Structured convex optimization under submodular constraints

Kiyohito Nagano, Yoshinobu Kawahara

研究成果: 会議への寄与タイプ論文

7 引用 (Scopus)

抜粋

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.

元の言語英語
ページ459-468
ページ数10
出版物ステータス出版済み - 11 28 2013
外部発表Yes
イベント29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, 米国
継続期間: 7 11 20137 15 2013

会議

会議29th Conference on Uncertainty in Artificial Intelligence, UAI 2013
米国
Bellevue, WA
期間7/11/137/15/13

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

これを引用

Nagano, K., & Kawahara, Y. (2013). Structured convex optimization under submodular constraints. 459-468. 論文発表場所 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, 米国.