PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 13, 2026Journal of Function Spaces0 citationsOpen Access

Milne‐Type Inequalities in the Context of Conformable Fractional Multiplicative Integrals

View Full Paper
İÇİrem ÇayHBHüseyin BudakİBİrem BAĞLAN

Key Points

  • This research aims to explore Milne inequalities within the realm of conformable fractional multiplicative integrals and develop new integral identities.
  • Established integral identity for deriving Milne-type inequalities.
  • Derived error estimates and integral bounds under mild conditions.
  • Presented numerical examples and discussed derivative constraints.
  • Developed new Milne-type inequalities for multiplicatively convex functions.
  • Provided error estimates that improve understanding within fractional calculus.
  • Demonstrated applications through numerical examples and graphical representations.

Abstract

This paper examines Milne inequalities in the setting of conformal fractional multiplicative integrals, which represent a modern extension of traditional fractional calculus. Drawing on advances in multiplicative analysis and non‐Newtonian calculus, we establish a new integral identity that forms the basis for deriving Milne‐type inequalities for multiplicatively convex functions with bounded ∗ derivatives. Using this framework, we derive new error estimates and integral bounds under relatively mild conditions. To demonstrate the applicability of our results, we present a numerical example with graphical representations. We also extend the analysis to functions subject to certain derivative constraints and discuss an application to specialized tools. The paper concludes by highlighting key contributions and suggesting possible directions for future research in multiplicative fractional calculus.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Çay et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf701faef96ed7f0589fahttps://doi.org/10.1155/jofs/3450748
Ask AI
Helpful
Bookmark
Share
View Full Paper