On the compounding of higher order monotonic pseudo-Boolean functions
@article{Ressel2021OnTC, title={On the compounding of higher order monotonic pseudo-Boolean functions}, author={Paul Ressel}, journal={Positivity}, year={2021}, volume={27} }
Compounding submodular monotone (i.e. 2-alternating) set functions on a finite set preserves this property, as shown in 2010. A natural generalization to k-alternating functions was presented in 2018, however hardly readable because of page long formulas. We give an easier proof of a more general result, exploiting known properties of higher order monotonic functions.
7 References
Higher order monotonicity and submodularity of influence in social networks: from local to global
- MathematicsInf. Comput.
- 2022
Monotonicity properties of multivariate distribution and survival functions - With an application to Lévy-frailty copulas
- MathematicsJ. Multivar. Anal.
- 2011
Copulas, stable tail dependence functions, and multivariate monotonicity
- Computer ScienceDependence Modeling
- 2019
It will turn out that for nested Archimedean copulas and stable tail dependence functions the basic degree of monotonicity is the only one possible, apart from the (trivial) independence functions.
Higher order monotonic functions of several variables
- Mathematics
- 2014
Multivariate functions with a specific degree of higher order monotonicity in each variable are introduced. When normalized, they turn out to form a simplex whose extreme points are precisely the…
Submodularity of Influence in Social Networks: From Local to Global
- Computer ScienceSIAM J. Comput.
- 2010
The results demonstrate that “local” submodularity is preserved “globally” under this diffusion process, which is of natural computational interest, as many optimization problems have good approximation algorithms for submodular functions.
Maximizing the spread of influence through a social network
- Computer ScienceKDD '03
- 2003
An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.