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Oct, 2010
可分解次模函数的高效最小化
Efficient Minimization of Decomposable Submodular Functions
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Peter Stobbe, Andreas Krause
TL;DR
本文研究了一类新的子模函数优化问题，提出了一种基于平滑凸优化的算法SLG，可用于解决具有数万个变量的分解子模函数问题，而且在一些合成基准测试和联合分类和分割任务中优于现有的子模函数优化算法。
Abstract
Many combinatorial problems arising in
machine learning
can be reduced to the problem of minimizing a submodular function.
submodular functions
are a natural discrete analog of convex functions, and can be minimi
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