Fast Submodular Function Maximization
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Submodular functions have many real-world applications, such as document summarization, sensor placement, and image segmentation. For all these applications, the key building block is how to compute the maximum value of a submodular function efficiently. We consider both the online and offline versions of the problem: in each iteration, the data set changes incrementally or is not changed, and a user can issue a query to maximize the function on a given subset of the data. The user can be malicious, issuing queries based on previous query results to break the competitive ratio for the online algorithm. Today, the best-known algorithm for online submodular function maximization has a running time of O(n k d^2) where n is the total number of elements, d is the feature dimension and k is the number of elements to be selected. We propose a new method based on a novel search tree data structure. Our algorithm only takes O(nk + kd^2 + nd) time.
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