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Discrete Newton's Algorithm for Parametric Submodular Function Minimization

机译:参数亚模函数最小化的离散牛顿算法

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We consider the line search problem in a submodular polyhedron P(f) ⊆ K~n: Given an arbitrary a ∈ R~n and x_0 ∈ P(f), compute max{δ : x_0 + δa ∈ P(f)}. The use of the discrete Newton's algorithm for this line search problem is very natural, but no strongly polynomial bound on its number of iterations was known (Iwata 2008). We solve this open problem by providing a quadratic bound of n~2 + O(n log~2 n) on its number of iterations. Our result considerably improves upon the only other known strongly polynomial time algorithm, which is based on Megiddo's parametric search framework and which requires O(n~8) submodular function minimizations (Nagano 2007). As a by-product of our study, we prove (tight) bounds on the length of chains of ring families and geometrically increasing sequences of sets, which might be of independent interest.
机译:我们考虑次模多面体P(f)⊆K〜n中的线搜索问题:给定任意a∈R〜n和x_0∈P(f),计算max {δ:x_0 +δa∈P(f)}。对于此线搜索问题,使用离散牛顿算法是很自然的事,但在迭代次数上没有强多项式的界线是已知的(Iwata 2008)。我们通过在其迭代次数上提供n〜2 + O(n log〜2 n)的二次边界来解决此开放问题。我们的结果大大改进了其他唯一已知的强多项式时间算法,该算法基于Megiddo的参数搜索框架,并且需要O(n〜8)次模函数最小化(Nagano 2007)。作为我们研究的副产品,我们证明了(紧)环家族的链长和几何上不断增加的序列集的界限,这可能是独立利益。

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