In mathematics, the Heinz mean (named after E. Heinz) of two non-negative real numbers A and B, was defined by Bhatia as:
H x ( A , B ) = A x B 1 − x + A 1 − x B x 2 , {\displaystyle \operatorname {H} _{x}(A,B)={\frac {A^{x}B^{1-x}+A^{1-x}B^{x}}{2}},}
with 0 ≤ x ≤ 1/2. For different values of x, this Heinz mean interpolates between the arithmetic (x = 0) and geometric (x = 1/2) means such that for 0 < x < 1/2:
A B = H 1 2 ( A , B ) < H x ( A , B ) < H 0 ( A , B ) = A + B 2 . {\displaystyle {\sqrt {AB}}=\operatorname {H} _{\frac {1}{2}}(A,B)<\operatorname {H} _{x}(A,B)<\operatorname {H} _{0}(A,B)={\frac {A+B}{2}}.}
The Heinz means appear naturally when symmetrizing
α {\textstyle \alpha } -divergences. It may also be defined in the same way for positive semidefinite matrices, and satisfies a similar interpolation formula.
See also Mean Muirhead's inequality Inequality of arithmetic and geometric means
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