Develops polynomial-time algorithms achieving competitive ratios of ~1/14.85 (general) and 1/6.86 (unit costs) for submodular welfare maximization with budgets under random-order item arrival.
Lipton, Evangelos Markakis, and Aranyak Mehta
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
Revenue maximization for pricing datasets to budget-constrained buyers is APX-hard, with a 2-approximation for online arrivals and a (1-1/e)^{-1}-approximation for offline.
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Submodular Welfare Maximization with Budget Constraints in the Random-Order Model
Develops polynomial-time algorithms achieving competitive ratios of ~1/14.85 (general) and 1/6.86 (unit costs) for submodular welfare maximization with budgets under random-order item arrival.
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Revenue-Optimal Pricing for Budget-Constrained Buyers in Data Markets
Revenue maximization for pricing datasets to budget-constrained buyers is APX-hard, with a 2-approximation for online arrivals and a (1-1/e)^{-1}-approximation for offline.