Extending curvature to all submodular functions yields the first multiplicative greedy approximation guarantees that apply even when the function takes negative values.
Submodular maximization with car- dinality constraints
3 Pith papers cite this work. Polarity classification is still indexing.
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A new ordered local search algorithm achieves k/2 + o(k) approximation for monotone submodular maximization over k matroids and (ln 4 k)/3 + o(k) for weighted k-set packing.
Adaptive scaling algorithm achieves 1.373 competitive ratio for incremental submodular maximization, improving on greedy's 1.582 with a 1.25 lower bound.
citing papers explorer
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Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions
Extending curvature to all submodular functions yields the first multiplicative greedy approximation guarantees that apply even when the function takes negative values.
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Submodular Maximization over Many Matroids via Ordered Local Search
A new ordered local search algorithm achieves k/2 + o(k) approximation for monotone submodular maximization over k matroids and (ln 4 k)/3 + o(k) for weighted k-set packing.
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Incremental Submodular Maximization: Better Than Greedy
Adaptive scaling algorithm achieves 1.373 competitive ratio for incremental submodular maximization, improving on greedy's 1.582 with a 1.25 lower bound.