A number of discrete and continuous optimization problems in machine learning are related to convex minimization problems under submodular constraints. In this paper, we deal with a submodular function with a directed graph structure, and we show that a wide range of convex optimization problems under submodular constraints can be solved much more efficiently than general submodular optimization methods by a reduction to a maximum flow problem. Furthermore, we give some applications, including sparse optimization methods, in which the proposed methods are effective. Additionally, we evaluate the performance of the proposed method through computational experiments.
|出版物ステータス||出版済み - 11 28 2013|
|イベント||29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, 米国|
継続期間: 7 11 2013 → 7 15 2013
|会議||29th Conference on Uncertainty in Artificial Intelligence, UAI 2013|
|期間||7/11/13 → 7/15/13|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence