Schur-convex function

A function f is 'Schur-concave' if its negative, −f, is Schur-convex.

If f is symmetric and all first partial derivatives exist, then f is Schur-convex if and only if

${\displaystyle (x_{i}-x_{j})\left({\frac {\partial f}{\partial x_{i}}}-{\frac {\partial f}{\partial x_{j}}}\right)\geq 0}$ for all ${\displaystyle x\in \mathbb {R} ^{d}}$

holds for all 1 ≤ i ≠ j ≤ d.[2]

• ${\displaystyle f(x)=\min(x)}$ is Schur-concave while ${\displaystyle f(x)=\max(x)}$ is Schur-convex. This can be seen directly from the definition.
• The Shannon entropy function ${\displaystyle \sum _{i=1}^{d}{P_{i}\cdot \log _{2}{\frac {1}{P_{i}}}}}$ is Schur-concave.
• The Rényi entropy function is also Schur-concave.
• ${\displaystyle \sum _{i=1}^{d}{x_{i}^{k}},k\geq 1}$ is Schur-convex.
• The function ${\displaystyle f(x)=\prod _{i=1}^{d}x_{i}}$ is Schur-concave, when we assume all ${\displaystyle x_{i}>0}$. In the same way, all the elementary symmetric functions are Schur-concave, when ${\displaystyle x_{i}>0}$.
• A natural interpretation of majorization is that if ${\displaystyle x\succ y}$ then ${\displaystyle x}$ is more spread out than ${\displaystyle y}$. 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 ${\displaystyle g}$ is a convex function defined on a real interval, then ${\displaystyle \sum _{i=1}^{n}g(x_{i})}$ is Schur-convex.
• A probability example: If ${\displaystyle X_{1},\dots ,X_{n}}$ are exchangeable random variables, then the function ${\displaystyle {\text{E}}\prod _{j=1}^{n}X_{j}^{a_{j}}}$ is Schur-convex as a function of ${\displaystyle a=(a_{1},\dots ,a_{n})}$, assuming that the expectations exist.
• The Gini coefficient is strictly Schur convex.

• Quasiconvex function