Results 51 to 60 of about 5,146,266 (215)
Connectivity of submodular functions
Discrete Mathematics, 1992 This paper relates the connectivity of submodular functions \(f\) to that of certain submodular functions which are derived from \(f\). Here the function \(f\) on \(S\) is submodular if \(f(A)+f(B)\geq f(A\cup B)+f(A\cap B)\) for all subsets \(A\) and \(B\) of \(S\).
James G. Oxley, Geoff Whittle
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Improved algorithms for submodular function minimization and submodular flow [PDF]
Proceedings of the thirty-second annual ACM symposium on Theory of computing, 2000 Very recently, two groups of researchers independently developed the first combinatorial, strongly polynomial-time algorithms for submodular function minimization (Iwata, Fleischer, Fujishige; and Schrijver). In this paper, we improve on these algorithms and show that the ideas generated in the design of these algorithms are helpful in other contexts ...
Lisa Fleischer, Satoru Iwata 0001
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Minimizing a submodular function arising from a concave function [PDF]
, 1999 We consider a class of submodular functions on distributive lattices that are defined in terms of concave functions and modular functions. The minimization of such a submodular function is made in time required for a max-flow computation on an associated
Satoru Fujishige +3 more
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New Query Lower Bounds for Submodular Function Minimization [PDF]
, 2020 We consider submodular function minimization in the oracle model: given black-box access to a submodular set function f:2^[n] → ℝ, find an element of arg min_S {f(S)} using as few queries to f(⋅) as possible. State-of-the-art algorithms succeed with Õ(n²)
Graur, Andrei +4 more
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Continuous submodular function maximization
CoRR, 2020 Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of functions - continuous submodularity - enables both exact minimization and approximate maximization in poly. time.
Bian, Yatao; id_orcid0000-0002-2368-4084 +2 more
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IEEE Access, 2018 Submodular optimization plays a significant role in combinatorial problems, since it captures the structure of the edge cuts in graphs, the coverage of sets, and so on. Many data mining and machine learning problems can be cast as submodular maximization
Qilian Yu, Li Xu, Shuguang Cui
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Mathematics, 2020 In this paper, we consider the single machine scheduling problem with release dates and nonmonotone submodular rejection penalty. We are given a single machine and multiple jobs with probably different release dates and processing times. For each job, it
Xiaofei Liu, Weidong Li
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Toward Optimal Placement of Spatial Sensors to Detect Poisson-Distributed Targets
IEEE Access, 2023 This paper addresses the challenges of optimally placing a finite number of sensors to detect Poisson-distributed targets in a bounded domain. We seek to rigorously account for uncertainty in the target arrival model throughout the problem.
Mingyu Kim +5 more
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CoRR, 2017 We start with an overview of a class of submodular functions called SCMMs (sums of concave composed with non-negative modular functions plus a final arbitrary modular). We then define a new class of submodular functions we call {\em deep submodular functions} or DSFs. We show that DSFs are a flexible parametric family of submodular functions that share
Jeffrey A. Bilmes, Wenruo Bai
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Sparse Submodular Function Minimization
2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS), 2023 Accepted to FOCS ...
Andrei Graur +2 more
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