Results 61 to 70 of about 12,182 (176)
Deterministic Algorithms for Submodular Maximization Problems
, 2015 Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the area of submodular function maximization.
Buchbinder, Niv, Feldman, Moran
core +1 more source
Weakly Submodular Function Maximization Using Local Submodularity Ratio
, 2020 31st International Symposium on Algorithms and Computation (ISAAC 2020)
Santiago, Richard, Yoshida, Yuichi
openaire +5 more sources
Same/Other/All K‐Fold Cross‐Validation for Estimating Similarity of Patterns in Data Subsets
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.ABSTRACT In many real‐world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc.).
Toby Dylan Hocking +5 more
wiley +1 more source
On Constructing Finite, Finitely Subadditive Outer Measures, and Submodularity
International Journal of Mathematics and Mathematical Sciences, 2008 Given a nonempty abstract set 𝑋, and a covering class 𝒞, and a finite, finitely subadditive outer measure 𝜈, we construct an outer measure 𝜈 and investigate conditions for 𝜈 to be submodular. We then consider several other set functions associated with 𝜈
Charles Traina
doaj +1 more source
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints [PDF]
, 2013 We investigate two new optimization problems -- minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular function subject to a submodular upper bound constraint (submodular knapsack ...
Bilmes, Jeff, Iyer, Rishabh
core +1 more source
On Some Algorithmic and Structural Results on Flames
Journal of Graph Theory, Volume 110, Issue 4, Page 392-397, December 2025.ABSTRACT A directed graph F with a root node r is called a flame if for every vertex v other than r the local edge‐connectivity value λ F ( r , v ) from r to v is equal to ϱ F ( v ), the in‐degree of v. It is a classic, simple and beautiful result of Lovász [4] that every digraph D with a root node r has a spanning subgraph F that is a flame and the λ (
Dávid Szeszlér
wiley +1 more source
Streaming Algorithms for Submodular Function Maximization
, 2015 We consider the problem of maximizing a nonnegative submodular set function $f:2^{\mathcal{N}} \rightarrow \mathbb{R}^+$ subject to a $p$-matchoid constraint in the single-pass streaming setting.
A Badanidiyuru Varadaraja +16 more
core +1 more source
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
openaire +1 more source
Control Node Placement and Structural Controllability of Water Quality Dynamics in Drinking Networks
Water Resources Research, Volume 61, Issue 12, December 2025.Abstract Chlorine, the most widely used disinfectant, needs to be adequately distributed in water distribution networks (WDNs) to maintain consistent residual levels and ensure safe water. This is performed through control node injections at the treatment plant and via booster stations distributed across the WDNs.
Salma M. Elsherif, Ahmad F. Taha
wiley +1 more source
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications [PDF]
, 2012 We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step.
Bilmes, Jeff, Iyer, Rishabh
core +1 more source