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3 articles
· 9 min read · Part 3 of 3
Stochastic Greedy: Scaling Submodular Maximization to Massive Datasets
Stochastic Greedy replaces greedy's full scan with random subsampling, reducing runtime from O(nk) to O(n log(1/ε)) while losing only an additive ε in the approximation guarantee. This post covers the algorithm, its proof, and practical guidance.
· 13 min read · Part 2 of 3
The Greedy Algorithm for Submodular Maximization
The greedy algorithm achieves a (1 - 1/e) approximation for monotone submodular maximization, provably the best any efficient algorithm can do. This post covers the algorithm, its proof, Lazy Greedy, and when greedy fails.
· 15 min read · Part 1 of 3
An Introduction to Submodularity
A practical introduction to submodular functions, the mathematical framework behind diminishing returns, covering set functions, marginal gains, and real-world applications from sensor placement to influence maximization.