Efficient minimization of higher order submodular functions using monotonic Boolean functions

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• 作者：Srikumar Ramalingam^a ; ^{srikumar@cs.utah.edu} ; Chris Russell^b ; ^c ; L&rsquo ; ubor Ladický ; ^d ; Philip H.S. Torr^e
• 关键词：Submodular functions ; Quadratic pseudo-Boolean functions ; Monotonic Boolean functions ; Dedekind number ; Max-flow/mincut algorithm
• 刊名：Discrete Applied Mathematics
• 出版年：2017
• 出版时间：31 March 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：1-19
• 全文大小：1604 K
• 卷排序：220

文摘

Submodular function minimization is a key problem in a wide variety of applications in machine learning, economics, game theory, computer vision, and many others. The general solver has a complexity of O(n3log2n.E+n4logO(1)n)O(n3log2n.E+n4logO(1)n) where EE is the time required to evaluate the function and nn is the number of variables (Lee et al., 2015). On the other hand, many computer vision and machine learning problems are defined over special subclasses of submodular functions that can be written as the sum of many submodular cost functions defined over cliques containing only a few variables. In such functions, the pseudo-Boolean (or polynomial) representation (Boros and Hammer, 2002) of these subclasses are of degree (or order, or clique size) kk where k≪nk≪n. In this work, we develop efficient algorithms for the minimization of this useful subclass of submodular functions. To do this, we define novel mapping that transform submodular functions of order kk into quadratic ones. The underlying idea is to use auxiliary variables to model the higher order terms and the transformation is found using a carefully constructed linear program. In particular, we model the auxiliary variables as monotonic Boolean functions, allowing us to obtain a compact transformation using as few auxiliary variables as possible. The transformed quadratic function can be efficiently minimized using the standard max-flow algorithm with a time complexity of O((n+m)3)O((n+m)3) where mm is the total number of auxiliary variables involved in transforming all the higher order terms to quadratic ones. Specifically, we show that our approach for fourth order function requires only 22 auxiliary variables in contrast to 3030 or more variables used in existing approaches. In the general case, we give an upper bound for the number or auxiliary variables required to transform a function of order kk using Dedekind number, which is substantially lower than the existing bound of 22k22k.