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Bounded Variation Separates Weak and Strong Average Lipschitz.

Ariel Elperin1, Aryeh Kontorovich1

  • 1Computer Science Department, Ben-Gurion University, Beer Sheva 84105, Israel.

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|September 27, 2025
PubMed
Summary
This summary is machine-generated.

We introduce new measures of average smoothness for functions. The weak average smoothness is less restrictive than bounded variation, while the strong version is more restrictive, offering new insights into function spaces.

Keywords:
Lipschitzsmooth averagevariation

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Area of Science:

  • Real Analysis
  • Functional Analysis
  • Metric Space Theory

Background:

  • The concept of function smoothness is central to analysis.
  • Lipschitz seminorms provide a quantitative measure of smoothness.
  • Bounded variation (BV) is a classical notion of smoothness for functions on the real line.

Purpose of the Study:

  • To introduce and analyze novel notions of average smoothness for real-valued functions.
  • To compare these new average smoothness notions with the established concept of bounded variation.
  • To investigate the combinatorial properties of the newly defined function classes.

Main Methods:

  • Definition of weak and strong average-Lipschitz seminorms on general metric spaces.
  • Specialization of these seminorms to the standard metric on the real line.
  • Comparison with the class of functions of bounded variation (BV).
  • Utilizing the fat-shattering dimension to analyze combinatorial properties.

Main Results:

  • The weak average-Lipschitz seminorm is strictly weaker than bounded variation.
  • The strong average-Lipschitz seminorm is strictly stronger than bounded variation.
  • The class of functions exhibiting weak average smoothness is combinatorially larger than BV functions, as quantified by the fat-shattering dimension.

Conclusions:

  • The newly introduced average smoothness notions offer a refined perspective on function regularity.
  • These notions provide a richer structure for function spaces compared to bounded variation.
  • The combinatorial analysis reveals significant differences in the size and complexity of function classes.