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Exploiting the Massey Gap.
Andrei Tănăsescu1, Pantelimon George Popescu1
1Department of Computer Science and Engineering, University Politehnica of Bucharest, Splaiul Independentei 313 (6), 060042 Bucharest, Romania.
Researchers refined the Massey inequality, which connects Shannon and guessing entropies, by introducing the Massey gap. Shrinking this gap improves prior results, offering new mathematical insights without added complexity.
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Area of Science:
- Information Theory
- Mathematical Inequalities
Background:
- The Massey inequality establishes a relationship between Shannon entropy and guessing entropy.
- Existing refinements may introduce additional parameters or lack closed-form solutions.
Purpose of the Study:
- To introduce a novel concept, the Massey gap, for refining the Massey inequality.
- To improve upon existing bounds relating Shannon and guessing entropies.
- To develop parameter-free refinements and numerical procedures.
Main Methods:
- Introduction of the Massey gap as a measure of difference.
- Derivation of closed-form strict refinements to the Massey inequality.
- Development of a numerical procedure for further enhancement.
Main Results:
- New, strict refinements to the Massey inequality are presented.
- The Massey gap provides a quantifiable measure for improving entropy bounds.
- Improvements are achieved without introducing new parameters.
Conclusions:
- The Massey gap offers a significant advancement in understanding the relationship between Shannon and guessing entropies.
- The proposed refinements provide tighter bounds and practical computational methods.

