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One prevalent assumption is sparsity, which posits that the size of the set minimizing the submodular function is small. This holds particular relevance in diverse applications, including signal processing, feature selection, and compressed sensing.
Nov 7, 2023
Sep 28, 2023 · Abstract. In this paper we study the problem of minimizing a submodular function f : 2V → R that is guaranteed to have a k-sparse minimizer ...
Nov 1, 2023 · In this paper we study the problem of minimizing a submodular function $f : 2^V \rightarrow \R$ that is guaranteed to have a $k$-sparse ...
In this paper we study the problem of minimizing a submodular function $f: 2^{V} \rightarrow \mathbb{R}$ that is guaranteed to have a k-sparse minimizer.
May 8, 2024 · Abstract: We study the problem of minimizing a submodular function, defined on subsets of a ground set of n elements, that is known to have ...
Abstract—In this paper we study the problem of min- imizing a submodular function f : 2V. → R that is guaranteed to have a k-sparse minimizer.
This paper gives a deterministic algorithm that computes an additive $\epsilon$-approximate minimizer of such f in $\widetilde{O}(\operatorname{poly}(k) ...
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Is the cut function submodular?
Dec 26, 2023 · Minimizing the difference of two submodular (DS) functions is a problem that naturally occurs in various machine learning problems. Although it ...
Duration: 32:23
Posted: Dec 1, 2023
Posted: Dec 1, 2023
Missing: Sparse | Show results with:Sparse
This chapter describes the submodular function minimization problem (SFM); why it is important; techniques for solving it; algorithms by Cunningham, ...