2019
DOI: 10.1080/03081079.2019.1586684
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Curve-based monotonicity: a generalization of directional monotonicity

Abstract: In this work we propose a generalization of the notion of directional monotonicity. Instead of considering increasingness or decreasingness along rays, we allow more general paths defined by curves in the n-dimensional space. These considerations lead us to the notion of α-monotonicity, where α is the corresponding curve. We study several theoretical properties of α-monotonicity and relate it to other notions of monotonicity, such as weak monotonicity and directional monotonicity.

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Cited by 7 publications
(3 citation statements)
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“…, 0)}, is called a pre-aggregation function [16]. There are also known some other concepts of monotonicity, for example, ordered directional monotonicity [17], cone monotonicity [18], or curve monotonicity [19]. However, except for the standard monotonicity of real n-ary functions, none of the mentioned types of monotonicity can be examined by means of the monotonicity in individual variables.…”
Section: Note That a Fusion Functionmentioning
confidence: 99%
“…, 0)}, is called a pre-aggregation function [16]. There are also known some other concepts of monotonicity, for example, ordered directional monotonicity [17], cone monotonicity [18], or curve monotonicity [19]. However, except for the standard monotonicity of real n-ary functions, none of the mentioned types of monotonicity can be examined by means of the monotonicity in individual variables.…”
Section: Note That a Fusion Functionmentioning
confidence: 99%
“…If a function f is both α-increasing and α-decreasing for a given curve α : [0, 1] → R n , then f is said to be α-constant. For curves defined on an open interval, see [9].…”
Section: Curve-based Monotonicitymentioning
confidence: 99%
“…In this work, we discuss a generalization of directional monotonicity: curvebased monotonicity [9]. Rather than directions given by vectors, curve-based monotonicity studies the monotonicity of functions along general curves α : [0, 1] → R n .…”
Section: Introductionmentioning
confidence: 99%