Perturbation function

In mathematical optimization, the perturbation function is any function which relates to primal and dual problems. The name comes from the fact that any such function defines a perturbation of the initial problem. In many cases this takes the form of shifting the constraints.[1] In some texts the value function is called the perturbation function, and the perturbation function is called the bifunction.[2]

## Contents

* 1 Definition
* 2 Use in duality
* 3 Examples
* 3.1 Lagrangian
* 3.2 Fenchel duality
* 4 References

## Definition

Given two dual pairs of separated locally convex spaces [math]\displaystyle{ \left(X,X^*\right) }[/math] and [math]\displaystyle{ \left(Y,Y^*\right) }[/math]. Then given the function [math]\displaystyle{ f: X \to \mathbb{R} \cup \\{+\infty\\} }[/math], we can define the primal problem by

[math]\displaystyle{ \inf_{x \in X} f(x). \, }[/math]

If there are constraint conditions, these can be built into the function [math]\displaystyle{ f }[/math] by letting [math]\displaystyle{ f \leftarrow f + I_\mathrm{constraints} }[/math] where [math]\displaystyle{ I }[/math] is the characteristic function. Then [math]\displaystyle{ F: X \times Y \to \mathbb{R} \cup \\{+\infty\\} }[/math] is a perturbation function if and only if [math]\displaystyle{ F(x,0) = f(x) }[/math].[1][3]

## Use in duality

The duality gap is the difference of the right and left hand side of the inequality

[math]\displaystyle{ \sup_{y^* \in Y^*} -F^*(0,y^*) \le \inf_{x \in X} F(x,0), }[/math]

where [math]\displaystyle{ F^* }[/math] is the convex conjugate in both variables.[3][4]

For any choice of perturbation function F weak duality holds. There are a number of conditions which if satisfied imply strong duality.[3] For instance, if F is proper, jointly convex, lower semi-continuous with [math]\displaystyle{ 0 \in \operatorname{core}({\Pr}_Y(\operatorname{dom}F)) }[/math] (where [math]\displaystyle{ \operatorname{core} }[/math] is the algebraic interior and [math]\displaystyle{ {\Pr}_Y }[/math] is the projection onto Y defined by [math]\displaystyle{ {\Pr}_Y(x,y) = y }[/math]) and X, Y are Fréchet spaces then strong duality holds.[1]

## Examples

### Lagrangian

Let [math]\displaystyle{ (X,X^*) }[/math] and [math]\displaystyle{ (Y,Y^*) }[/math] be dual pairs. Given a primal problem (minimize f(x)) and a related perturbation function (F(x,y)) then the Lagrangian [math]\displaystyle{ L: X \times Y^* \to \mathbb{R} \cup \\{+\infty\\} }[/math] is the negative conjugate of F with respect to y (i.e. the concave conjugate). That is the Lagrangian is defined by

[math]\displaystyle{ L(x,y^*) = \inf_{y \in Y} \left\\{F(x,y) - y^*(y)\right\\}. }[/math]

In particular the weak duality minmax equation can be shown to be

[math]\displaystyle{ \sup_{y^* \in Y^*} -F^*(0,y^*) = \sup_{y^* \in Y^*} \inf_{x \in X} L(x,y^*) \leq \inf_{x \in X} \sup_{y^* \in Y^*} L(x,y^*) = \inf_{x \in X} F(x,0). }[/math]

If the primal problem is given by

[math]\displaystyle{ \inf_{x: g(x) \leq 0} f(x) = \inf_{x \in X} \tilde{f}(x) }[/math]

where [math]\displaystyle{ \tilde{f}(x) = f(x) + I_{\mathbb{R}^d_+}(-g(x)) }[/math]. Then if the perturbation is given by

[math]\displaystyle{ \inf_{x: g(x) \leq y} f(x) }[/math]

then the perturbation function is

[math]\displaystyle{ F(x,y) = f(x) + I_{\mathbb{R}^d_+}(y - g(x)). }[/math]

Thus the connection to Lagrangian duality can be seen, as L can be trivially seen to be

[math]\displaystyle{ L(x,y^*) = \begin{cases} f(x) - y^*(g(x)) & \text{if } y^* \in \mathbb{R}^d_-, \\\ -\infty & \text{else}. \end{cases} }[/math]

### Fenchel duality

Let [math]\displaystyle{ (X,X^*) }[/math] and [math]\displaystyle{ (Y,Y^*) }[/math] be dual pairs. Assume there exists a linear map [math]\displaystyle{ T: X \to Y }[/math] with adjoint operator [math]\displaystyle{ T^*: Y^* \to X^* }[/math]. Assume the primal objective function [math]\displaystyle{ f(x) }[/math] (including the constraints by way of the indicator function) can be written as [math]\displaystyle{ f(x) = J(x,Tx) }[/math] such that [math]\displaystyle{ J: X \times Y \to \mathbb{R} \cup \\{+\infty\\} }[/math]. Then the perturbation function is given by

[math]\displaystyle{ F(x,y) = J(x,Tx - y). }[/math]

In particular if the primal objective is [math]\displaystyle{ f(x) + g(Tx) }[/math] then the perturbation function is given by [math]\displaystyle{ F(x,y) = f(x) + g(Tx - y) }[/math], which is the traditional definition of Fenchel duality.[5]

## References

1. ↑ 1.0 1.1 1.2 Radu Ioan Boţ; Gert Wanka; Sorin-Mihai Grad (2009). Duality in Vector Optimization. Springer. ISBN 978-3-642-02885-4.
2. ↑ J. P. Ponstein (2004). Approaches to the Theory of Optimization. Cambridge University Press. ISBN 978-0-521-60491-8.
3. ↑ 3.0 3.1 3.2 Zălinescu, C. (2002). Convex analysis in general vector spaces. River Edge, NJ: World Scientific Publishing Co., Inc. pp. 106–113. ISBN 981-238-067-1.
4. ↑ Ernö Robert Csetnek (2010). Overcoming the failure of the classical generalized interior-point regularity conditions in convex optimization. Applications of the duality theory to enlargements of maximal monotone operators. Logos Verlag Berlin GmbH. ISBN 978-3-8325-2503-3.
5. ↑ Radu Ioan Boţ (2010). Conjugate Duality in Convex Optimization. Springer. p. 68. ISBN 978-3-642-04899-9.

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