Results 11 to 20 of about 5,397,625 (216)
Sparsification of Decomposable Submodular Functions
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the original function is so large ...
Akbar Rafiey, Yuichi Yoshida
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Submodular function minimization
Mathematical Programming, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
S. Thomas McCormick
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Parallel Submodular Function Minimization [PDF]
Advances in Neural Information Processing Systems 36, 2023 We consider the parallel complexity of submodular function minimization (SFM). We provide a pair of methods which obtain two new query versus depth trade-offs a submodular function defined on subsets of $n$ elements that has integer values between $-M$ and $M$.
Deeparnab Chakrabarty +3 more
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Maximizing Symmetric Submodular Functions [PDF]
ACM Transactions on Algorithms, 2015 Symmetric submodular functions are an important family of submodular functions capturing many interesting cases, including cut functions of graphs and hypergraphs. Maximization of such functions subject to various constraints receives little attention by current research, unlike similar minimization problems that have been widely studied. In this work,
Moran Feldman
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Submodular function minimization and polarity [PDF]
Mathematical Programming, 2021 Using polarity, we give an outer polyhedral approximation for the epigraph of set functions. For a submodular function, we prove that the corresponding polar relaxation is exact; hence, it is equivalent to the Lovász extension. The polar approach provides an alternative proof for the convex hull description of the epigraph of a submodular function ...
Alper Atamtürk, Vishnu Narayanan
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Learning submodular functions [PDF]
Proceedings of the forty-third annual ACM symposium on Theory of computing, 2011 There has been much interest in the machine learning and algorithmic game theory communities on understanding and using submodular functions. Despite this substantial interest, little is known about their learnability from data. Motivated by applications, such as pricing goods in economics, this paper considers PAC-style learning of submodular ...
Balcan, Maria-Florina +1 more
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Ranking with submodular functions on a budget. [PDF]
Data Min Knowl Discov, 2022 AbstractSubmodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items.
Zhang G, Tatti N, Gionis A.
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Distributed Maximization of Submodular and Approximately Submodular Functions [PDF]
2020 59th IEEE Conference on Decision and Control (CDC), 2020 We study the problem of maximizing a submodular function, subject to a cardinality constraint, with a set of agents communicating over a connected graph. We propose a distributed greedy algorithm that allows all the agents to converge to a near-optimal solution to the global maximization problem using only local information and communication with ...
Lintao Ye, Shreyas Sundaram
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Constrained Path Search with Submodular Function Maximization [PDF]
, 2022 In this paper, we study the problem of constrained path search with submodular function maximization (CPS-SM). We aim to find the path with the best submodular function score under a given constraint (e.g., a length limit), where the submodular function ...
Fang, Yixiang +6 more
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Monotone Submodular Maximization over a Matroid via Non-Oblivious Local Search [PDF]
, 2013 We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pál and Vondrák, 2008), our algorithm is extremely simple ...
Filmus, Yuval +3 more
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