Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions - Bai, Wenruo; Noble, William S; Bilmes, Jeff A (Advances in neural information processing systems, 2018)
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 using gradient ascent. Our results show a guarantee of with a running time of ( ) plus time for pipage rounding [6] to recover a discrete solution, where is the rank of the matroid constraint. This bound is often better than the standard 1 - 1 guarantee of the continuous greedy algorithm, but runs much faster. Our bound also holds even for fully curved ( = 1) functions where the guarantee of 1 - degenerates to 1 - 1 where is the curvature of [37]. We perform computational experiments that support our theoretical results.