Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs
Abstract
In this paper, we investigate a class of submodular problems which in general are very hard. These include minimizing a submodular cost function under combinatorial constraints, which include cuts, matchings, paths, etc., optimizing a submodular function under submodular cover and submodular knapsack constraints, and minimizing a ratio of submodular functions. All these problems appear in several real world problems but have hardness factors of $\Omega(\sqrt{n})$ for general submodular cost functions. We show how we can achieve constant approximation factors when we restrict the cost functions to low rank sums of concave over modular functions. A wide variety of machine learning applications are very naturally modeled via this subclass of submodular functions. Our work therefore provides a tighter connection between theory and practice by enabling theoretically satisfying guarantees for a rich class of expressible, natural, and useful submodular cost models. We empirically demonstrate the utility of our models on real world problems of cooperative image matching and sensor placement with cooperative costs.
BibTeX
@InProceedings{pmlr-v89-iyer19a,
title = {Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs},
author = {Iyer, Rishabh and Bilmes, Jeffrey},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {276--285},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/iyer19a/iyer19a.pdf},
url = {https://proceedings.mlr.press/v89/iyer19a.html},
abstract = {In this paper, we investigate a class of submodular problems which in general are very hard. These include minimizing a submodular cost function under combinatorial constraints, which include cuts, matchings, paths, etc., optimizing a submodular function under submodular cover and submodular knapsack constraints, and minimizing a ratio of submodular functions. All these problems appear in several real world problems but have hardness factors of $\Omega(\sqrt{n})$ for general submodular cost functions. We show how we can achieve constant approximation factors when we restrict the cost functions to low rank sums of concave over modular functions. A wide variety of machine learning applications are very naturally modeled via this subclass of submodular functions. Our work therefore provides a tighter connection between theory and practice by enabling theoretically satisfying guarantees for a rich class of expressible, natural, and useful submodular cost models. We empirically demonstrate the utility of our models on real world problems of cooperative image matching and sensor placement with cooperative costs.}
}