Results 61 to 70 of about 476 (159)

A Mazur-Orlicz type theorem for submodular set functions

Journal of Mathematical Analysis and Applications, 1986
Let \({\mathcal L}\) be a lattice of subsets of a given set \(\Omega\) with \(\emptyset \in {\mathcal L}\). A function \(\gamma:{\mathcal L}\to {\mathbb{R}}\cup \{- \infty \}\) is called a submodular (modular) set function if \(\gamma (\emptyset)=0\) and \[ \gamma (A\cup B)+\gamma (A\cap B)\leq (=)\gamma (A)+\gamma (B),\quad A\in {\mathcal L},\quad B ...
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Balanced sets in an independence structure induced by a submodular function

Journal of Mathematical Analysis and Applications, 1983
AbstractA submodular (and non-decreasing) function on a set induces an independence structure; the notion of a “balanced” set in this situation helps us determine whether a given independence structure is induced by any submodular function other than its own rank function, answering a question of U. S. R. Murty and I. Simon.
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On the Initial Set of Constraints for Graph-Based Submodular Function Maximization

Acta Cybernetica
A crucial problem in combinatorial optimization is the submodular function maximization (SFM), and in many cases it involves graphs on which the maximization is specified. The problem is well-studied and hence there are several proposed algorithms in the literature.
Eszter Csókás, Tamás Vinkó
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Hypergraphs with edge-dependent vertex weights: p-Laplacians and spectral clustering. [PDF]

Front Big Data, 2023
Zhu Y, Segarra S.
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On Equivalence of M$^\natural$-concavity of a Set Function and Submodularity of Its Conjugate

, 2017
A fundamental theorem in discrete convex analysis states that a set function is M$^\natural$-concave if and only if its conjugate function is submodular. This paper gives a new proof to this fact.
Kazuo Murota, Akiyoshi Shioura
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Maximizing Submodular Set Functions Subject to Multiple Linear Constraints [PDF]

Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2009
Ariel Kulik, Hadas Shachnai, Tami Tamir
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Ranking with submodular functions on a budget. [PDF]

Data Min Knowl Discov, 2022
Zhang G, Tatti N, Gionis A.
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Regularized impurity reduction: accurate decision trees with complexity guarantees. [PDF]

Data Min Knowl Discov, 2023
Zhang G, Gionis A.
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Discovering Key Sub-Trajectories to Explain Traffic Prediction. [PDF]

Sensors (Basel), 2022
Wang H, Fan Z, Chen J, Zhang L, Song X.
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Infodemic Source Detection with Information Flow: Foundations and Scalable Computation. [PDF]

Entropy (Basel)
Wang Z, Zhao C, Zhou Q, Tan CW, Chan C.
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