Danskin's theorem

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

The theorem has applications in optimization, where it sometimes is used to solve minimax problems. The original theorem given by J. M. Danskin in his 1967 monograph ^[1] provides a formula for the directional derivative of the maximum of a (not necessarily convex) directionally differentiable function.

An extension to more general conditions was proven 1971 by Dimitri Bertsekas.

Statement[]

The following version is proven in "Nonlinear programming" (1991).^[2] Suppose ${\displaystyle \phi (x,z)}$ is a continuous function of two arguments,

where ${\displaystyle Z\subset \mathbb {R} ^{m}}$ is a compact set. Further assume that ${\displaystyle \phi (x,z)}$ is convex in ${\displaystyle x}$ for every ${\displaystyle z\in Z.}$

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

To state these results, we define the set of maximizing points ${\displaystyle Z_{0}(x)}$ as

Danskin's theorem then provides the following results.

Convexity

${\displaystyle f(x)}$ is convex.

Directional derivatives

The directional derivative of ${\displaystyle f(x)}$ in the direction ${\displaystyle y}$, denoted ${\displaystyle D_{y}\ f(x),}$ is given by

$${\displaystyle D_{y}f(x)=\max _{z\in Z_{0}(x)}\phi '(x,z;y),}$$

where ${\displaystyle \phi '(x,z;y)}$ is the directional derivative of the function ${\displaystyle \phi (\cdot ,z)}$ at ${\displaystyle x}$ in the direction ${\displaystyle y.}$

Derivative

${\displaystyle f(x)}$ is differentiable at ${\displaystyle x}$ if ${\displaystyle Z_{0}(x)}$ consists of a single element ${\displaystyle {\overline {z}}}$. In this case, the derivative of ${\displaystyle f(x)}$ (or the gradient of ${\displaystyle f(x)}$ if ${\displaystyle x}$ is a vector) is given by

$${\displaystyle {\frac {\partial f}{\partial x}}={\frac {\partial \phi (x,{\overline {z}})}{\partial x}}.}$$

Subdifferential

If ${\displaystyle \phi (x,z)}$ is differentiable with respect to ${\displaystyle x}$ for all ${\displaystyle z\in Z,}$ and if ${\displaystyle \partial \phi /\partial x}$ is continuous with respect to ${\displaystyle z}$ for all ${\displaystyle x}$, then the subdifferential of ${\displaystyle f(x)}$ is given by

$${\displaystyle \partial f(x)=\mathrm {conv} \left\{{\frac {\partial \phi (x,z)}{\partial x}}:z\in Z_{0}(x)\right\}}$$

where ${\displaystyle \mathrm {conv} }$ indicates the convex hull operation.

Extension[]

The 1971 Ph.D. Thesis by Bertsekas (Proposition A.22) ^[3] proves a more general result, which does not require that ${\displaystyle \phi (\cdot ,z)}$ is differentiable. Instead it assumes that ${\displaystyle \phi (\cdot ,z)}$ is an extended real-valued closed proper convex function for each ${\displaystyle z}$ in the compact set ${\displaystyle Z,}$ that ${\displaystyle \operatorname {int} (\operatorname {dom} (f)),}$ the interior of the effective domain of ${\displaystyle f,}$ is nonempty, and that ${\displaystyle \phi }$ is continuous on the set ${\displaystyle \operatorname {int} (\operatorname {dom} (f))\times Z.}$ Then for all ${\displaystyle x}$ in ${\displaystyle \operatorname {int} (\operatorname {dom} (f)),}$ the subdifferential of ${\displaystyle f}$ at ${\displaystyle x}$ is given by

where ${\displaystyle \partial \phi (x,z)}$ is the subdifferential of ${\displaystyle \phi (\cdot ,z)}$ at ${\displaystyle x}$ for any ${\displaystyle z}$ in ${\displaystyle Z.}$

See also[]

• Maximum theorem
• Envelope theorem
• Hotelling's lemma

References[]