Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions.
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ABSTRACT:
SUBMITTER: Bai W
PROVIDER: S-EPMC6351064 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
Publications
Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions.
Bai Wenruo W Noble William S WS Bilmes Jeff A JA
Advances in neural information processing systems 20181201
We study the problem of maximizing deep submodular functions (DSFs) [13, 3] subject to a matroid constraint. DSFs are an expressive class of submodular functions that include, as strict subfamilies, the facility location, weighted coverage, and sums of concave composed with modular functions. We use a strategy similar to the continuous greedy approach [6], but we show that the multilinear extension of any DSF has a natural and computationally attainable concave relaxation that we can optimize us ...[more]
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