Quaternion-Kähler symmetric space: Difference between revisions

From formulasearchengine
Jump to navigation Jump to search
en>Giftlite
 
en>Yobot
m WP:CHECKWIKI error fixes / special characters in pagetitle using AWB (9485)
Line 1: Line 1:
The writer's title is Christy. What me and my family adore is bungee leaping but I've been using on new issues lately. Ohio is where her house is. Distributing production has been his profession for some time.<br><br>My blog :: telephone psychic ([http://checkmates.co.za/index.php?do=/profile-56347/info/ Read Home Page])
In [[convex analysis]], '''Danskin's theorem''' is a [[theorem]] which provides information about the [[derivative]]s of a [[function (mathematics)|function]] of the form
 
:<math>f(x) = \max_{z \in Z} \phi(x,z).</math>
 
The theorem has applications in [[optimization (mathematics)|optimization]], where it sometimes is used to solve [[minimax]] problems.
 
== Statement ==
The theorem applies to the following situation. Suppose <math>\phi(x,z)</math> is a [[continuous function]] of two arguments,
:<math>\phi: {\mathbb R}^n \times Z \rightarrow {\mathbb R}</math>
where <math>Z \subset {\mathbb R}^m</math> is a [[compact set]]. Further assume that <math>\phi(x,z)</math> is [[convex function|convex]] in <math>x</math> for every <math>z \in Z</math>.
 
Under these conditions, Danskin's theorem provides conclusions regarding the [[differentiability]] of the function
:<math>f(x) = \max_{z \in Z} \phi(x,z).</math>
To state these results, we define the set of maximizing points <math>Z_0(x)</math> as
:<math>Z_0(x) = \left\{ \overline{z} : \phi(x,\overline{z}) = \max_{z \in Z} \phi(x,z)\right\}.</math>
 
Danskin's theorem then provides the following results.
 
;Convexity
: <math>f(x)</math> is [[convex function|convex]].
;Directional derivatives
: The [[directional derivative]] of <math>f(x)</math> in the direction <math>y</math>, denoted <math>D_y\ f(x)</math>, is given by
::<math>D_y f(x) = \max_{z \in Z_0(x)} \phi'(x,z;y),</math>
: where <math>\phi'(x,z;y)</math> is the directional derivative of the function <math>\phi(\cdot,z)</math> at <math>x</math> in the direction <math>y</math>.
;Derivative
: <math>f(x)</math> is [[differentiable]] at <math>x</math> if <math>Z_0(x)</math> consists of a single element <math>\overline{z}</math>. In this case, the [[derivative]] of <math>f(x)</math> (or the [[gradient]] of <math>f(x)</math> if <math>x</math> is a vector) is given by
:: <math>\frac{\partial f}{\partial x} = \frac{\partial \phi(x,\overline{z})}{\partial x}.</math>
;Subdifferential
:If <math>\phi(x,z)</math> is differentiable with respect to <math>x</math> for all <math>z \in Z</math>, and if <math>\partial \phi/\partial x</math> is continuous with respect to <math>z</math> for all <math>x</math>, then the [[subdifferential]] of <math>f(x)</math> is given by
:: <math>\partial f(x) = \mathrm{conv} \left\{ \frac{\partial \phi(x,z)}{\partial x} : z \in Z_0(x) \right\}</math>
: where <math>\mathrm{conv}</math> indicates the [[convex hull]] operation.
 
== See also ==
* [[Maximum theorem]]
* [[Envelope theorem]]
* [[Hotellings Lemma]]
 
== References ==
* {{cite book
| last = Bertsekas
| first = Dimitri P.
| title = Nonlinear Programming
| publisher = Athena Scientific
| date = 1999
| pages = 717
| location = Belmont, MA
| id = ISBN 1-886529-00-0 }}
 
[[Category:Convex analysis]]
[[Category:Mathematical optimization]]
[[Category:Theorems in analysis]]
[[Category:Convex optimization]]

Revision as of 18:27, 17 September 2013

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

  • 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534