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.
Rudin,Functional Analysis
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Reformulating Lumer-Phillips conditions via PIE representation turns well-posedness verification for linear PDEs into convex optimization problems that bound exponential growth rates.
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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Verifying Well-Posedness of Linear PDEs using Convex Optimization
Reformulating Lumer-Phillips conditions via PIE representation turns well-posedness verification for linear PDEs into convex optimization problems that bound exponential growth rates.