Quaternion-Kähler symmetric space

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In convex analysis, Danskin's theorem is a theorem which provides information about the derivatives of a function of the form

f(x)=maxzZϕ(x,z).

The theorem has applications in optimization, where it sometimes is used to solve minimax problems.

Statement

The theorem applies to the following situation. Suppose ϕ(x,z) is a continuous function of two arguments,

ϕ:n×Z

where Zm is a compact set. Further assume that ϕ(x,z) is convex in x for every zZ.

Under these conditions, Danskin's theorem provides conclusions regarding the differentiability of the function

f(x)=maxzZϕ(x,z).

To state these results, we define the set of maximizing points Z0(x) as

Z0(x)={z:ϕ(x,z)=maxzZϕ(x,z)}.

Danskin's theorem then provides the following results.

Convexity
f(x) is convex.
Directional derivatives
The directional derivative of f(x) in the direction y, denoted Dyf(x), is given by
Dyf(x)=maxzZ0(x)ϕ(x,z;y),
where ϕ(x,z;y) is the directional derivative of the function ϕ(,z) at x in the direction y.
Derivative
f(x) is differentiable at x if Z0(x) consists of a single element z. In this case, the derivative of f(x) (or the gradient of f(x) if x is a vector) is given by
fx=ϕ(x,z)x.
Subdifferential
If ϕ(x,z) is differentiable with respect to x for all zZ, and if ϕ/x is continuous with respect to z for all x, then the subdifferential of f(x) is given by
f(x)=conv{ϕ(x,z)x:zZ0(x)}
where conv indicates the convex hull operation.

See also

References

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