Submodular Dominance and Applications

Authors Frederick Qiu, Sahil Singla



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Author Details

Frederick Qiu
  • Department of Computer Science, Princeton University, NJ, USA
Sahil Singla
  • School of Computer Science, Georgia Tech, Atlanta, GA, USA

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Frederick Qiu and Sahil Singla. Submodular Dominance and Applications. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 44:1-44:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2022.44

Abstract

In submodular optimization we often deal with the expected value of a submodular function f on a distribution 𝒟 over sets of elements. In this work we study such submodular expectations for negatively dependent distributions. We introduce a natural notion of negative dependence, which we call Weak Negative Regression (WNR), that generalizes both Negative Association and Negative Regression. We observe that WNR distributions satisfy Submodular Dominance, whereby the expected value of f under 𝒟 is at least the expected value of f under a product distribution with the same element-marginals.
Next, we give several applications of Submodular Dominance to submodular optimization. In particular, we improve the best known submodular prophet inequalities, we develop new rounding techniques for polytopes of set systems that admit negatively dependent distributions, and we prove existence of contention resolution schemes for WNR distributions.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • Mathematics of computing → Probabilistic algorithms
  • Theory of computation → Algorithmic game theory
Keywords
  • Submodular Optimization
  • Negative Dependence
  • Negative Association
  • Weak Negative Regression
  • Submodular Dominance
  • Submodular Prophet Inequality

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