding better decisions and sometimes lower costs in EV charging applications.
Sinkhorn divergence defines ambiguity sets that make distributionally robust linear quadratic control over linear policies solvable via convex programming even with safety constraints.
citing papers explorer
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Robust Optimization Under Objective Functional Uncertainty
ObRO is a min-max robust optimization formulation under objective functional uncertainty with an alternating algorithm proven to converge to a semi-global saddle point via operator theory, a numerically consistent piecewise linear approximation, and an application to battery charging scheduling.
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Strategically Robust Aggregative Games
A new equilibrium concept uses optimal-transport ambiguity sets to make agents robust to uncertain aggregate behavior in convex aggregative games, yielding better decisions and sometimes lower costs in EV charging applications.
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Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
Sinkhorn divergence defines ambiguity sets that make distributionally robust linear quadratic control over linear policies solvable via convex programming even with safety constraints.