Structured convex optimization under submodular constraints

Kiyohito Nagano, Yoshinobu Kawahara

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages459-468
Number of pages10
Publication statusPublished - 2013
Externally publishedYes
Event29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States
Duration: Jul 11 2013Jul 15 2013

Conference

Conference29th Conference on Uncertainty in Artificial Intelligence, UAI 2013
CountryUnited States
CityBellevue, WA
Period7/11/137/15/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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