Schur-convex function
In mathematics, a Schur-convex function, also known as S-convex, isotonic function and order-preserving function is a function that for all such that is majorized by , one has that . Named after Issai Schur, Schur-convex functions are used in the study of majorization. Every function that is convex and symmetric is also Schur-convex. The opposite implication is not true, but all Schur-convex functions are symmetric (under permutations of the arguments).[1]
Schur-concave function
A function f is 'Schur-concave' if its negative, -f, is Schur-convex.
Schur-Ostrowski criterion
If f is symmetric and all first partial derivatives exist, then f is Schur-convex if and only if
for all
holds for all 1≤i≠j≤d.[2]
Examples
- is Schur-concave while is Schur-convex. This can be seen directly from the definition.
- The Shannon entropy function is Schur-concave.
- The Rényi entropy function is also Schur-concave.
- is Schur-convex.
- The function is Schur-concave, when we assume all . In the same way, all the Elementary symmetric functions are Schur-concave, when .
- A natural interpretation of majorization is that if then is less spread out than . So it is natural to ask if statistical measures of variability are Schur-convex. The variance and standard deviation are Schur-convex functions, while the Median absolute deviation is not.
- If is a convex function defined on a real interval, then is Schur-convex.
- A probability example: If are exchangeable random variables, then the function is Schur-convex as a function of , assuming that the expectations exist.
- The Gini coefficient is strictly Schur convex.
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References
- Roberts, A. Wayne; Varberg, Dale E. (1973). Convex functions. New York: Academic Press. p. 258. ISBN 9780080873725.
- E. Peajcariaac, Josip; L. Tong, Y. Convex Functions, Partial Orderings, and Statistical Applications. Academic Press. p. 333. ISBN 9780080925226.
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