A Physical Application of a Rearrangement Inequality

1970 ◽  
Vol 77 (1) ◽  
pp. 68 ◽  
Author(s):  
M. S. Klamkin
2001 ◽  
Vol 8 (4) ◽  
pp. 727-732
Author(s):  
L. Ephremidze

Abstract The equivalence of the decreasing rearrangement of the ergodic maximal function and the maximal function of the decreasing rearrangement is proved. Exact constants are obtained in the corresponding inequalities.


Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 959 ◽  
Author(s):  
Mateu Sbert ◽  
Min Chen ◽  
Jordi Poch ◽  
Anton Bardera

Cross entropy and Kullback–Leibler (K-L) divergence are fundamental quantities of information theory, and they are widely used in many fields. Since cross entropy is the negated logarithm of likelihood, minimizing cross entropy is equivalent to maximizing likelihood, and thus, cross entropy is applied for optimization in machine learning. K-L divergence also stands independently as a commonly used metric for measuring the difference between two distributions. In this paper, we introduce new inequalities regarding cross entropy and K-L divergence by using the fact that cross entropy is the negated logarithm of the weighted geometric mean. We first apply the well-known rearrangement inequality, followed by a recent theorem on weighted Kolmogorov means, and, finally, we introduce a new theorem that directly applies to inequalities between K-L divergences. To illustrate our results, we show numerical examples of distributions.


1982 ◽  
Vol 60 (sup1) ◽  
pp. 44-63
Author(s):  
Leonard Goddard ◽  
Brenda Judge
Keyword(s):  

1976 ◽  
Vol 61 (1) ◽  
pp. 35-44 ◽  
Author(s):  
R. Friedberg ◽  
J. M. Luttinger

1975 ◽  
Vol 43 (3) ◽  
pp. 268-269
Author(s):  
Mary L. Boas
Keyword(s):  

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